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Original Articles
- Gastrointestinal cancer
- Clinical Significance of Combining Preoperative and Postoperative Albumin-Bilirubin Score in Colorectal Cancer
- Doyoun Kim, Jae-Hoon Lee, Eun-Suk Cho, Su-Jin Shin, Hye Sun Lee, Hwa-Hee Koh, Kang Young Lee, Jeonghyun Kang
- Cancer Res Treat. 2023;55(4):1261-1269. Published online April 17, 2023
- DOI: https://doi.org/10.4143/crt.2022.1444
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Abstract
PDF
Supplementary Material
PubReader
ePub - Purpose
Albumin-bilirubin (ALBI) score is a well-known prognostic factor for various diseases, including colorectal cancer (CRC). However, little is known about the significance of postoperative ALBI score changes in patients with CRC.
Materials and Methods
A total of 723 patients who underwent surgery were enrolled. Preoperative ALBI (ALBI-pre) and postoperative ALBI (ALBI-post) scores were divided into low and high score groups. ALBI-trend was defined as a combination of four groups comprising the low and high ALBI-pre and ALBI-post score groups. Kaplan-Meier survival curves were used to compare the overall survival (OS) between the different ALBI groups. The Cox proportional hazards model was used to examine the independent relevant factors of OS. Stratification performance was compared between the different ALBI groupings using Harrell’s concordance index (C-index).
Results
ALBI-pre, ALBI-post, and ALBI-trend score groups were significant prognostic factors of OS in the univariable analysis. However, multivariable analysis showed that ALBI-trend was an independent prognostic factor while ALBI-pre and ALBI-post were not. The C-index of ALBI-trend (0.622; 95% confidence interval [CI], 0.587 to 0.655) was higher than that of ALBI-pre (0.589; 95% CI, 0.557 to 0.621; bootstrap mean difference, 0.033; 95% CI, 0.013 to 0.057) and ALBI-post (0.575; 95% CI, 0.545 to 0.605; bootstrap mean difference, 0.047; 95% CI, 0.024 to 0.074).
Conclusion
Combining ALBI-pre and ALBI-post scores is an independent prognostic factor of OS and shows superior predictive power compared to ALBI-pre or ALBI-post alone in patients with CRC. -
Citations
Citations to this article as recorded by
- Prognostic value of functional MRI and liver function synergy in hepatocellular carcinoma patients receiving combined locoregional-systemic therapy: A multicenter scoring model
Langlang Tang, Kaiqian Que, Yanfen Lan, Yimin Li, Bin Wang, Zhuting Fang
European Journal of Radiology.2026; 196: 112705. CrossRef - Associations of bilirubin, albumin, and the ALBI index with gastric cancer risk: a smoking-stratified prospective cohort study in men
Jong Won Shin, Ji-Won Lee, Nguyen Thien Minh, Sun Ha Jee
BMC Gastroenterology.2026;[Epub] CrossRef - Comparing laboratory toxicity of selective intra-arterial radionuclide therapy for primary and metastatic liver tumors: resin versus glass microspheres
Başak Soydaş-Turan, M. Fani Bozkurt, Gonca Eldem, Bora Peynircioglu, Omer Ugur, Bilge Volkan-Salanci
Annals of Nuclear Medicine.2025; 39(4): 373. CrossRef - Improvement of Hypoalbuminemia and Hepatic Reserve after Stent Placement for Postsurgical Portal Vein Stenosis
Naoya Kinota, Daisuke Abo, Ryo Morita, Koji Yamasaki, Takaaki Fujii, Daisuke Kato, Tasuku Kimura, Yusuke Sakuhara, Kazufumi Okada, Isao Yokota, Tatsuya Orimo, Tatsuhiko Kakisaka, Toru Nakamura, Satoshi Hirano, Kazuyuki Minowa, Kohsuke Kudo
Journal of Vascular and Interventional Radiology.2025; 36(4): 616. CrossRef - Gastrointestinal tumors of the small bowel: prognostic roles of tumor stage and inflammatory markers
Mehmet Torun, Sevil Özkan, Deniz Kol Özbay, Erkan Özkan
Anatolian Current Medical Journal.2025; 7(2): 164. CrossRef - Predictive value of preoperative serum cytokeratin 19 fragment antigen 21-1(CYFRA 21-1) in surgical resection for colorectal cancer: A retrospective study
Shuyuan Gu, Shihui Chen, Yuan Chai, Chao Qu, Xuejun Sun, Junhui Yu
Current Problems in Surgery.2025; 69: 101791. CrossRef - Sex-Specific Associations of Total Bilirubin, ALBI, and PALBI with Lung Cancer Risk: Interactions with Smoking and Alcohol
Jong Won Shin, Nguyen Thien Minh, Sun Ha Jee
Healthcare.2025; 13(11): 1321. CrossRef - Bilirubin Metabolism and Thyroid Cancer: Insights from ALBI and PALBI Indices
Jong Won Shin, Jae Woong Sull, Nguyen Thien Minh, Sun Ha Jee
Biomolecules.2025; 15(7): 1042. CrossRef - Hemoglobin-Albumin-Lymphocyte-Platelet Score as an Integrative Biomarker for Prognosis and Sarcopenia in Colorectal Cancer
Hailun Xie, Lishuang Wei, Shuangyi Tang, Jialiang Gan
Journal of Inflammation Research.2025; Volume 18: 14709. CrossRef - The association between the albumin-bilirubin score and postoperative delirium and prognosis in cardiac surgery patients: a retrospective dual-center cohort study using MIMIC-IV and eICU databases
Na Zhu, Xiejia Weng, Xinxin Wang, Huiyang Cai
BMC Surgery.2025;[Epub] CrossRef - The prognostic value of preoperative serum liver function-related indicators in colorectal cancer
Lang Wang, Jinshan Liu, Can Tan, Yuhang Diao, Lijuan Wang, Youyun Shi, Xinpeng Shu, Yong Cheng
Medicine.2025; 104(51): e46047. CrossRef - Research Progress of Albumin-Based Composite Markers in Prognosis Evaluation of Patients with Colorectal Cancer
Xu Lei, Jie Yu
Journal of Biosciences and Medicines.2025; 13(09): 24. CrossRef - Assessment of the albumin-bilirubin score in breast cancer patients with liver metastasis after surgery
Li Chen, Chunlei Tan, Qingwen Li, Zhibo Ma, Meng Wu, Xiaosheng Tan, Tiangen Wu, Jinwen Liu, Jing Wang
Heliyon.2023; 9(11): e21772. CrossRef
- Prognostic value of functional MRI and liver function synergy in hepatocellular carcinoma patients receiving combined locoregional-systemic therapy: A multicenter scoring model
- 6,982 View
- 195 Download
- 11 Web of Science
- 13 Crossref
- Breast cancer
- Marked Reduction in the Risk of Dementia in Patients with Breast Cancer: A Nationwide Population-Based Cohort Study
- Jooyoung Oh, Hye Sun Lee, Soyoung Jeon, Jeong-Ho Seok, Tae-Kyung Yoo, Woo-Chan Park, Chang Ik Yoon
- Cancer Res Treat. 2023;55(2):551-561. Published online October 19, 2022
- DOI: https://doi.org/10.4143/crt.2022.272
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Abstract
PDFSupplementary MaterialPubReaderePub
- Purpose
An inverse relationship between cancer and neurodegenerative disease, which presents the possibility of a reduced risk of dementia in cancer patients, has been suggested previously. However, a nationwide longitudinal population-based study of specific types of cancer with due consideration of treatment effects has not been conducted.
Materials and Methods
This nationwide population-based cohort study used data obtained in a 12-year period (January 2007- December 2018) in the Korean National Health Insurance claims database. All female breast cancer patients (age ≥ 50 years) diagnosed between 2009 and 2010 were included after excluding those with physician visits for any cancer during a 2-year period (2007-2008). Patients with senile cataract constituted the control group. The main study outcome was the risk of developing dementia.
Results
From a total of 90,396 and 85,906 patients with breast cancer and cataract, respectively, patients without behavior codes were excluded. Data for 15,407 breast cancer patients and 7,020 controls were analyzed before matching. After matching for comorbidities and age, either group comprised 2,252 patients. The median follow-up time was 104.1±24.0 months after matching. After matching, breast cancer was a predictor of a lower risk of for dementia (hazard ratio, 0.091; 95% confidence interval, 0.075 to 0.111; p < 0.001). In breast cancer patients, receiving chemotherapy and endocrine therapy did not significantly affect the incidence of dementia.
Conclusion
Breast cancer was associated with a remarkably decreased risk of dementia. The findings strongly suggest an inverse relationship between cancer and neurodegeneration, regardless of the adverse effects of cancer treatment on cognitive function. -
Citations
Citations to this article as recorded by- Timing of risk factors, prodromal features, and comorbidities of dementia from a large health claims case–control study
Stefan Teipel, Manas Akmatov, Bernhard Michalowsky, Steffi Riedel-Heller, Jens Bohlken, Jakob Holstiege
Alzheimer's Research & Therapy.2025;[Epub] CrossRef - Bidirectional association between breast cancer and dementia: a systematic review and meta-analysis of observational studies
Fuxing Bao, Liang Yu, Xiaolei Zhang, Qier Mu
PeerJ.2025; 13: e18888. CrossRef - Changes in the Emergency Department Visits Among the Older Adults With Dementia Before, and After the Nationwide Social Distancing Measures: An Interrupted Time Series Analysis
Jeongmin Moon, Kyung‐Shin Lee, Ho Kyung Sung, Seonji Kim
International Journal of Geriatric Psychiatry.2025;[Epub] CrossRef - Association between Benzodiazepine and Dementia Risk in Treating Depression after Breast Cancer Diagnosis: A Nationwide Population-Based Cohort Study
Taehwan Kim, Hye Sun Lee, Soyoung Jeon, Dooreh Kim, Eunjoo Kim, Woo-Chan Park, Chang Ik Yoon, Jooyoung Oh
Yonsei Medical Journal.2025; 66(9): 564. CrossRef - Cardiovascular contributions to dementia: Examining sex differences and female‐specific factors
Madeline Wood Alexander, Jane Paterson, Zoe Arvanitakis, Sandra E. Black, Kaitlin B. Casaletto, Marie K. Christakis, Gillian Einstein, Liisa A. M. Galea, Leila Harwood, Amy Kirkham, Sara Mitchell, Judy Pa, Lucas Perri, Tallinn F. L. Splinter, Walter Sward
Alzheimer's & Dementia.2025;[Epub] CrossRef - Alzheimer Disease in Breast Cancer Survivors
Su-Min Jeong, Wonyoung Jung, Hyeonjin Cho, Hea Lim Choi, Keun Hye Jeon, Ki-Woong Nam, Yun-Gyoo Lee, Bongseong Kim, Kyungdo Han, Dong Wook Shin
JAMA Network Open.2025; 8(6): e2516468. CrossRef - The risk of dementia in breast cancer survivors: a meta-analysis of observational studies
Shijun Tan, Jiawei Yang, Guiping Guo, Shicui Hong, Li Guo, Honglin Situ
Annals of Medicine.2025;[Epub] CrossRef - Inverse association of cancer with diagnoses of dementia in a large health claims case-control study
Stefan Teipel, Manas Akmatov, Bernhard Michalowsky, Steffi Riedel-Heller, Christian Junghanss, Jakob Holstiege, Jens Bohlken
Journal of Alzheimer’s Disease.2025; 108(1): 98. CrossRef - Dementia and Cancer: Unravelling Methodological Biases in a Population-Based Cohort
Amber P. Selie, Kimberly D. van der Willik, M. Arfan Ikram, Jeremy A. Labrecque, Sanne B. Schagen
Neuroepidemiology.2025; : 1. CrossRef - Developing theragnostics for Alzheimer's disease: Insights from cancer treatment
Hyun-ju Lee, Hee-Jeong Choi, Yoo Joo Jeong, Yoon-Hee Na, Jin Tae Hong, Ji Min Han, Hyang-Sook Hoe, Key-Hwan Lim
International Journal of Biological Macromolecules.2024; 269: 131925. CrossRef
- Timing of risk factors, prodromal features, and comorbidities of dementia from a large health claims case–control study
- 8,787 View
- 190 Download
- 12 Web of Science
- 10 Crossref
- Gastrointestinal cancer
- Association of Body Mass Index with Survival in Asian Patients with Colorectal Cancer
- Sangwon Lee, Dong Hee Lee, Jae-Hoon Lee, Su-Jin Shin, Hye Sun Lee, Eun Jung Park, Seung Hyuk Baik, Kang Young Lee, Jeonghyun Kang
- Cancer Res Treat. 2022;54(3):860-872. Published online October 15, 2021
- DOI: https://doi.org/10.4143/crt.2021.656
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Abstract
PDFSupplementary MaterialPubReaderePub
- Purpose
The clinical significance of body mass index (BMI) on long-term outcomes has not been extensively investigated in Asian patients with colorectal cancer (CRC). This study aims to describe the association between BMI and survival, plus providing BMI cut-off value for predicting prognosis in CRC patients.
Materials and Methods
A total of 1,182 patients who had undergone surgery for stage I-III CRC from June 2004 to February 2014 were included. BMI was categorized into four groups based on the recommendation for Asian ethnicity. The optimal BMI cut-off value was determined to maximize overall survival (OS) difference.
Results
In multivariable analysis, underweight BMI was significantly associated with poor OS (hazard ratio [HR], 2.38; 95% confidence interval [CI], 1.55 to 3.71; p < 0.001) and obese BMI was associated with better OS (HR, 0.72; 95% CI, 0.53 to 0.97; p=0.036) compared with the normal BMI. Overweight and obese BMI were associated with better recurrence-free survival (HR, 0.64; 95% CI, 0.42 to 0.99; p=0.046 and HR, 0.58; 95% CI, 0.38 to 0.89; p=0.014, respectively) compared with the normal BMI group. BMI cutoff value was 20.44 kg/m2. Adding the BMI cutoff value to cancer staging could increase discriminatory performance in terms of integrated area under the curve and Harrell’s concordance index.
Conclusion
Compared to normal BMI, underweight BMI was associated with poor survival whereas obese BMI was associated with better survival. BMI cut-off value of 20.44 kg/m2 is a useful discriminator in Asian patients with CRC. -
Citations
Citations to this article as recorded by- Impact of overweight and obesity on gastric and colorectal cancer incidence in the older adults: a nationwide cohort study
Jinju Choi, Cheol Min Shin, Kyungdo Han, Jin-Hyung Jung, Se Yun Kim, Hyemin Jo, Ho-Kyoung Lee, Eun Hyo Jin, Seung Joo Kang, Joo Hyun Lim, Dong Ho Lee
BMC Geriatrics.2026;[Epub] CrossRef - Survival Outcomes of Patients With Colorectal Cancer After Primary Surgery: A Population‐Based Study in Hainan, China
Le Du, Luyao Sun, Dongsheng Ge, Mingfa Wang, Benguo Yu, Wenjun Yang, Wenlong Yang, Ruyuan An, Junchao Li, Jike Zhu, Zaipan Wang, Ning Liu, Wenting Cao
Cancer Medicine.2026;[Epub] CrossRef - Comment on “Dense Tumor‐Infiltrating Lymphocytes (TILs) in Liver Metastasis From Colorectal Cancer Are Related to Improved Overall Survival”
Fuji Lai, Sheng Li, Zhonglei Shen
Journal of Surgical Oncology.2025; 131(4): 566. CrossRef - Body mass index, weight change, and cancer prognosis: a meta-analysis and systematic review of 73 cohort studies
H. Wen, G. Deng, X. Shi, Z. Liu, A. Lin, Q. Cheng, J. Zhang, P. Luo
ESMO Open.2024; 9(3): 102241. CrossRef - Trends in Anticoagulant Utilization and Clinical Outcomes for Cancer-Associated Thrombosis: A Multicenter Cohort Study in Thailand's Upper-Middle–Income Country Setting
Kirati Kengkla, Surakit Nathisuwan, Warunsuda Sripakdee, Pirun Saelue, Kwanruethai Sengnoo, Aumkhae Sookprasert, Suphat Subongkot
JCO Global Oncology.2024;[Epub] CrossRef - Post‐diagnosis adiposity and colorectal cancer prognosis: A Global Cancer Update Programme (CUP Global) systematic literature review and meta‐analysis
Nerea Becerra‐Tomás, Georgios Markozannes, Margarita Cariolou, Katia Balducci, Rita Vieira, Sonia Kiss, Dagfinn Aune, Darren C. Greenwood, Laure Dossus, Ellen Copson, Andrew G. Renehan, Martijn Bours, Wendy Demark‐Wahnefried, Melissa M. Hudson, Anne M. Ma
International Journal of Cancer.2024; 155(3): 400. CrossRef - Low muscle mass-to-fat ratio is an independent factor that predicts worse overall survival and complications in patients with colon cancer: a retrospective single-center cohort study
Jiabao Tang, Jingwen Xu, Xiaohua Li, Chun Cao
Annals of Surgical Treatment and Research.2024; 107(2): 68. CrossRef - Obesity, the Adipose Organ and Cancer in Humans: Association or Causation?
Elisabetta Trevellin, Silvia Bettini, Anna Pilatone, Roberto Vettor, Gabriella Milan
Biomedicines.2023; 11(5): 1319. CrossRef - Higher body mass index was associated with better prognosis in diabetic patients with stage II colorectal cancer
Xiao-Yu Liu, Bing Kang, Yu-Xi Cheng, Chao Yuan, Wei Tao, Bin Zhang, Zheng-Qiang Wei, Dong Peng
BMC Cancer.2022;[Epub] CrossRef - Preoperative carcinoembryonic antigen to body mass index ratio contributes to prognosis prediction in colorectal cancer
Jia Xiang, Mengyao Ding, Jixing Lin, Tianhui Xue, Qianwen Ye, Bing Yan
Oncology Letters.2022;[Epub] CrossRef
- Impact of overweight and obesity on gastric and colorectal cancer incidence in the older adults: a nationwide cohort study
- 11,681 View
- 161 Download
- 12 Web of Science
- 10 Crossref
- Hematologic malignancy
- Predictive Factors of Event-Free Survival at 24 Months in Patients with Peripheral T-Cell Lymphoma: A Retrospective Study
- Yu Ri Kim, Soo-Jeong Kim, Hye Sun Lee, Soyoung Jeon, Hyunsoo Cho, Haerim Chung, Ji Eun Jang, June-Won Cheong, Yoo Hong Min, Jin Seok Kim
- Cancer Res Treat. 2022;54(2):613-620. Published online August 5, 2021
- DOI: https://doi.org/10.4143/crt.2021.270
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Abstract
PDFPubReaderePub
- Purpose
Event-free survival at 24 months (EFS24) is known to be a surrogate marker for overall survival (OS) for patients with peripheral T-cell lymphoma (PTCL). We examined the role of EFS24 in PTCL compared to diffuse large B-cell lymphoma (DLBCL), and then assessed the clinical predictive factors of achieving EFS24.
Materials and Methods
Patients with newly diagnosed PTCL treated with anthracycline-based chemotherapy were included. Subsequent OS was defined as the time elapsed from 24 months after diagnosis until death from any cause in those who achieved EFS24.
Results
Overall, 153 patients were evaluated, and 51 patients (33.3%) achieved EFS24. Patients who achieved EFS24 showed superior OS compared to patients who did not (p < 0.001). EFS24 could stratify the subsequent OS although it did not reach to that of the general population. After matching the PTCL group to the DLBCL group based on the international prognostic index, the subsequent OS in patients who achieved EFS24 was similar between the two groups (p=0.094). Advanced stage was a significant factor to predict the failing EFS24 by multivariable analysis (p < 0.001).
Conclusion
Patients with PTCL who achieve EFS24 could have a favorable subsequent OS. Since advanced disease stage is a predictor of EFS24 failure, future efforts should focus on developing novel therapeutic strategies for PTCL patients presenting with advanced disease. -
Citations
Citations to this article as recorded by- Prognostic impact of pre-treatment and post-treatment plasma Epstein-Barr virus DNA in peripheral T-cell lymphomas
Chu-Yi Chan, Tung-Liang Lin, Ming-Chung Kuo, Yu-Shin Hung, Hung Chang, Che-Wei Ou, Jin-Hou Wu, Hsuan-Jen Shih, Yi-Jiun Su, Lee-Yung Shih, Yuen-Chin Ong, Wen-Yu Chuang, Hsiao-Wen Kao
Annals of Medicine.2025;[Epub] CrossRef - Clinical significance and predictive risk factors for event-free survival at 24 months in patients with PTCL, NOS
Zheng Cao, Xiaojun Wang, Xuemin Xue, Xiaoli Feng
Annals of Hematology.2024; 103(3): 869. CrossRef - Validity of event-free survival as a surrogate endpoint in haematological malignancy: Review of the literature and health technology assessments
Sarit Assouline, Adriana Wiesinger, Clare Spooner, Jelena Jovanović, Max Schlueter
Critical Reviews in Oncology/Hematology.2022; 175: 103711. CrossRef
- Prognostic impact of pre-treatment and post-treatment plasma Epstein-Barr virus DNA in peripheral T-cell lymphomas
- 9,120 View
- 146 Download
- 4 Web of Science
- 3 Crossref
- Gastrointestinal Cancer
- LASSO-Based Machine Learning Algorithm for Prediction of Lymph Node Metastasis in T1 Colorectal Cancer
- Jeonghyun Kang, Yoon Jung Choi, Im-kyung Kim, Hye Sun Lee, Hogeun Kim, Seung Hyuk Baik, Nam Kyu Kim, Kang Young Lee
- Cancer Res Treat. 2021;53(3):773-783. Published online December 29, 2020
- DOI: https://doi.org/10.4143/crt.2020.974
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Abstract
PDFSupplementary MaterialPubReaderePub
- Purpose
The role of tumor-infiltrating lymphocytes (TILs) in predicting lymph node metastasis (LNM) in patients with T1 colorectal cancer (CRC) remains unclear. Furthermore, clinical utility of a machine learning–based approach has not been widely studied.
Materials and Methods
Immunohistochemistry for TILs against CD3, CD8, and forkhead box P3 in both center and invasive margin of the tumor were performed using surgically resected T1 CRC slides. Three hundred and sixteen patients were enrolled and categorized into training (n=221) and validation (n=95) sets via random sampling. Using clinicopathologic variables including TILs, the least absolute shrinkage and selection operator (LASSO) regression model was applied for variable selection and predictive signature building in the training set. The predictive accuracy of our model and the Japanese criteria were compared using area under the receiver operating characteristic (AUROC), net reclassification improvement (NRI)/integrated discrimination improvement (IDI), and decision curve analysis (DCA) in the validation set.
Results
LNM was detected in 29 (13.1%) and 12 (12.6%) patients in training and validation sets, respectively. Nine variables were selected and used to generate the LASSO model. Its performance was similar in training and validation sets (AUROC, 0.795 vs. 0.765; p=0.747). In the validation set, the LASSO model showed better outcomes in predicting LNM than Japanese criteria, as measured by AUROC (0.765 vs. 0.518, p=0.003) and NRI (0.447, p=0.039)/IDI (0.121, p=0.034). DCA showed positive net benefits in using our model.
Conclusion
Our LASSO model incorporating histopathologic parameters and TILs showed superior performance compared to conventional Japanese criteria in predicting LNM in patients with T1 CRC. -
Citations
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Frontiers in Immunology.2026;[Epub] CrossRef - Integrating single-cell RNA-seq and machine learning to dissect polyamine metabolism in metabolic dysfunction-associated steatotic liver disease
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Frontiers in Medicine.2026;[Epub] CrossRef - Integrated multi-omics analysis identifies TBC1D10C as an NK- and T cell-associated diagnostic biomarker in childhood obesity
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Molecular Neurobiology.2026;[Epub] CrossRef - Identification and validation of cholesterol homeostasis and NK cell-associated predictive and therapeutic models for Coronary atherosclerotic heart disease patients with ST-elevated myocardial infarction: Insights from artificial intelligence and multi-o
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Computational Biology and Chemistry.2026; 124: 109103. CrossRef - Construction and validation of a predictive model for the risk of emergence delirium in older adult patients
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World Journal of Psychiatry.2026;[Epub] CrossRef - Bioinformatics reveals the prognostic potential of manganese metabolism-related genes in lung adenocarcinoma
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Functional & Integrative Genomics.2026;[Epub] CrossRef - Construction and internal temporal validation of a LASSO regression-based risk assessment model for non-suicidal self-injury addiction-like features in adolescents and young adults with depression
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Frontiers in Psychology.2026;[Epub] CrossRef - AI-Guided Ferroptosis Biomarker Discovery and Routine Laboratory–Based Machine Learning for Predicting Nodal Metastasis in Colon Cancer
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Metabolites.2026; 16(8): 557. CrossRef - Multiparametric Radiomics for Characterization and Outcome Prediction in Colorectal Cancer: The Central Role of Diagnostic Imaging
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Cancers.2026; 18(16): 2578. CrossRef - A machine learning–based approach to establish predictors of recurrent tuberculosis in the Guelmim region of Morocco
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Biostatistics & Epidemiology.2026;[Epub] CrossRef - Multiparametric MRI radiomics integrating Arterial Spin Labeling, Quantitative Susceptibility Mapping, and Amide Proton Transfer imaging for differentiating glioma recurrence from post-treatment response
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Frontiers in Oncology.2026;[Epub] CrossRef - Development and internal validation of a machine-learning model for 3-year mortality after lung cancer resection: an online risk calculator and nomogram
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Frontiers in Medicine.2026;[Epub] CrossRef - Assessing prospective molecular biomarkers and functional pathways in severe asthma based on a machine learning method and bioinformatics analyses
Ya-Da Zhang, Yi-Ren Chen, Wei Zhang, Bin-Qing Tang
Journal of Asthma.2025; 62(3): 465. CrossRef - A web-based tool for cancer risk prediction for middle-aged and elderly adults using machine learning algorithms and self-reported questions
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Annals of Epidemiology.2025; 101: 27. CrossRef - Risk stratification scores for lymph node metastases in T1 colorectal cancer—A systematic review
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Gut and Liver.2025; 19(1): 3. CrossRef - Utilizing Ensemble Learning and Dimension Reduction in Predicting Stock Prices: A Transparent Methodology with Insights from Explainable AI
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Scientific Reports.2025;[Epub] CrossRef - Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.)
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Frontiers in Plant Science.2025;[Epub] CrossRef - Following intravenous thrombolysis, the outcome of diabetes mellitus associated with acute ischemic stroke was predicted via machine learning
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Frontiers in Pharmacology.2025;[Epub] CrossRef - An early lung cancer diagnosis model for non-smokers incorporating ct imaging analysis and circulating genetically abnormal cells (CACs)
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BMC Cancer.2025;[Epub] CrossRef - Identification and validation of immune-related biomarkers and polarization types of macrophages in keloid based on bulk RNA-seq and single-cell RNA-seq analysis
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Burns.2025; 51(3): 107413. CrossRef - Comprehensive analysis of mitochondrial solute carrier family 25 (SLC25) identifies member 19 (SLC25A19) as a regulatory factor in hepatocellular carcinoma
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Gene.2025; 944: 149299. CrossRef - A dynamic nomogram predicting nosocomial infections in patients after colon cancer surgery
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Frontiers in Oncology.2025;[Epub] CrossRef - Machine learning models to predict 6-month mortality risk in home-based hospice patients with advanced cancer
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Asia-Pacific Journal of Oncology Nursing.2025; 12: 100679. CrossRef - Interpretable machine learning model for predicting depression in middle-aged and elderly Chinese arthritis patients: A nationwide prospective cohort study
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Archives of Gerontology and Geriatrics.2025; 134: 105810. CrossRef - Prediction of Lymph Node Metastasis in Colorectal Cancer Using Intraoperative Fluorescence Multi-Modal Imaging
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IEEE Transactions on Medical Imaging.2025; 44(3): 1568. CrossRef - Tertiary Lymphoid Structures Are Associated with Enhanced Macrophage Activation and Immune Checkpoint Expression and Predict Outcome in Cervical Cancer
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Cancer Immunology Research.2025; 13(5): 712. CrossRef - A predictive model for the transformation from cervical inflammation to cancer based on tumor immune-related factors
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Frontiers in Immunology.2025;[Epub] CrossRef - Application of an interpretable machine learning method to predict the risk of death during hospitalization in patients with acute myocardial infarction combined with diabetes mellitus
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PLOS ONE.2025; 20(4): e0321751. CrossRef - Establishment and validation of a nomogram model for surgical site infections after posterior lumbar interbody fusion: a retrospective observational study
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Biomedicines.2025; 13(5): 1067. CrossRef - Clinical potential and experimental validation of prognostic genes in hepatocellular carcinoma revealed by risk modeling utilizing single cell and transcriptome constructs
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Frontiers in Immunology.2025;[Epub] CrossRef - Identifying GAP43, NMU, and TEX29 as Potential Prognostic Biomarkers for COPD Combined With Lung Cancer Patients Using Machine Learning
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The Journal of Gene Medicine.2025;[Epub] CrossRef - Identification of DSC2 as a Key Biomarker for Induction Chemotherapy Sensitivity in Locally Advanced Laryngeal and Hypopharyngeal Squamous Cell Carcinoma
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Clinical Breast Cancer.2025; 25(6): e756. CrossRef - Multi-Omics-Based Analysis of the Effect of Longevity Genes on the Immune Relevance of Colorectal Cancer
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Biomedicines.2025; 13(5): 1085. CrossRef - EM-DeepSD: A Deep Neural Network Model Based on Cell-Free DNA End-Motif Signal Decomposition for Cancer Diagnosis
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Diagnostics.2025; 15(9): 1156. CrossRef - Identification of thyroid cancer biomarkers using WGCNA and machine learning
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European Journal of Medical Research.2025;[Epub] CrossRef - Identification of neutrophil extracellular trap-related biomarkers in ulcerative colitis based on bioinformatics and machine learning
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Frontiers in Genetics.2025;[Epub] CrossRef - Correlation Analysis Between Low-Density Lipoprotein and Clinical Prognosis in Aneurysmal Subarachnoid Hemorrhage: A Single-Center Study
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Journal of Craniofacial Surgery.2025; 36(6): e757. CrossRef - Integrated analysis of single-cell RNA-seq and spatial transcriptomics to identify the lactylation-related protein TUBB2A as a potential biomarker for glioblastoma in cancer cells by machine learning
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Frontiers in Immunology.2025;[Epub] CrossRef - Machine learning assists regulated cell death crucial biomarker selection in adenocarcinoma of the lung: biological data testing and cell assay determination
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Discover Oncology.2025;[Epub] CrossRef - Predicting the risk of lean non-alcoholic fatty liver disease based on interpretable machine models in a Chinese T2DM population
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Frontiers in Endocrinology.2025;[Epub] CrossRef - Investigation of the mechanism of Xiaoyin Jiedu Yin in the treatment of psoriasis based on bioinformatics, machine learning
Ru-Nan Fang, Yang Zhou, Yang Shen, Yuan Sun, Jian-Hong Li
Frontiers in Chemistry.2025;[Epub] CrossRef - Hybrid Intelligence-Driven Nanopolymeric Sensor for Precise Electrochemical Vitamin C Analysis, Free from LoD: Application in Real Lemon Juice
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ACS Applied Electronic Materials.2025; 7(15): 6980. CrossRef - A risk model based on signature genes predicts prognosis and associates with tumor immunity, drug sensitivity in breast cancer
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Cancer Biomarkers.2025;[Epub] CrossRef - Tumor Budding as a Risk Factor for Lymph Node Metastasis and Local Recurrence in pT1 Colorectal Cancer: A Systematic Review and Meta-Analysis
Heng Zhang, Femke Simmer, Alessandro Lugli, Iris D. Nagtegaal
Gastro Hep Advances.2025; 4(9): 100713. CrossRef - Interpretable machine learning approaches for predicting prostate cancer by using multiple heavy metal exposures based on the data from NHANES 2003–2018
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Ecotoxicology and Environmental Safety.2025; 302: 118730. CrossRef - CT-based intratumoral and peritumoral radiomics to predict the treatment response to hepatic arterial infusion chemotherapy plus lenvatinib and PD-1 in high-risk hepatocellular carcinoma cases: a multi-center study
Zihao Liu, Xinge Li, Yong Huang, Xu Chang, Hong Zhang, Xiaodong Wu, Yanzhao Diao, Fengling He, Junyong Sun, Baomin Feng, Hexin Liang
Hepatology International.2025; 19(6): 1397. CrossRef - Paternally Expressed Gene 10 Promoter Methylation Level as a Predictor of HBeAg Seroconversion in Chronic Hepatitis B Patients
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Journal of Medical Virology.2025;[Epub] CrossRef - The role of mitochondria-related genes and immune infiltration in carotid atherosclerosis: identification of hub targets through bioinformatics and machine learning approaches
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Frontiers in Genetics.2025;[Epub] CrossRef - Machine learning integration of bulk and single-cell RNA-seq data reveals glycolytic heterogeneity in colorectal cancer
Yuanyuan Du, Zefeng Miao, Peng Li, Dan Feng, Mulin Liu, Aifang Ji, Shijun Li
Medical Oncology.2025;[Epub] CrossRef - Identification and Experimental Validation of OS-Related Gene Sets Based on Integrated Analysis of Single-Cell and Bulk RNA Sequencing Data with Machine Learning in Patients with Sepsis
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Inflammation.2025; 48(6): 4549. CrossRef - Prognostic Significance of R-Loops in Lung Adenocarcinoma: Implications for Immune Response and Drug Sensitivity
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Cancer Management and Research.2025; Volume 17: 1625. CrossRef - Significant adverse prognostic events in patients with urosepsis: a machine learning based model development and validation study
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Frontiers in Cellular and Infection Microbiology.2025;[Epub] CrossRef - Dendrobin A inhibits gastric cancer: Mechanistic insights supported by integrated evidence
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Phytomedicine.2025; 147: 157215. CrossRef - Analysis of the toxicity and mechanisms of osteoporosis caused by cigarette toxicants using network toxicology and molecular docking techniques
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Science of The Total Environment.2025; 1000: 180414. CrossRef - Machine Learning Integration of Bulk and Single-Cell RNA-Seq Data Reveals Cathepsin B as a Central PANoptosis Regulator in Influenza
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International Journal of Molecular Sciences.2025; 26(17): 8533. CrossRef - Prediction Models of Microinvasive Cervical Cancer in High-Grade Squamous Intraepithelial Lesion Treatment by Loop Electrosurgical Excision Procedure
Maodan Huang, Xiaohong Chen, Xin Lin, Yuxiang Yang, Lu Liu, Youzhong Zhang, Ronglong Wang, Wei Chen
Risk Management and Healthcare Policy.2025; Volume 18: 2921. CrossRef - Interpretable Machine Learning Models for Predicting Lateral Pelvic Lymph Node Metastasis in Rectal Cancer: A Chinese Multicenter Retrospective Study
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International Journal of Surgery.2025; 111(9): 5904. CrossRef - Polybrominated diphenyl ether profiles in adipose tissues of breast cancer patients and their carcinogenic potential investigation based on network toxicology and molecular docking
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Frontiers in Chemistry.2025;[Epub] CrossRef - Role of SAA1 in mediating renal fibrosis through TLR4-dependent NF-κB activation
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BMC Nephrology.2025;[Epub] CrossRef - Mendelian Randomization and Machine Learning Reveal Immune Cell and Gene Drivers in Systemic Lupus Erythematosus
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Brain and Behavior.2025;[Epub] CrossRef - Explainable machine learning model for predicting the outcome of acute ischemic stroke after intravenous thrombolysis
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Frontiers in Neurology.2025;[Epub] CrossRef - Relationships Among Mobile Internet Use, Social Support, and Depressive Symptoms: Prospective Cohort Study Among Community Residents
Yingyue Xu, Meiqi Wang, Qixiu Li, Xiaoying Su, Long Sun
Journal of Medical Internet Research.2025; 27: e76567. CrossRef - Optimizing methane catalytic hydrogen production via a hybrid Boruta-XGB and stacking ensemble machine learning framework
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International Journal of Hydrogen Energy.2025; 181: 151812. CrossRef - Prediction of prognosis in T4 or N3 locally advanced nasopharyngeal carcinoma receiving chemoradiotherapy using machine learning methods
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Frontiers in Oncology.2025;[Epub] CrossRef - Development and validation of an endoscopic diagnostic model for sessile serrated lesions based on machine learning algorithms
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Frontiers in Medicine.2025;[Epub] CrossRef - Applying machine learning for perioperative adverse event prediction: a narrative review toward better clinical efficacy and usability
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Anesthesiology and Perioperative Science.2025;[Epub] CrossRef - The diagnostic accuracy of deep learning-based AI models in predicting lymph node metastasis in T1 and T2 colorectal cancer: A systematic review and meta-analysis
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Medicine.2025; 104(45): e45172. CrossRef - Identification of mitochondrial-related genes to evaluate the immune infiltration and prognosis of lung adenocarcinoma
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PeerJ.2025; 13: e20262. CrossRef - The toxicological impact of PET-MPs exposure on atherosclerosis: insights from network toxicology, molecular docking, and machine learning
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Scientific Reports.2025;[Epub] CrossRef - Predicting Age-Related Hearing Loss in Community-Dwelling Older Adults: Multicenter Retrospective Cohort Study
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Interactive Journal of Medical Research.2025; 14: e81135. CrossRef - Development and explanation of a machine learning model for identifying non-localized early-onset T1 colorectal cancer
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Discover Oncology.2025;[Epub] CrossRef - Development and validation of an AI-augmented deep learning model for survival prediction in de novo metastatic colorectal cancer
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Discover Oncology.2025;[Epub] CrossRef - Inflammatory markers partially mediate the association between volatile organic compounds exposure and hyperlipidemia: a nationally representative cross-sectional study from NHANES
Yaxiong Nie, Zining He, Bei Liu, Jiaai Li, Yanyu Liu, Xin Su, Zhiqiang Yan, Zheng Li, Chang Yan, Qian Lu, Yanfang Fu, Wanyu Yang, Yutong He
Frontiers in Public Health.2025;[Epub] CrossRef - Clinical applications of artificial intelligence in the histopathology of lymphoma: diagnosis, treatment and prognosis
Mengyao Kang, Zibo Yang, Tian Yu, Dongyu Li, Zhiqiong Wang, Liting Chen
Discover Oncology.2025;[Epub] CrossRef - Targeting PSMB5-induced PANoptosis in bladder cancer: multi-omics insights and TCM candidate discovery
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Frontiers in Immunology.2025;[Epub] CrossRef - Development of robust machine learning models to estimate hydrochar higher heating value and yield based upon biomass proximate analysis
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Bioresources and Bioprocessing.2025;[Epub] CrossRef - Comprehensive molecular characterization of high-stemness gastric cancer cells using single-cell transcriptomics, spatial mapping, and machine learning
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npj Precision Oncology.2025;[Epub] CrossRef - Machine learning-based survival prediction in colorectal cancer combining clinical and biological features
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Oncotarget.2025; 16(1): 834. CrossRef - Analysis of CSF2RA Expression and Its Functional and Clinical Significance in Gastric Cancer Based on Bioinformatics
Rong He, Yongle Li, Lin Jiang, Mingtao Luo, Shiquan Wei, Lihe Jiang
Journal of Biosciences and Medicines.2025; 13(09): 405. CrossRef - Additional staining for lymphovascular invasion is associated with increased estimation of lymph node metastasis in patients with T1 colorectal cancer: Systematic review and meta‐analysis
Jun Watanabe, Katsuro Ichimasa, Yuki Kataoka, Atsushi Miki, Hidehiro Someko, Munenori Honda, Makiko Tahara, Takeshi Yamashina, Khay Guan Yeoh, Shigeo Kawai, Kazuhiko Kotani, Naohiro Sata
Digestive Endoscopy.2024; 36(5): 533. CrossRef - Risk assessment in pT1 colorectal cancer
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Journal of Clinical Pathology.2024; 77(4): 225. CrossRef - Identification and Analysis of Immune Microenvironment-Related Genes for Keloid Risk Prediction and Their Effects on Keloid Proliferation and Migration
Yongyan Pei, Yikai Wu, Mengqi Zhang, Xuemin Su, Hua Cao, Jiaji Zhao
Biochemical Genetics.2024; 62(4): 3174. CrossRef - Multiple machine-learning tools identifying prognostic biomarkers for acute Myeloid Leukemia
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BMC Medical Informatics and Decision Making.2024;[Epub] CrossRef - Artificial Intelligence Applications in the Treatment of Colorectal Cancer: A Narrative Review
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Clinical Medicine Insights: Oncology.2024;[Epub] CrossRef - Transcriptomic and machine learning analyses identify hub genes of metabolism and host immune response that are associated with the progression of breast capsular contracture
Yukun Mao, Xueying Hou, Su Fu, Jie Luan
Genes & Diseases.2024; 11(3): 101087. CrossRef - Bioinformatics and Machine Learning Methods Identified MGST1 and QPCT as Novel Biomarkers for Severe Acute Pancreatitis
Yang Sun, Jingjun Xie, Jun Zhu, Yadong Yuan
Molecular Biotechnology.2024; 66(5): 1246. CrossRef - Multi-omics reveals the role of ENO1 in bladder cancer and constructs an epithelial-related prognostic model to predict prognosis and efficacy
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Scientific Reports.2024;[Epub] CrossRef - Use of artificial intelligence in the management of T1 colorectal cancer: a new tool in the arsenal or is deep learning out of its depth?
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Clinical Endoscopy.2024; 57(1): 24. CrossRef - SLCO4A1, as a novel prognostic biomarker of non‑small cell lung cancer, promotes cell proliferation and migration
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Frontiers in Oncology.2024;[Epub] CrossRef - Management after non-curative endoscopic resection of T1 rectal cancer
Hao Dang, Daan A. Verhoeven, Jurjen J. Boonstra, Monique E. van Leerdam
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-
Identification of nitric oxide-related key genes in pulmonary hypertension via bioinformatics and
in vitro
validation for therapeutic target discovery
- 24,116 View
- 428 Download
- 225 Web of Science
- 217 Crossref
- Genitourinary cancer
- External Validation of the Long Short-Term Memory Artificial Neural Network-Based SCaP Survival Calculator for Prediction of Prostate Cancer Survival
- Bumjin Lim, Kwang Suk Lee, Young Hwa Lee, Suah Kim, Choongki Min, Ju-Young Park, Hye Sun Lee, Jin Seon Cho, Sun Il Kim, Byung Ha Chung, Choung-Soo Kim, Kyo Chul Koo
- Cancer Res Treat. 2021;53(2):558-566. Published online October 6, 2020
- DOI: https://doi.org/10.4143/crt.2020.637
-
Abstract
PDFPubReaderePub
- Purpose
Decision-making for treatment of newly diagnosed prostate cancer (PCa) is complex due to the multiple initial treatment modalities available. We aimed to externally validate the SCaP (Severance Study Group of Prostate Cancer) Survival Calculator that incorporates a long short-term memory artificial neural network (ANN) model to estimate survival outcomes of PCa according to initial treatment modality. Materials and Methods The validation cohort consisted of clinicopathological data of 4,415 patients diagnosed with biopsy-proven PCa between April 2005 and November 2018 at three institutions. Area under the curves (AUCs) and time-to-event calibration plots were utilized to determine the predictive accuracies of the SCaP Survival Calculator in terms of progression to castration-resistant PCa (CRPC)–free survival, cancer-specific survival (CSS), and overall survival (OS). Results Excellent discrimination was observed for CRPC-free survival, CSS, and OS outcomes, with AUCs of 0.962, 0.944, and 0.884 for 5-year outcomes and 0.959, 0.928, and 0.854 for 10-year outcomes, respectively. The AUC values were higher for all survival endpoints compared to those of the development cohort. Calibration plots showed that predicted probabilities of 5-year survival endpoints had concordance comparable to those of the observed frequencies. However, calibration performances declined for 10-year predictions with an overall underestimation. Conclusion The SCaP Survival Calculator is a reliable and useful tool for determining the optimal initial treatment modality and for guiding survival predictions for patients with newly diagnosed PCa. Further modifications in the ANN model incorporating cases with more extended follow-up periods are warranted to improve the ANN model for long-term predictions. -
Citations
Citations to this article as recorded by- Harnessing machine learning to predict prostate cancer survival: a review
Sungun Bang, Young Jin Ahn, Kyo Chul Koo
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Fadila Kouhen, Meryem Naciri, Hanae El Gouache, Nadia Errafiy, Abdelhak Maghous
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Mengying Zhu, Zhichao Gu, Fang Chen, Xi Chen, Yue Wang, Guohua Zhao
Frontiers in Oncology.2024;[Epub] CrossRef - Computational Intelligence in Cancer Diagnostics: A Contemporary Review of Smart Phone Apps, Current Problems, and Future Research Potentials
Somit Jain, Dharmik Naicker, Ritu Raj, Vedanshu Patel, Yuh-Chung Hu, Kathiravan Srinivasan, Chun-Ping Jen
Diagnostics.2023; 13(9): 1563. CrossRef - Development and external validation of a machine learning-based model to classify uric acid stones in patients with kidney stones of Hounsfield units < 800
Ben H. Chew, Victor K. F. Wong, Abdulghafour Halawani, Sujin Lee, Sangyeop Baek, Hoyong Kang, Kyo Chul Koo
Urolithiasis.2023;[Epub] CrossRef - Multilayer perceptron-based prediction of stroke mimics in prehospital triage
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Scientific Reports.2022;[Epub] CrossRef
- Harnessing machine learning to predict prostate cancer survival: a review
- 9,410 View
- 130 Download
- 6 Crossref
- Prognostic Scoring Index for Patients with Metastatic Pancreatic Adenocarcinoma
- Hyung Soon Park, Hye Sun Lee, Ji Soo Park, Joon Seong Park, Dong Ki Lee, Se-Joon Lee, Dong Sup Yoon, Min Goo Lee, Hei-Cheul Jeung
- Cancer Res Treat. 2016;48(4):1253-1263. Published online February 3, 2016
- DOI: https://doi.org/10.4143/crt.2015.400
-
Abstract
PDFPubReaderePub
- Purpose
This study focused on implementation of a prognostic scoring index based on clinico-laboratory parameters measured routinely on admission in metastatic pancreatic cancer patients.
Materials and Methods
Records from 403 patients of metastatic disease were analyzed retrospectively. Continuous variables were dichotomized according to the normal range or the best cut-off values statistically determined by Contal and O’Quigley method, and then analyzed in association with prognosis—overall survival (OS), using Cox’s proportional hazard model. Scores were calculated by summing the rounded chi-square scores for the factors that emerged in the multivariate analysis.
Results
Performance status, hemoglobin, leucocyte count, neutrophil-lymphocyte ratio, and carcinoembryonic antigen were independent factors for OS. When patients were divided into three risk groups according to these factors, median survival was 11.7, 6.2, and 1.3 months for the low, intermediate, and high-risk groups, respectively (p < 0.001). Palliative chemotherapy has a significant survival benefit for low and intermediate-risk patients (median OS; 12.5 months vs. 5.9 months, p < 0.001 and 8.0 months vs. 2.0 months, p < 0.001, respectively).
Conclusion
We advocate the use of a multivariable approach with continuous variables for prognostic modeling. Our index is helpful in accurate patient risk stratification and may aid in treatment selection. -
Citations
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