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Chun L, Wang D, He L, Li D, Fu Z, Xue S, Su X, Zhou J. Explainable machine learning model for predicting paratracheal lymph node metastasis in cN0 papillary thyroid cancer. Sci Rep 2024; 14:22361. [PMID: 39333646 PMCID: PMC11436978 DOI: 10.1038/s41598-024-73837-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2024] [Accepted: 09/20/2024] [Indexed: 09/29/2024] Open
Abstract
Prophylactic dissection of paratracheal lymph nodes in clinically lymph node-negative (cN0) papillary thyroid carcinoma (PTC) remains controversial. This study aims to integrate preoperative and intraoperative variables to compare traditional nomograms and machine learning (ML) models, developing and validating an interpretable predictive model for paratracheal lymph node metastasis (PLNM) in cN0 PTC patients. We retrospectively selected 3213 PTC patients treated at the First Affiliated Hospital of Chongqing Medical University from 2016 to 2020. They were randomly divided into the training and test datasets with a 7:3 ratio. The 533 PTC patients treated at the Guangyuan Central Hospital from 2019 to 2022 were used as an external test sets. We developed and validated nine ML models using 10-fold cross-validation and grid search for hyperparameter tuning. The predictive performance was evaluated using ROC curves, decision curve analysis (DCA), calibration curves, and precision-recall curves. The best model was compared to a traditional logistic regression-based nomogram. The XGBoost model achieved AUC values of 0.935, 0.857, and 0.775 in the training, validation, and test sets, respectively, significantly outperforming the traditional nomogram model with AUCs of 0.85, 0.844, and 0.769, respectively. SHapley Additive exPlanations (SHAP)-based visualization identified the top 10 predictive features of the XGBoost model, and a web-based calculator was created based on these features. ML is a reliable tool for predicting PLNM in cN0 PTC patients. The SHAP method provides valuable insights into the XGBoost model, and the resultant web-based calculator is a clinically useful tool to assist in the surgical planning for paratracheal lymph node dissection.
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Affiliation(s)
- Lin Chun
- Department of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 404100, China
| | - Denghuan Wang
- Department of Thyroid and Breast Surgery, Guangyuan Central Hospital, Sichuan, 628400, China
| | - Liqiong He
- Department of Thyroid and Breast Surgery, Guangyuan Central Hospital, Sichuan, 628400, China
| | - Donglun Li
- Department of Nephrology, University Hospital Essen, University of Duisburg-Essen, 45147, Essen, Germany
| | - Zhiping Fu
- Department of Thyroid and Breast Surgery, Guangyuan Central Hospital, Sichuan, 628400, China
| | - Song Xue
- Intelligent Integrated Circuits and Systems Laboratory (SICS Lab), University of Electronic Science and Technology of China, Chengdu, 611730, China
| | - Xinliang Su
- Department of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 404100, China.
| | - Jing Zhou
- Department of Thyroid and Breast Surgery, Chongqing Health Center for Women and Children, Women and Children's Hospital of Chongqing Medical University, Chongqing, 401120, China.
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Yang F, Li C, Yang W, He Y, Wu L, Jiang K, Sun C. Development and validation of an explainable machine learning model for predicting multidimensional frailty in hospitalized patients with cirrhosis. Brief Bioinform 2024; 25:bbae491. [PMID: 39358034 PMCID: PMC11446601 DOI: 10.1093/bib/bbae491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 09/10/2024] [Accepted: 09/17/2024] [Indexed: 10/04/2024] Open
Abstract
We sought to develop and validate a machine learning (ML) model for predicting multidimensional frailty based on clinical and laboratory data. Moreover, an explainable ML model utilizing SHapley Additive exPlanations (SHAP) was constructed. This study enrolled 622 patients hospitalized due to decompensating episodes at a tertiary hospital. The cohort data were randomly divided into training and test sets. External validation was carried out using 131 patients from other tertiary hospitals. The frail phenotype was defined according to a self-reported questionnaire (Frailty Index). The area under the receiver operating characteristics curve was adopted to compare the performance of five ML models. The importance of the features and interpretation of the ML models were determined using the SHAP method. The proportions of cirrhotic patients with nonfrail and frail phenotypes in combined training and test sets were 87.8% and 12.2%, respectively, while they were 88.5% and 11.5% in the external validation dataset. Five ML algorithms were used, and the random forest (RF) model exhibited substantially predictive performance. Regarding the external validation, the RF algorithm outperformed other ML models. Moreover, the SHAP method demonstrated that neutrophil-to-lymphocyte ratio, age, lymphocyte-to-monocyte ratio, ascites, and albumin served as the most important predictors for frailty. At the patient level, the SHAP force plot and decision plot exhibited a clinically meaningful explanation of the RF algorithm. We constructed an ML model (RF) providing accurate prediction of frail phenotype in decompensated cirrhosis. The explainability and generalizability may foster clinicians to understand contributors to this physiologically vulnerable situation and tailor interventions.
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Affiliation(s)
- Fang Yang
- Department of Digestive System, Baodi Clinical College of Tianjin Medical University, No.8 Guangchuan Road, Baodi District, Tianjin 301800, China
| | - Chaoqun Li
- Department of Geriatrics, Tianjin Hexi Hospital, No.43 Qiongzhou Road, Hexi District, Tianjin 300202, China
| | - Wanting Yang
- Department of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin 300052, China
| | - Yumei He
- Department of Gastroenterology, The Third People's Hospital of Chengdu, Chengdu 610031, Sichuan Province, China
| | - Liping Wu
- Department of Gastroenterology, The Third People's Hospital of Chengdu, Chengdu 610031, Sichuan Province, China
| | - Kui Jiang
- Department of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin 300052, China
| | - Chao Sun
- Department of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Anshan Road 154, Heping District, Tianjin 300052, China
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Lin WC, Weng CS, Ko AT, Jan YT, Lin JB, Wu KP, Lee J. Interpretable machine learning model based on clinical factors for predicting muscle radiodensity loss after treatment in ovarian cancer. Support Care Cancer 2024; 32:544. [PMID: 39046568 DOI: 10.1007/s00520-024-08757-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 07/21/2024] [Indexed: 07/25/2024]
Abstract
PURPOSE Muscle radiodensity loss after surgery and adjuvant chemotherapy is associated with poor outcomes in ovarian cancer. Assessing muscle radiodensity is a real-world clinical challenge owing to the requirement for computed tomography (CT) with consistent protocols and labor-intensive processes. This study aimed to use interpretable machine learning (ML) to predict muscle radiodensity loss. METHODS This study included 723 patients with ovarian cancer who underwent primary debulking surgery and platinum-based chemotherapy between 2010 and 2019 at two tertiary centers (579 in cohort 1 and 144 in cohort 2). Muscle radiodensity was assessed from pre- and post-treatment CT acquired with consistent protocols, and a decrease in radiodensity ≥ 5% was defined as loss. Six ML models were trained, and their performances were evaluated using the area under the curve (AUC) and F1-score. The SHapley Additive exPlanations (SHAP) method was applied to interpret the ML models. RESULTS The CatBoost model achieved the highest AUC of 0.871 (95% confidence interval, 0.870-0.874) and F1-score of 0.688 (95% confidence interval, 0.685-0.691) among the models in the training set and outperformed in the external validation set, with an AUC of 0.839 and F1-score of 0.673. Albumin change, ascites, and residual disease were the most important features associated with a higher likelihood of muscle radiodensity loss. The SHAP force plot provided an individualized interpretation of model predictions. CONCLUSION An interpretable ML model can assist clinicians in identifying ovarian cancer patients at risk of muscle radiodensity loss after treatment and understanding the contributors of muscle radiodensity loss.
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Affiliation(s)
- Wan-Chun Lin
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Li-Nong St., Beitou District, Taipei, 112304, Taiwan
| | - Chia-Sui Weng
- Department of Obstetrics and Gynecology, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
| | - Ai-Tung Ko
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Li-Nong St., Beitou District, Taipei, 112304, Taiwan
| | - Ya-Ting Jan
- Department of Radiology, MacKay Memorial Hospital, Taipei, Taiwan
| | - Jhen-Bin Lin
- Department of Radiation Oncology, Changhua Christian Hospital, Changhua, Taiwan
| | - Kun-Pin Wu
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Li-Nong St., Beitou District, Taipei, 112304, Taiwan.
| | - Jie Lee
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan.
- Department of Radiation Oncology, MacKay Memorial Hospital, 92, Section 2, Chung Shan North Road, Taipei, 104217, Taiwan.
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Callaway CS, Mouchantat LM, Bitler BG, Bonetto A. Mechanisms of Ovarian Cancer-Associated Cachexia. Endocrinology 2023; 165:bqad176. [PMID: 37980602 PMCID: PMC10699881 DOI: 10.1210/endocr/bqad176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/02/2023] [Accepted: 11/15/2023] [Indexed: 11/21/2023]
Abstract
Cancer-associated cachexia occurs in 50% to 80% of cancer patients and is responsible for 20% to 30% of cancer-related deaths. Cachexia limits survival and treatment outcomes, and is a major contributor to morbidity and mortality during cancer. Ovarian cancer is one of the leading causes of cancer-related deaths in women, and recent studies have begun to highlight the prevalence and clinical impact of cachexia in this population. Here, we review the existing understanding of cachexia pathophysiology and summarize relevant studies assessing ovarian cancer-associated cachexia in clinical and preclinical studies. In clinical studies, there is increased evidence that reduced skeletal muscle mass and quality associate with worse outcomes in subjects with ovarian cancer. Mouse models of ovarian cancer display cachexia, often characterized by muscle and fat wasting alongside inflammation, although they remain underexplored relative to other cachexia-associated cancer types. Certain soluble factors have been identified and successfully targeted in these models, providing novel therapeutic targets for mitigating cachexia during ovarian cancer. However, given the relatively low number of studies, the translational relevance of these findings is yet to be determined and requires more research. Overall, our current understanding of ovarian cancer-associated cachexia is insufficient and this review highlights the need for future research specifically aimed at exploring mechanisms of ovarian cancer-associated cachexia by using unbiased approaches and animal models representative of the clinical landscape of ovarian cancer.
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Affiliation(s)
- Chandler S Callaway
- Department of Pathology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Lila M Mouchantat
- Department of Pathology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Benjamin G Bitler
- Department of Obstetrics & Gynecology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Comprehensive Cancer Center, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Andrea Bonetto
- Department of Pathology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Comprehensive Cancer Center, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
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