{"title":"基于多任务机器学习的肿瘤相关胶原蛋白特征可预测胃癌腹膜复发和无病生存期","authors":"Meiting Fu, Yuyu Lin, Junyao Yang, Jiaxin Cheng, Liyan Lin, Guangxing Wang, Chenyan Long, Shuoyu Xu, Jianping Lu, Guoxin Li, Jun Yan, Gang Chen, Shuangmu Zhuo, Dexin Chen","doi":"10.1007/s10120-024-01551-0","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Background</h3><p>Accurate prediction of peritoneal recurrence for gastric cancer (GC) is crucial in clinic. The collagen alterations in tumor microenvironment affect the migration and treatment response of cancer cells. Herein, we proposed multitask machine learning-based tumor-associated collagen signatures (TACS), which are composed of quantitative collagen features derived from multiphoton imaging, to simultaneously predict peritoneal recurrence (TACS<sub>PR</sub>) and disease-free survival (TACS<sub>DFS</sub>).</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>Among 713 consecutive patients, with 275 in training cohort, 222 patients in internal validation cohort, and 216 patients in external validation cohort, we developed and validated a multitask machine learning model for simultaneously predicting peritoneal recurrence (TACS<sub>PR</sub>) and disease-free survival (TACS<sub>DFS</sub>). The accuracy of the model for prediction of peritoneal recurrence and prognosis as well as its association with adjuvant chemotherapy were evaluated.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The TACS<sub>PR</sub> and TACS<sub>DFS</sub> were independently associated with peritoneal recurrence and disease-free survival in three cohorts, respectively (all <i>P</i> < 0.001). The TACS<sub>PR</sub> demonstrated a favorable performance for peritoneal recurrence in all three cohorts. In addition, the TACS<sub>DFS</sub> also showed a satisfactory accuracy for disease-free survival among included patients. For stage II and III diseases, adjuvant chemotherapy improved the survival of patients with low TACS<sub>PR</sub> and low TACS<sub>DFS</sub>, or high TACS<sub>PR</sub> and low TACS<sub>DFS</sub>, or low TACS<sub>PR</sub> and high TACS<sub>DFS</sub>, but had no impact on patients with high TACS<sub>PR</sub> and high TACS<sub>DFS</sub>.</p><h3 data-test=\"abstract-sub-heading\">Conclusions</h3><p>The multitask machine learning model allows accurate prediction of peritoneal recurrence and survival for GC and could distinguish patients who might benefit from adjuvant chemotherapy.</p>","PeriodicalId":12684,"journal":{"name":"Gastric Cancer","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multitask machine learning-based tumor-associated collagen signatures predict peritoneal recurrence and disease-free survival in gastric cancer\",\"authors\":\"Meiting Fu, Yuyu Lin, Junyao Yang, Jiaxin Cheng, Liyan Lin, Guangxing Wang, Chenyan Long, Shuoyu Xu, Jianping Lu, Guoxin Li, Jun Yan, Gang Chen, Shuangmu Zhuo, Dexin Chen\",\"doi\":\"10.1007/s10120-024-01551-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3 data-test=\\\"abstract-sub-heading\\\">Background</h3><p>Accurate prediction of peritoneal recurrence for gastric cancer (GC) is crucial in clinic. The collagen alterations in tumor microenvironment affect the migration and treatment response of cancer cells. Herein, we proposed multitask machine learning-based tumor-associated collagen signatures (TACS), which are composed of quantitative collagen features derived from multiphoton imaging, to simultaneously predict peritoneal recurrence (TACS<sub>PR</sub>) and disease-free survival (TACS<sub>DFS</sub>).</p><h3 data-test=\\\"abstract-sub-heading\\\">Methods</h3><p>Among 713 consecutive patients, with 275 in training cohort, 222 patients in internal validation cohort, and 216 patients in external validation cohort, we developed and validated a multitask machine learning model for simultaneously predicting peritoneal recurrence (TACS<sub>PR</sub>) and disease-free survival (TACS<sub>DFS</sub>). The accuracy of the model for prediction of peritoneal recurrence and prognosis as well as its association with adjuvant chemotherapy were evaluated.</p><h3 data-test=\\\"abstract-sub-heading\\\">Results</h3><p>The TACS<sub>PR</sub> and TACS<sub>DFS</sub> were independently associated with peritoneal recurrence and disease-free survival in three cohorts, respectively (all <i>P</i> < 0.001). The TACS<sub>PR</sub> demonstrated a favorable performance for peritoneal recurrence in all three cohorts. In addition, the TACS<sub>DFS</sub> also showed a satisfactory accuracy for disease-free survival among included patients. For stage II and III diseases, adjuvant chemotherapy improved the survival of patients with low TACS<sub>PR</sub> and low TACS<sub>DFS</sub>, or high TACS<sub>PR</sub> and low TACS<sub>DFS</sub>, or low TACS<sub>PR</sub> and high TACS<sub>DFS</sub>, but had no impact on patients with high TACS<sub>PR</sub> and high TACS<sub>DFS</sub>.</p><h3 data-test=\\\"abstract-sub-heading\\\">Conclusions</h3><p>The multitask machine learning model allows accurate prediction of peritoneal recurrence and survival for GC and could distinguish patients who might benefit from adjuvant chemotherapy.</p>\",\"PeriodicalId\":12684,\"journal\":{\"name\":\"Gastric Cancer\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2024-09-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Gastric Cancer\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s10120-024-01551-0\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GASTROENTEROLOGY & HEPATOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Gastric Cancer","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s10120-024-01551-0","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GASTROENTEROLOGY & HEPATOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
背景准确预测胃癌(GC)腹膜复发在临床上至关重要。肿瘤微环境中的胶原蛋白改变会影响癌细胞的迁移和治疗反应。在此,我们提出了基于多任务机器学习的肿瘤相关胶原特征(TACS),该特征由多光子成像得到的定量胶原特征组成,可同时预测腹膜复发(TACSPR)和无病生存(TACSDFS)。方法在713例连续患者(其中275例为训练队列,222例为内部验证队列,216例为外部验证队列)中,我们开发并验证了同时预测腹膜复发(TACSPR)和无病生存(TACSDFS)的多任务机器学习模型。结果在三个队列中,TACSPR 和 TACSDFS 分别与腹膜复发和无病生存率独立相关(均为 P <0.001)。在所有三个队列中,TACSPR 在腹膜复发方面表现良好。此外,TACSDFS 对纳入患者的无病生存率也显示出令人满意的准确性。对于II期和III期疾病,辅助化疗提高了低TACSPR和低TACSDFS、或高TACSPR和低TACSDFS、或低TACSPR和高TACSDFS患者的生存率,但对高TACSPR和高TACSDFS患者没有影响。
Multitask machine learning-based tumor-associated collagen signatures predict peritoneal recurrence and disease-free survival in gastric cancer
Background
Accurate prediction of peritoneal recurrence for gastric cancer (GC) is crucial in clinic. The collagen alterations in tumor microenvironment affect the migration and treatment response of cancer cells. Herein, we proposed multitask machine learning-based tumor-associated collagen signatures (TACS), which are composed of quantitative collagen features derived from multiphoton imaging, to simultaneously predict peritoneal recurrence (TACSPR) and disease-free survival (TACSDFS).
Methods
Among 713 consecutive patients, with 275 in training cohort, 222 patients in internal validation cohort, and 216 patients in external validation cohort, we developed and validated a multitask machine learning model for simultaneously predicting peritoneal recurrence (TACSPR) and disease-free survival (TACSDFS). The accuracy of the model for prediction of peritoneal recurrence and prognosis as well as its association with adjuvant chemotherapy were evaluated.
Results
The TACSPR and TACSDFS were independently associated with peritoneal recurrence and disease-free survival in three cohorts, respectively (all P < 0.001). The TACSPR demonstrated a favorable performance for peritoneal recurrence in all three cohorts. In addition, the TACSDFS also showed a satisfactory accuracy for disease-free survival among included patients. For stage II and III diseases, adjuvant chemotherapy improved the survival of patients with low TACSPR and low TACSDFS, or high TACSPR and low TACSDFS, or low TACSPR and high TACSDFS, but had no impact on patients with high TACSPR and high TACSDFS.
Conclusions
The multitask machine learning model allows accurate prediction of peritoneal recurrence and survival for GC and could distinguish patients who might benefit from adjuvant chemotherapy.
期刊介绍:
Gastric Cancer is an esteemed global forum that focuses on various aspects of gastric cancer research, treatment, and biology worldwide.
The journal promotes a diverse range of content, including original articles, case reports, short communications, and technical notes. It also welcomes Letters to the Editor discussing published articles or sharing viewpoints on gastric cancer topics.
Review articles are predominantly sought after by the Editor, ensuring comprehensive coverage of the field.
With a dedicated and knowledgeable editorial team, the journal is committed to providing exceptional support and ensuring high levels of author satisfaction. In fact, over 90% of published authors have expressed their intent to publish again in our esteemed journal.