Chiara Maria Lavinia Loeffler, Hideaki Bando, Srividhya Sainath, Hannah Sophie Muti, Xiaofeng Jiang, Marko van Treeck, Nic Gabriel Reitsam, Zunamys I. Carrero, Tomomi Nishikawa, Toshihiro Misumi, Saori Mishima, Daisuke Kotani, Hiroya Taniguchi, Ichiro Takemasa, Takeshi Kato, Eiji Oki, Tanwei Yuan, Durgesh Wankhede, Sebastian Foersch, Hermann Brenner, Michael Hoffmeister, Yoshiaki Nakamura, Takayuki Yoshino, Jakob Nikolas Kather
{"title":"HIBRID:基于组织学和 ct-DNA 深度学习的风险分级","authors":"Chiara Maria Lavinia Loeffler, Hideaki Bando, Srividhya Sainath, Hannah Sophie Muti, Xiaofeng Jiang, Marko van Treeck, Nic Gabriel Reitsam, Zunamys I. Carrero, Tomomi Nishikawa, Toshihiro Misumi, Saori Mishima, Daisuke Kotani, Hiroya Taniguchi, Ichiro Takemasa, Takeshi Kato, Eiji Oki, Tanwei Yuan, Durgesh Wankhede, Sebastian Foersch, Hermann Brenner, Michael Hoffmeister, Yoshiaki Nakamura, Takayuki Yoshino, Jakob Nikolas Kather","doi":"10.1101/2024.07.23.24310822","DOIUrl":null,"url":null,"abstract":"Background: Although surgical resection is the standard therapy for stage II/III colorectal cancer (CRC), recurrence rates exceed 30%. Circulating tumor DNA (ctDNA) emerged as a promising recurrence predictor, detecting molecular residual disease (MRD). However, spatial information about the tumor and its microenvironment is not directly measured by ctDNA. Deep Learning (DL) can predict prognosis directly from routine histopathology slides. Methods: We developed a DL pipeline utilizing vision transformers to predict disease-free survival (DFS) based on histological hematoxylin & eosin (H&E) stained whole slide images (WSIs) from patients with resectable stage II-IV CRC. This model was trained on the DACHS cohort (n=1766) and independently validated on the GALAXY cohort (n=1555). Patients were categorized into high- or low-risk groups based on the DL-prediction scores. In the GALAXY cohort, the DL-scores were combined with the four-weeks post-surgery MRD status measured by ctDNA for prognostic stratification. Results: In GALAXY, the DL-model categorized 307 patients as DL high-risk and 1248 patients as DL low-risk (p<0.001; HR 2.60, CI 95% 2.11-3.21). Combining the DL scores with the MRD status significantly stratified both the MRD-positive group into DL high-risk (n=81) and DL low-risk (n=160) (HR 1.58 (CI 95% 1.17-2.11; p=0.002) and the MRD-negative group into DL high-risk (n=226) and DL low-risk (n=1088) (HR 2.37 CI 95% 1.73-3.23; p<0.001). Moreover, MRD-negative patients had significantly longer DFS when predicted as DL high-risk and treated with ACT (HR 0.48, CI 95% 0.27-0.86; p= 0.01), compared to the MRD-negative patients predicted as DL low-risk (HR=1.14, CI 95% 0.8-1.63; p=0.48). Conclusion: DL-based spatial assessment of tumor histopathology slides significantly improves the risk stratification provided by MRD alone. Combining histologic information with ctDNA yields the most powerful predictor for disease recurrence to date, with the potential to improve follow-up, withhold adjuvant chemotherapy in low-risk patients and escalate adjuvant chemotherapy in high-risk patients.","PeriodicalId":501258,"journal":{"name":"medRxiv - Gastroenterology","volume":"166 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"HIBRID: Histology and ct-DNA based Risk-stratification with Deep Learning\",\"authors\":\"Chiara Maria Lavinia Loeffler, Hideaki Bando, Srividhya Sainath, Hannah Sophie Muti, Xiaofeng Jiang, Marko van Treeck, Nic Gabriel Reitsam, Zunamys I. Carrero, Tomomi Nishikawa, Toshihiro Misumi, Saori Mishima, Daisuke Kotani, Hiroya Taniguchi, Ichiro Takemasa, Takeshi Kato, Eiji Oki, Tanwei Yuan, Durgesh Wankhede, Sebastian Foersch, Hermann Brenner, Michael Hoffmeister, Yoshiaki Nakamura, Takayuki Yoshino, Jakob Nikolas Kather\",\"doi\":\"10.1101/2024.07.23.24310822\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Background: Although surgical resection is the standard therapy for stage II/III colorectal cancer (CRC), recurrence rates exceed 30%. Circulating tumor DNA (ctDNA) emerged as a promising recurrence predictor, detecting molecular residual disease (MRD). However, spatial information about the tumor and its microenvironment is not directly measured by ctDNA. Deep Learning (DL) can predict prognosis directly from routine histopathology slides. Methods: We developed a DL pipeline utilizing vision transformers to predict disease-free survival (DFS) based on histological hematoxylin & eosin (H&E) stained whole slide images (WSIs) from patients with resectable stage II-IV CRC. This model was trained on the DACHS cohort (n=1766) and independently validated on the GALAXY cohort (n=1555). Patients were categorized into high- or low-risk groups based on the DL-prediction scores. In the GALAXY cohort, the DL-scores were combined with the four-weeks post-surgery MRD status measured by ctDNA for prognostic stratification. Results: In GALAXY, the DL-model categorized 307 patients as DL high-risk and 1248 patients as DL low-risk (p<0.001; HR 2.60, CI 95% 2.11-3.21). Combining the DL scores with the MRD status significantly stratified both the MRD-positive group into DL high-risk (n=81) and DL low-risk (n=160) (HR 1.58 (CI 95% 1.17-2.11; p=0.002) and the MRD-negative group into DL high-risk (n=226) and DL low-risk (n=1088) (HR 2.37 CI 95% 1.73-3.23; p<0.001). Moreover, MRD-negative patients had significantly longer DFS when predicted as DL high-risk and treated with ACT (HR 0.48, CI 95% 0.27-0.86; p= 0.01), compared to the MRD-negative patients predicted as DL low-risk (HR=1.14, CI 95% 0.8-1.63; p=0.48). Conclusion: DL-based spatial assessment of tumor histopathology slides significantly improves the risk stratification provided by MRD alone. Combining histologic information with ctDNA yields the most powerful predictor for disease recurrence to date, with the potential to improve follow-up, withhold adjuvant chemotherapy in low-risk patients and escalate adjuvant chemotherapy in high-risk patients.\",\"PeriodicalId\":501258,\"journal\":{\"name\":\"medRxiv - Gastroenterology\",\"volume\":\"166 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"medRxiv - Gastroenterology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1101/2024.07.23.24310822\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"medRxiv - Gastroenterology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2024.07.23.24310822","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
HIBRID: Histology and ct-DNA based Risk-stratification with Deep Learning
Background: Although surgical resection is the standard therapy for stage II/III colorectal cancer (CRC), recurrence rates exceed 30%. Circulating tumor DNA (ctDNA) emerged as a promising recurrence predictor, detecting molecular residual disease (MRD). However, spatial information about the tumor and its microenvironment is not directly measured by ctDNA. Deep Learning (DL) can predict prognosis directly from routine histopathology slides. Methods: We developed a DL pipeline utilizing vision transformers to predict disease-free survival (DFS) based on histological hematoxylin & eosin (H&E) stained whole slide images (WSIs) from patients with resectable stage II-IV CRC. This model was trained on the DACHS cohort (n=1766) and independently validated on the GALAXY cohort (n=1555). Patients were categorized into high- or low-risk groups based on the DL-prediction scores. In the GALAXY cohort, the DL-scores were combined with the four-weeks post-surgery MRD status measured by ctDNA for prognostic stratification. Results: In GALAXY, the DL-model categorized 307 patients as DL high-risk and 1248 patients as DL low-risk (p<0.001; HR 2.60, CI 95% 2.11-3.21). Combining the DL scores with the MRD status significantly stratified both the MRD-positive group into DL high-risk (n=81) and DL low-risk (n=160) (HR 1.58 (CI 95% 1.17-2.11; p=0.002) and the MRD-negative group into DL high-risk (n=226) and DL low-risk (n=1088) (HR 2.37 CI 95% 1.73-3.23; p<0.001). Moreover, MRD-negative patients had significantly longer DFS when predicted as DL high-risk and treated with ACT (HR 0.48, CI 95% 0.27-0.86; p= 0.01), compared to the MRD-negative patients predicted as DL low-risk (HR=1.14, CI 95% 0.8-1.63; p=0.48). Conclusion: DL-based spatial assessment of tumor histopathology slides significantly improves the risk stratification provided by MRD alone. Combining histologic information with ctDNA yields the most powerful predictor for disease recurrence to date, with the potential to improve follow-up, withhold adjuvant chemotherapy in low-risk patients and escalate adjuvant chemotherapy in high-risk patients.