{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T19:01:49Z","timestamp":1785438109603,"version":"3.56.0"},"posted":{"date-parts":[[2026,6,24]]},"group-title":"Oncology","reference-count":23,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T00:00:00Z","timestamp":1782259200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.medrxiv.org\/about\/FAQ#license"}],"funder":[{"id":[{"id":"https:\/\/ror.org\/05wvpxv85","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,6,24]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>For patients with metastatic gastrointestinal cancers, chemotherapy resistance is a common phenomenon that, if known in advance, would allow for individualized treatment decisions. This study aimed to test the feasibility of developing a deep learning computer vision system that uses laparoscopy images depicting peritoneal surface metastases (i.e., capturing the in-vivo optical appearance of metastases as a summary of their molecular makeup) to predict whether a patient is resistant to standard chemotherapy.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>The retrospective observational feasibility study included 35 adult patients who underwent staging laparoscopy for non-colon gastrointestinal adenocarcinoma with biopsy-confirmed peritoneal surface metastases and who underwent chemotherapy as their only treatment modality. Chemotherapy resistance was determined based on each patient\u2019s observed cancer-specific survival after controlling for confounders.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Of 35 patients, 17 were assigned to the chemotherapy sensitive group and 18 to the chemotherapy resistant group. The study cohort provided 1010 laparoscopy image patches of 101 biopsy-confirmed metastases. A densely connected convolutional neural network with cross-validation provided the best results for correctly predicting chemotherapy resistance at the patient level (accuracy 0.80 (95%CI 0.63-0.92), sensitivity 0.72, specificity 0.88, AUC-ROC 0.78). Saliency maps demonstrated the system\u2019s trustworthiness.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>In this study, a prototype surgical computer vision system designed to determine chemotherapy resistance from operative images of peritoneal surface metastases demonstrated its technical feasibility. Further development and validation in a multi-institutional clinical study are pending.<\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2026.06.22.26356226","type":"posted-content","created":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T17:45:18Z","timestamp":1782323118000},"source":"Crossref","is-referenced-by-count":0,"title":["Predicting Chemotherapy Response from Staging Laparoscopy Images"],"prefix":"10.64898","author":[{"given":"Thomas","family":"Schnelldorfer","sequence":"first","affiliation":[{"name":"Surgical Imaging Lab, Tufts Medical Center, Boston, Massachusetts"},{"name":"Tufts Institute for Artificial Intelligence, Tufts University, Medford, Massachusetts"},{"name":"Department of Translational Research, Lahey Hospital and Medical Center, Burlington, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Janil","family":"Castro","sequence":"additional","affiliation":[{"name":"Surgical Imaging Lab, Tufts Medical Center, Boston, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atoussa","family":"Goldar-Najafi","sequence":"additional","affiliation":[{"name":"Department of Pathology, Lahey Hospital and Medical Center, Burlington, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francis W.","family":"Nugent","sequence":"additional","affiliation":[{"name":"Division of Hematology and Oncology, Tufts Medical Center, Boston, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bipin","family":"Gaikwad","sequence":"additional","affiliation":[{"name":"Surgical Imaging Lab, Tufts Medical Center, Boston, Massachusetts"},{"name":"Tufts Institute for Artificial Intelligence, Tufts University, Medford, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026062607550676000_2026.06.22.26356226v1.1","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-020-0628-z"},{"key":"2026062607550676000_2026.06.22.26356226v1.2","doi-asserted-by":"crossref","first-page":"3200","DOI":"10.3390\/cells11203200","article-title":"Biomarker-targeted therapies in non-small cell lung cancer: current status and perspectives","volume":"11","year":"2022","journal-title":"Cells"},{"key":"2026062607550676000_2026.06.22.26356226v1.3","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1007\/s10549-021-06310-8","article-title":"Prediction of pathologic complete response to neoadjuvant chemotherapy using machine learning models in patients with breast cancer","volume":"189","year":"2021","journal-title":"Breast Cancer Res Treat"},{"key":"2026062607550676000_2026.06.22.26356226v1.4","doi-asserted-by":"publisher","DOI":"10.1038\/s41563-021-01057-5"},{"key":"2026062607550676000_2026.06.22.26356226v1.5","doi-asserted-by":"publisher","DOI":"10.1111\/cas.14377"},{"key":"2026062607550676000_2026.06.22.26356226v1.6","doi-asserted-by":"crossref","unstructured":"Tran KA , Kondrashova O , Bradley A , et al. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med 2021;13:152.","DOI":"10.1186\/s13073-021-00968-x"},{"key":"2026062607550676000_2026.06.22.26356226v1.7","doi-asserted-by":"publisher","DOI":"10.1158\/2159-8290.CD-21-0090"},{"key":"2026062607550676000_2026.06.22.26356226v1.8","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1007\/s10278-020-00332-2","article-title":"Predicting the response to FOLFOX-based chemotherapy regimen from untreated liver metastases on baseline CT: a deep neural network approach","volume":"33","year":"2020","journal-title":"J Digit Imaging"},{"key":"2026062607550676000_2026.06.22.26356226v1.9","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1002\/mp.14563","article-title":"Deep learning-based radiomics predicts response to chemotherapy in colorectal liver metastases","volume":"48","year":"2021","journal-title":"Med Phys"},{"key":"2026062607550676000_2026.06.22.26356226v1.10","doi-asserted-by":"crossref","first-page":"2975","DOI":"10.1245\/s10434-020-09581-5","article-title":"Radiomics texture analysis for the identification of colorectal liver metastases sensitive to first-line oxaliplatin-based chemotherapy","volume":"28","year":"2021","journal-title":"Ann Surg Oncol"},{"key":"2026062607550676000_2026.06.22.26356226v1.11","doi-asserted-by":"crossref","first-page":"1535","DOI":"10.1002\/mp.15437","article-title":"Deep learning-based AI model for signet-ring cell carcinoma diagnosis and chemotherapy response prediction in gastric cancer","volume":"49","year":"2022","journal-title":"Med Phys"},{"key":"2026062607550676000_2026.06.22.26356226v1.12","doi-asserted-by":"crossref","first-page":"4624","DOI":"10.1245\/s10434-022-11471-x","article-title":"Prediction of recurrence pattern of pancreatic cancer post-pancreatic surgery using histology-based supervised machine learning algorithms: a single-center retrospective study","volume":"29","year":"2022","journal-title":"Ann Surg Oncol"},{"key":"2026062607550676000_2026.06.22.26356226v1.13","doi-asserted-by":"publisher","DOI":"10.1186\/s12943-026-02678-7"},{"key":"2026062607550676000_2026.06.22.26356226v1.14","doi-asserted-by":"publisher","DOI":"10.32604\/or.2026.075028"},{"key":"2026062607550676000_2026.06.22.26356226v1.15","doi-asserted-by":"crossref","unstructured":"Tsimberidou AM , Fountzilas E , Nikanjam M , Kurzrock R . Review of precision cancer medicine: Evolution of the treatment paradigm. Cancer Treat Rev 2020;86:102019.","DOI":"10.1016\/j.ctrv.2020.102019"},{"key":"2026062607550676000_2026.06.22.26356226v1.16","doi-asserted-by":"crossref","unstructured":"Fountzilas E , Tsimberidou AM , Vo HH , Kurzrock R . Clinical trial design in the era of precision medicine. Genome Med 2022;14:101.","DOI":"10.1186\/s13073-022-01102-1"},{"key":"2026062607550676000_2026.06.22.26356226v1.17","doi-asserted-by":"publisher","DOI":"10.1097\/SLA.0000000000006294"},{"key":"2026062607550676000_2026.06.22.26356226v1.18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"2026062607550676000_2026.06.22.26356226v1.19","doi-asserted-by":"crossref","unstructured":"Miko\u0142ajczyk A , Grochowski M . Data augmentation for improving deep learning in image classification problem. IEEE International Interdisciplinary PhD Workshop 2018:117\u2013122.","DOI":"10.1109\/IIPHDW.2018.8388338"},{"key":"2026062607550676000_2026.06.22.26356226v1.20","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2670313"},{"key":"2026062607550676000_2026.06.22.26356226v1.21","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2015.03.009"},{"key":"2026062607550676000_2026.06.22.26356226v1.22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"2026062607550676000_2026.06.22.26356226v1.23","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-98434-w"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.64898\/2026.06.22.26356226","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T14:55:20Z","timestamp":1782485720000},"score":1,"resource":{"primary":{"URL":"http:\/\/medrxiv.org\/lookup\/doi\/10.64898\/2026.06.22.26356226"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,24]]},"references-count":23,"URL":"https:\/\/doi.org\/10.64898\/2026.06.22.26356226","relation":{},"subject":[],"published":{"date-parts":[[2026,6,24]]},"subtype":"preprint"}}