{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T11:24:33Z","timestamp":1761218673983,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T00:00:00Z","timestamp":1719964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["Subject 8620 of Grant 5P20GM135009","HR23-122"],"award-info":[{"award-number":["Subject 8620 of Grant 5P20GM135009","HR23-122"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Oklahoma Center for Advancement of Science &amp; Technology (OCAST)","award":["Subject 8620 of Grant 5P20GM135009","HR23-122"],"award-info":[{"award-number":["Subject 8620 of Grant 5P20GM135009","HR23-122"]}]},{"name":"University of Oklahoma Libraries\u2019 Open Access Fund","award":["Subject 8620 of Grant 5P20GM135009","HR23-122"],"award-info":[{"award-number":["Subject 8620 of Grant 5P20GM135009","HR23-122"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>The purpose of this investigation is to develop and initially assess a quantitative image analysis scheme that utilizes histopathological images to predict the treatment effectiveness of bevacizumab therapy in ovarian cancer patients. As a widely accessible diagnostic tool, histopathological slides contain copious information regarding underlying tumor progression that is associated with tumor prognosis. However, this information cannot be readily identified by conventional visual examination. This study utilizes novel pathomics technology to quantify this meaningful information for treatment effectiveness prediction. Accordingly, a total of 9828 features were extracted from segmented tumor tissue, cell nuclei, and cell cytoplasm, which were categorized into geometric, intensity, texture, and subcellular structure features. Next, the best performing features were selected as the input for SVM (support vector machine)-based prediction models. These models were evaluated on an open dataset containing a total of 78 patients and 288 whole slides images. The results indicated that the sufficiently optimized, best-performing model yielded an area under the receiver operating characteristic (ROC) curve of 0.8312. When examining the best model\u2019s confusion matrix, 37 and 25 cases were correctly predicted as responders and non-responders, respectively, achieving an overall accuracy of 0.7848. This investigation initially validated the feasibility of utilizing pathomics techniques to predict tumor responses to chemotherapy at an early stage.<\/jats:p>","DOI":"10.3390\/bioengineering11070678","type":"journal-article","created":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T09:23:59Z","timestamp":1719998639000},"page":"678","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Utilizing a Pathomics Biomarker to Predict the Effectiveness of Bevacizumab in Ovarian Cancer Treatment"],"prefix":"10.3390","volume":"11","author":[{"given":"Patrik","family":"Gilley","sequence":"first","affiliation":[{"name":"School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3194-2546","authenticated-orcid":false,"given":"Ke","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA"},{"name":"Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4959-2652","authenticated-orcid":false,"given":"Neman","family":"Abdoli","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youkabed","family":"Sadri","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8419-4022","authenticated-orcid":false,"given":"Laura","family":"Adhikari","sequence":"additional","affiliation":[{"name":"Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kar-Ming","family":"Fung","sequence":"additional","affiliation":[{"name":"Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6047-7204","authenticated-orcid":false,"given":"Yuchen","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"17","DOI":"10.3322\/caac.21763","article-title":"Cancer statistics, 2023","volume":"73","author":"Siegel","year":"2023","journal-title":"CA Cancer J. 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