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Histopathologic image–based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer
by
Kim, Milim
, Lee, Jung-Yun
, Lee, Yong-Moon
, Moon, Damin
, Kim, Ji Min
, Nam, Eun Ji
, Kwon, Dohee
, Lee, Yangkyu
, Park, Hyunjin
, Won, Dongju
, Jung, Hye Ra
, Lee, Chung
, Shin, Su-Jin
, Cho, Nam Hoon
, Park, Heejung
, Choi, Sung-Eun
, Sung, Sun Hee
, An, Hee Jung
, Noh, Songmi
, Park, Eunhyang
, Kim, Hyun-Soo
, Ahn, Byungsoo
, Choi, Heung-Kook
, Kwon, Sun Young
, Cha, Yoon Jin
, Kim, Dongmin
in
38/91
/ 631/67/1517/1709
/ 631/67/2321
/ 692/4028/67/1517/1709
/ 692/53/2423
/ Adult
/ Aged
/ Biomarkers
/ Biomarkers, Tumor - genetics
/ Biomarkers, Tumor - metabolism
/ Cancer
/ Chemotherapy
/ Classifiers
/ Cohort Studies
/ Cystadenocarcinoma, Serous - diagnostic imaging
/ Cystadenocarcinoma, Serous - drug therapy
/ Cystadenocarcinoma, Serous - genetics
/ Cystadenocarcinoma, Serous - pathology
/ Decisions
/ Deep Learning
/ Female
/ Histopathology
/ Humanities and Social Sciences
/ Humans
/ Medical imaging
/ Middle Aged
/ multidisciplinary
/ Neoplasm Grading
/ Ovarian cancer
/ Ovarian carcinoma
/ Ovarian Neoplasms - diagnostic imaging
/ Ovarian Neoplasms - drug therapy
/ Ovarian Neoplasms - genetics
/ Ovarian Neoplasms - pathology
/ Performance prediction
/ Platinum
/ Platinum - therapeutic use
/ Reproducibility of Results
/ Risk
/ Science
/ Science (multidisciplinary)
/ Transcriptomics
/ Treatment Outcome
2024
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Histopathologic image–based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer
by
Kim, Milim
, Lee, Jung-Yun
, Lee, Yong-Moon
, Moon, Damin
, Kim, Ji Min
, Nam, Eun Ji
, Kwon, Dohee
, Lee, Yangkyu
, Park, Hyunjin
, Won, Dongju
, Jung, Hye Ra
, Lee, Chung
, Shin, Su-Jin
, Cho, Nam Hoon
, Park, Heejung
, Choi, Sung-Eun
, Sung, Sun Hee
, An, Hee Jung
, Noh, Songmi
, Park, Eunhyang
, Kim, Hyun-Soo
, Ahn, Byungsoo
, Choi, Heung-Kook
, Kwon, Sun Young
, Cha, Yoon Jin
, Kim, Dongmin
in
38/91
/ 631/67/1517/1709
/ 631/67/2321
/ 692/4028/67/1517/1709
/ 692/53/2423
/ Adult
/ Aged
/ Biomarkers
/ Biomarkers, Tumor - genetics
/ Biomarkers, Tumor - metabolism
/ Cancer
/ Chemotherapy
/ Classifiers
/ Cohort Studies
/ Cystadenocarcinoma, Serous - diagnostic imaging
/ Cystadenocarcinoma, Serous - drug therapy
/ Cystadenocarcinoma, Serous - genetics
/ Cystadenocarcinoma, Serous - pathology
/ Decisions
/ Deep Learning
/ Female
/ Histopathology
/ Humanities and Social Sciences
/ Humans
/ Medical imaging
/ Middle Aged
/ multidisciplinary
/ Neoplasm Grading
/ Ovarian cancer
/ Ovarian carcinoma
/ Ovarian Neoplasms - diagnostic imaging
/ Ovarian Neoplasms - drug therapy
/ Ovarian Neoplasms - genetics
/ Ovarian Neoplasms - pathology
/ Performance prediction
/ Platinum
/ Platinum - therapeutic use
/ Reproducibility of Results
/ Risk
/ Science
/ Science (multidisciplinary)
/ Transcriptomics
/ Treatment Outcome
2024
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Histopathologic image–based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer
by
Kim, Milim
, Lee, Jung-Yun
, Lee, Yong-Moon
, Moon, Damin
, Kim, Ji Min
, Nam, Eun Ji
, Kwon, Dohee
, Lee, Yangkyu
, Park, Hyunjin
, Won, Dongju
, Jung, Hye Ra
, Lee, Chung
, Shin, Su-Jin
, Cho, Nam Hoon
, Park, Heejung
, Choi, Sung-Eun
, Sung, Sun Hee
, An, Hee Jung
, Noh, Songmi
, Park, Eunhyang
, Kim, Hyun-Soo
, Ahn, Byungsoo
, Choi, Heung-Kook
, Kwon, Sun Young
, Cha, Yoon Jin
, Kim, Dongmin
in
38/91
/ 631/67/1517/1709
/ 631/67/2321
/ 692/4028/67/1517/1709
/ 692/53/2423
/ Adult
/ Aged
/ Biomarkers
/ Biomarkers, Tumor - genetics
/ Biomarkers, Tumor - metabolism
/ Cancer
/ Chemotherapy
/ Classifiers
/ Cohort Studies
/ Cystadenocarcinoma, Serous - diagnostic imaging
/ Cystadenocarcinoma, Serous - drug therapy
/ Cystadenocarcinoma, Serous - genetics
/ Cystadenocarcinoma, Serous - pathology
/ Decisions
/ Deep Learning
/ Female
/ Histopathology
/ Humanities and Social Sciences
/ Humans
/ Medical imaging
/ Middle Aged
/ multidisciplinary
/ Neoplasm Grading
/ Ovarian cancer
/ Ovarian carcinoma
/ Ovarian Neoplasms - diagnostic imaging
/ Ovarian Neoplasms - drug therapy
/ Ovarian Neoplasms - genetics
/ Ovarian Neoplasms - pathology
/ Performance prediction
/ Platinum
/ Platinum - therapeutic use
/ Reproducibility of Results
/ Risk
/ Science
/ Science (multidisciplinary)
/ Transcriptomics
/ Treatment Outcome
2024
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Histopathologic image–based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer
Journal Article
Histopathologic image–based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer
2024
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Overview
Platinum-based chemotherapy is the cornerstone treatment for female high-grade serous ovarian carcinoma (HGSOC), but choosing an appropriate treatment for patients hinges on their responsiveness to it. Currently, no available biomarkers can promptly predict responses to platinum-based treatment. Therefore, we developed the Pathologic Risk Classifier for HGSOC (PathoRiCH), a histopathologic image–based classifier. PathoRiCH was trained on an in-house cohort (
n
= 394) and validated on two independent external cohorts (
n
= 284 and
n
= 136). The PathoRiCH-predicted favorable and poor response groups show significantly different platinum-free intervals in all three cohorts. Combining PathoRiCH with molecular biomarkers provides an even more powerful tool for the risk stratification of patients. The decisions of PathoRiCH are explained through visualization and a transcriptomic analysis, which bolster the reliability of our model’s decisions. PathoRiCH exhibits better predictive performance than current molecular biomarkers. PathoRiCH will provide a solid foundation for developing an innovative tool to transform the current diagnostic pipeline for HGSOC.
Predicting the response to platinum-based chemotherapy in high-grade serous ovarian carcinoma (HGSOC) remains challenging. Here, the authors develop the histopathology image-based Pathologic Risk Classifier for HGSOC - PathoRiCH - to predict and stratify HGSOC patient response to therapy, especially when combined with molecular biomarkers.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
/ Adult
/ Aged
/ Biomarkers, Tumor - genetics
/ Biomarkers, Tumor - metabolism
/ Cancer
/ Cystadenocarcinoma, Serous - diagnostic imaging
/ Cystadenocarcinoma, Serous - drug therapy
/ Cystadenocarcinoma, Serous - genetics
/ Cystadenocarcinoma, Serous - pathology
/ Female
/ Humanities and Social Sciences
/ Humans
/ Ovarian Neoplasms - diagnostic imaging
/ Ovarian Neoplasms - drug therapy
/ Ovarian Neoplasms - genetics
/ Ovarian Neoplasms - pathology
/ Platinum
/ Risk
/ Science
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