dc.contributor.author | Lan, C | |
dc.contributor.author | Li, J | |
dc.contributor.author | Huang, X | |
dc.contributor.author | Heindl, A | |
dc.contributor.author | Wang, Y | |
dc.contributor.author | Yan, S | |
dc.contributor.author | Yuan, Y | |
dc.date.accessioned | 2020-06-11T12:18:02Z | |
dc.date.issued | 2019-02-18 | |
dc.identifier.citation | BMC cancer, 2019, 19 (1), pp. 159 - ? | |
dc.identifier.issn | 1471-2407 | |
dc.identifier.uri | https://repository.icr.ac.uk/handle/internal/3720 | |
dc.identifier.eissn | 1471-2407 | |
dc.identifier.doi | 10.1186/s12885-019-5343-8 | |
dc.description.abstract | BACKGROUND: Identifying high-risk patients for platinum resistance is critical for improving clinical management of ovarian cancer. We aimed to use automated image analysis of hematoxylin & eosin (H&E) stained sections to identify the association between microenvironmental composition and platinum-resistant recurrent ovarian cancer. METHODS: Ninety-one patients with ovarian cancer containing the data of automated image analysis for H&E histological sections were initially reviewed. RESULTS: Seventy-one patients with recurrent disease were finally identified. Among 30 patients with high stromal cell ratio, 60% of the patients had platinum-resistant recurrence, which was significantly higher than the rate in patients with low stromal cell ratio (9.80%, P < 0.001). Multivariate logistic regression analysis revealed elevated CA125 level after 3 cycles of chemotherapy (P < 0.001) and high stromal cell ratio (P = 0.002) were the negative predictors of platinum-resistant relapse. The area under the curve (AUC) of receiver operating characteristic (ROC) curves of the models for predicting platinum-resistant recurrence with stromal cell ratio, normalization of CA125 level, and the combination of two parameters were 0.78, 0.79, and 0.89 respectively. CONCLUSIONS: Our results demonstrated stromal cell ratio based on automated image analysis may be a potential predictor for ovarian cancer patients at high risk of platinum-resistant recurrence, and it could improve the predictive value of model when combined with normalization of CA125 level after 3 cycles of chemotherapy. | |
dc.format | Electronic | |
dc.format.extent | 159 - ? | |
dc.language | eng | |
dc.language.iso | eng | |
dc.publisher | BMC | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0 | |
dc.subject | Stromal Cells | |
dc.subject | Humans | |
dc.subject | Ovarian Neoplasms | |
dc.subject | Neoplasm Recurrence, Local | |
dc.subject | Platinum | |
dc.subject | Membrane Proteins | |
dc.subject | CA-125 Antigen | |
dc.subject | Drug Therapy | |
dc.subject | Logistic Models | |
dc.subject | Odds Ratio | |
dc.subject | Chi-Square Distribution | |
dc.subject | Drug Resistance, Neoplasm | |
dc.subject | Image Processing, Computer-Assisted | |
dc.subject | Aged | |
dc.subject | Female | |
dc.subject | Tumor Microenvironment | |
dc.subject | Biomarkers, Tumor | |
dc.title | Stromal cell ratio based on automated image analysis as a predictor for platinum-resistant recurrent ovarian cancer. | |
dc.type | Journal Article | |
dcterms.dateAccepted | 2019-02-01 | |
rioxxterms.versionofrecord | 10.1186/s12885-019-5343-8 | |
rioxxterms.licenseref.uri | https://creativecommons.org/licenses/by/4.0 | |
rioxxterms.licenseref.startdate | 2019-02-18 | |
rioxxterms.type | Journal Article/Review | |
dc.relation.isPartOf | BMC cancer | |
pubs.issue | 1 | |
pubs.notes | Not known | |
pubs.organisational-group | /ICR | |
pubs.organisational-group | /ICR/Primary Group | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Molecular Pathology | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Molecular Pathology/Computational Pathology & Integrated Genomics | |
pubs.organisational-group | /ICR | |
pubs.organisational-group | /ICR/Primary Group | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Molecular Pathology | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Molecular Pathology/Computational Pathology & Integrated Genomics | |
pubs.publication-status | Published | |
pubs.volume | 19 | |
pubs.embargo.terms | Not known | |
icr.researchteam | Computational Pathology & Integrated Genomics | |
dc.contributor.icrauthor | Yuan, Yinyin | |