Predictive value of changes in the serum CA-125 levels in patients undergoing interval debulking surgery after neoadjuvant chemotherapy in advanced epithelial ovarian carcinoma

Batra, Sonia; Arora, Ruchi; Dave, Kalpana

doi:10.18203/2320-1770.ijrcog20190272

Cited by 3 publications

(2 citation statements)

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“…So far, predictive studies have focused on individual data streams, such as clinical features 9 , 10 , CA-125 11 – 13 , computed tomography (CT) imaging 14 , 15 , and circulating tumour DNA (ctDNA) 16 . The superior predictive power of integrative models for complex endpoints is well demonstrated in several cancer types 17 – 19 , but training such models requires large, well-annotated, multi-omic datasets, which have not been available for NACT treatment of HGSOC.…”

Section: Introductionmentioning

confidence: 99%

Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer

Crispin-Ortuzar,

Woitek,

Reinius

et al. 2023

Nat Commun

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High grade serous ovarian carcinoma (HGSOC) is a highly heterogeneous disease that typically presents at an advanced, metastatic state. The multi-scale complexity of HGSOC is a major obstacle to predicting response to neoadjuvant chemotherapy (NACT) and understanding critical determinants of response. Here we present a framework to predict the response of HGSOC patients to NACT integrating baseline clinical, blood-based, and radiomic biomarkers extracted from all primary and metastatic lesions. We use an ensemble machine learning model trained to predict the change in total disease volume using data obtained at diagnosis (n = 72). The model is validated in an internal hold-out cohort (n = 20) and an independent external patient cohort (n = 42). In the external cohort the integrated radiomics model reduces the prediction error by 8% with respect to the clinical model, achieving an AUC of 0.78 for RECIST 1.1 classification compared to 0.47 for the clinical model. Our results emphasize the value of including radiomics data in integrative models of treatment response and provide methods for developing new biomarker-based clinical trials of NACT in HGSOC.

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Section: Introductionmentioning

confidence: 99%

Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer

Crispin-Ortuzar,

Woitek,

Reinius

et al. 2023

Nat Commun

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“…The second major challenge is predicting response. Studies so far have focused on single data streams which can only offer a partial view of the disease, such as clinical features (14,15), CA-125 (16)(17)(18), computed tomography (CT) imaging (19,20), and circulating tumour DNA (ctDNA) (21). The superior predictive power of integrative models for complex endpoints is D R A F T well documented in several cancer types (22)(23)(24), but training such models requires large, well-annotated, multi-omic datasets, which have not been available in HGSOC.…”

Section: Introductionmentioning

confidence: 99%

Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer

Crispín-Ortuzar

Woitek

Moore³

et al. 2021

Preprint

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High grade serous ovarian cancer (HGSOC) is a highly heterogeneous disease that often presents at an advanced, metastatic state. The multi-scale complexity of HGSOC is a major obstacle to measuring response to neoadjuvant chemotherapy (NACT) and understanding its determinants. Here we propose a radiogenomic framework integrating clinical, radiomic, and blood-based biomarkers to measure and predict the response of HGSOC patients to NACT, showing how quantitative imaging data can serve as the backbone of multi-scale data integration. We developed and validated our approach in two independent highly-annotated multi-omic multi-lesion data sets. In a discovery cohort (n=72) we found that different tumour sites present distinct response patterns, and identified volumetric response assessment as a better predictor of overall survival (OS) than RECIST~1.1 status. We trained an ensemble machine learning approach to predict tumour volume response to NACT from data obtained prior to treatment, and validated the model in an internal hold-out cohort (n=20) and an independent external patient cohort (n=42). Benchmarking integrated models against models built on single data types highlighted the importance of comprehensive patient characterisation. Our study sets the foundation for developing new clinical trials of NACT in HGSOC.

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Histopathological prognostic factors in post NACT ovarian cancers: A retrospective study

Kaur,

Sethi,

Sharma

et al. 2023

IJPO

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Epithelial ovarian carcinoma is the most common type of all the ovarian cancers. The patients with advanced stage are initially treated with neo-adjuvant chemotherapy followed by interval debulking surgery. The high mortality rate is mainly due to advanced stage disease at initial presentation. This a retrospective study carried out in department of pathology at Bhagwan Mahaveer cancer hospital, Jaipur. The retrospective cases data was collected and analyzed from patient records on basis of inclusion and exclusion criteria.: Patients of advanced ovarian cancer with fibrosis grade 3, necrosis grade 2, presence of psammoma bodies, presence of collagen deposition, low Ki67 index, positive ER status were associated with longer DFS (p value= 0.014,0.029,0.033,0.028,0.001 and 0.001 respectively) and OS (P value 0.025,0.005,0.002,0.015,0.001 and 0.001 respectively).We propose that the prognostic histopathological parameters analysed in our study in post NACT patients of ovarian carcinoma should be reported in final histopathological report, as these factors can provide an extra tool for clinicians to optimize patient management and care.

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Predictive value of changes in the serum CA-125 levels in patients undergoing interval debulking surgery after neoadjuvant chemotherapy in advanced epithelial ovarian carcinoma

Cited by 3 publications

References 0 publications

Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer

Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer

Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer

Histopathological prognostic factors in post NACT ovarian cancers: A retrospective study

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