CT-Based Radiomics and Deep Learning for BRCA Mutation and Progression-Free Survival Prediction in Ovarian Cancer Using a Multicentric Dataset

Avesani, Giacomo; Tran, Huong Elena; Cammarata, Giulio; Botta, Francesca; Raimondi, Sara; Russo, Leila; Persiani, Salvatore; Bonatti, Matteo; Tagliaferri, Tiziana; Dolciami, Miriam; Celli, Veronica; Boldrini, Luca; Lenkowicz, Jacopo; Pricolo, Paola; Tomao, Federica; Rizzo, Stefania; Colombo, Nicoletta; Manganaro, Lucia; Fagotti, Anna; Scambia, Giovanni; Gui, Benedetta; Manfredi, Roberto

doi:10.3390/cancers14112739

Cited by 29 publications

(27 citation statements)

References 45 publications

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“…The results demonstrated that combining clinical and radiomics models improved model performance. This finding was consistent with the research of Avesani [ 39 ], who built predictive radiomics models for early relapse and BRCA mutation based on a multicentric database of OC. Their models showed low performance in predicting both BRCA mutation and 1-year relapse with traditional radiomics (AUC: 0.46-0.59 for BRCA and 0.46-0.56 for relapse) and deep learning (AUC of 0.48 BRCA and 0.50 for relapse).…”

Section: Discussionsupporting

confidence: 91%

CT-based machine learning radiomics predicts CCR5 expression level and survival in ovarian cancer

et al. 2023

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Objective We aimed to evaluate the prognostic value of C-C motif chemokine receptor type 5 (CCR5) expression level for patients with ovarian cancer and to establish a radiomics model that can predict CCR5 expression level using The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) database. Methods A total of 343 cases of ovarian cancer from the TCGA were used for the gene-based prognostic analysis. Fifty seven cases had preoperative computed tomography (CT) images stored in TCIA with genomic data in TCGA were used for radiomics feature extraction and model construction. 89 cases with both TCGA and TCIA clinical data were used for radiomics model evaluation. After feature extraction, a radiomics signature was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis. A prognostic scoring system incorporating radiomics signature based on CCR5 expression level and clinicopathologic risk factors was proposed for survival prediction. Results CCR5 was identified as a differentially expressed prognosis-related gene in tumor and normal sample, which were involved in the regulation of immune response and tumor invasion and metastasis. Four optimal radiomics features were selected to predict overall survival. The performance of the radiomics model for predicting the CCR5 expression level with 10-fold cross- validation achieved Area Under Curve (AUCs) of 0.770 and of 0.726, respectively, in the training and validation sets. A predictive nomogram was generated based on the total risk score of each patient, the AUCs of the time-dependent receiver operating characteristic (ROC) curve of the model was 0.8, 0.673 and 0.792 for 1-year, 3-year and 5-year, respectively. Along with clinical features, important imaging biomarkers could improve the overall survival accuracy of the prediction model. Conclusion The expression levels of CCR5 can affect the prognosis of patients with ovarian cancer. CT-based radiomics could serve as a new tool for prognosis prediction.

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Section: Discussionsupporting

confidence: 91%

CT-based machine learning radiomics predicts CCR5 expression level and survival in ovarian cancer

et al. 2023

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“…Despite not distinguishing between subtypes, the radiomics model developed by Liu et al achieved an AUC of 0.82 in predicting BRCA gene mutation status in EOC (16). Another multicenter study applied radiomics models that included first order statistical features, shape features, and textural features based on machine-learning algorithms to assess the BRCA mutation status of HGSOC, but the models did not include filter features and all of them presented a low performance (21). However, the subsequently added clinical indicator (CA125 level) brought improvements to the performance of the models in assessing BRCA mutation (AUC = 0.74).…”

Section: Discussionmentioning

confidence: 99%

CT-based radiomics nomogram analysis for assessing BRCA mutation status in patients with high-grade serous ovarian cancer

et al. 2023

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Background Radiomics nomogram analysis is widely preoperatively used to assess gene mutations in various tumors. Purpose To explore the value of computed tomography (CT)-based radiomics nomogram analysis for assessing BRCA gene mutation status of patients with high-grade serous ovarian cancer (HGSOC). Material and Methods In total, 96 patients with HGSOC were retrospectively screened and randomly divided into primary (n = 68) and validation cohorts (n = 28). The clinical model was constructed based on clinical features and CT morphological features using univariate and multivariate logistic analyses. Maximum-relevance and minimum-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) were performed for feature dimensionality reduction and radiomics score was calculated. The nomogram model combining the clinical model and the radiomics score was constructed using multivariate logistic regression. Receiver operating characteristic (ROC) curves were generated to assess models’ performance. The calibration analysis and decision curve analysis (DCA) were also performed. Results The clinical model consisted of CA125 level and supradiaphragmatic lymphadenopathy and yielded an area under the curve (AUC) of 0.69 (primary cohort) and 0.81 (validation cohort). The radiomics model was built with seven selected features and showed an AUC of 0.87 (primary cohort) and 0.81 (validation cohort). The nomogram finally showed the highest AUC of 0.89 (primary cohort) and 0.87 (validation cohort). The nomogram presented favorable calibrations in both the primary and validation cohorts. DCA further confirmed the clinical benefits of the constructed nomogram. Conclusion CT-based radiomics nomogram provides a non-invasive method to discriminate BRCA gene mutation status of HGSOC and potentially helps develop precise medical strategies.

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“…Several studies so far investigated the correlation of radiomics feature and gene expression profile in various malignancies with a main focus on Non-Small Cell Lung Cancer (NSCLC) [ 43 , 44 , 45 ]. For example, Zhang et al [ 44 ] constructed a clinical-radiological-radiomics model (C-R-R), based on the combination of CT radiomics feature signature with clinical and radiological features, aiming to predict epidermal growth factor receptor (EGFR) status among 420 patients with lung adenocarcinoma.…”

Section: Discussionmentioning

confidence: 99%

CT Radiomics and Whole Genome Sequencing in Patients with Pancreatic Ductal Adenocarcinoma: Predictive Radiogenomics Modeling

Hinzpeter

Kulanthaivelu

Kohan

et al. 2022

Cancers

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We investigate whether computed tomography (CT) derived radiomics may correlate with driver gene mutations in patients with pancreatic ductal adenocarcinoma (PDAC). In this retrospective study, 47 patients (mean age 64 ± 11 years; range: 42–86 years) with PDAC, who were treated surgically and who underwent preoperative CT imaging at our institution were included in the study. Image segmentation and feature extraction was performed semi-automatically with a commonly used open-source software platform. Genomic data from whole genome sequencing (WGS) were collected from our institution’s web-based resource. Two statistical models were then built, in order to evaluate the predictive ability of CT-derived radiomics feature for driver gene mutations in PDAC. 30/47 of all tumor samples harbored 2 or more gene mutations. Overall, 81% of tumor samples demonstrated mutations in KRAS, 68% of samples had alterations in TP53, 26% in SMAD4 and 19% in CDKN2A. Extended statistical analysis revealed acceptable predictive ability for KRAS and TP53 (Youden Index 0.56 and 0.67, respectively) and mild to acceptable predictive signal for SMAD4 and CDKN2A (Youden Index 0.5, respectively). Our study establishes acceptable correlation of radiomics features and driver gene mutations in PDAC, indicating an acceptable prognostication of genomic profiles using CT-derived radiomics. A larger and more homogenous cohort may further enhance the predictive ability.

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CT-Based Radiomics and Deep Learning for BRCA Mutation and Progression-Free Survival Prediction in Ovarian Cancer Using a Multicentric Dataset

Cited by 29 publications

References 45 publications

CT-based machine learning radiomics predicts CCR5 expression level and survival in ovarian cancer

CT-based machine learning radiomics predicts CCR5 expression level and survival in ovarian cancer

CT-based radiomics nomogram analysis for assessing BRCA mutation status in patients with high-grade serous ovarian cancer

CT Radiomics and Whole Genome Sequencing in Patients with Pancreatic Ductal Adenocarcinoma: Predictive Radiogenomics Modeling

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