Xin Gao

Overall Survival and the Evolving Benefit-Risk Assessment for Poly (ADP-ribose) Polymerase Inhibitors in Advanced Ovarian Cancer

From 2014 through 2019, the US Food and Drug Administration (FDA) granted approval to six indications for poly (ADP-ribose) polymerase (PARP) inhibitors in advanced epithelial ovarian cancer (EOC). From 2022 through 2023, these six indications were withdrawn or narrowed after observation of a potential detrimental effect on overall survival (OS) in four randomized controlled trials. The indications for niraparib, olaparib, and rucaparib for the treatment of BRCA -mutated or homologous recombination deficiency–positive advanced EOC were withdrawn. The indications for niraparib, olaparib, and rucaparib for the maintenance treatment of recurrent EOC were narrowed to only patients with BRCA mutations. Recognizing the clinical implications of these regulatory actions, herein we describe the FDA's decision-making process and the rationale behind the removal or narrowing of these indications for PARP inhibitors in advanced EOC. Furthermore, this article provides insight into the FDA's interpretation of potential OS detriments and subgroup analyses to shape regulatory decisions.

Peritumoral MRI Radiomics Features Increase the Evaluation Efficiency for Response to Chemotherapy in Patients With Epithelial Ovarian Cancer

BackgroundIt remains unclear whether extracting peritumoral volume (PTV) radiomics features are useful tools for evaluating response to chemotherapy of epithelial ovarian cancer (EOC).PurposeTo evaluate MRI radiomics signatures (RS) capturing subtle changes of PTV and their added evaluation performance to whole tumor volume (WTV) for response to chemotherapy in patients with EOC.Study TypeRetrospective.Population219 patients aged from 15 to 79 years were enrolled.Field Strength/Sequence3.0 or 1.5T, axial fat‐suppressed T2‐weighted imaging (FS‐T2WI), diffusion‐weighted imaging (DWI), and contrast enhanced T1‐weighted imaging (CE‐T1WI).AssessmentMRI features were extracted from the four axial sequences and six different volumes of interest (VOIs) (WTV and WTV + PTV (WPTV)) with different peritumor sizes (PS) ranging from 1 to 5 mm. Those features underwent preprocessing, and the most informative features were selected using minimum redundancy maximum relevance and least absolute shrinkage and selection operator to construct the RS. The optimal RS, with the highest area under the curve (AUC) of receiver operating characteristic was then integrated with independent clinical characteristics through multivariable logistic regression to construct the radiomics‐clinical model (RCM).Statistical TestsMann–Whitney U test, chi‐squared test, DeLong test, log‐rank test. P < 0.05 indicated a significant difference.ResultsAll the RSs constructed on WPTV exhibited higher AUCs (0.720–0.756) than WTV (0.671). Of which, RS with PS = 2 mm displayed a significantly better performance (AUC = 0.756). International Federation of Gynecology and Obstetrics (FIGO) stage was identified as the exclusive independent clinical evaluation characteristic, and the RCM demonstrated higher AUC (0.790) than the RS, but without statistical significance (P = 0.261).Data ConclusionThe radiomics features extracted from PTV could increase the efficiency of WTV radiomics for evaluating the chemotherapy response of EOC. The cut‐off of 2 mm PTV was a reasonable value to obtain effective evaluation efficiency.Level of Evidence4Technical EfficacyStage 2

MRI‐Based Machine Learning for Differentiating Borderline From Malignant Epithelial Ovarian Tumors: A Multicenter Study

BackgroundPreoperative differentiation of borderline from malignant epithelial ovarian tumors (BEOT from MEOT) can impact surgical management. MRI has improved this assessment but subjective interpretation by radiologists may lead to inconsistent results.PurposeTo develop and validate an objective MRI‐based machine‐learning (ML) assessment model for differentiating BEOT from MEOT, and compare the performance against radiologists' interpretation.Study TypeRetrospective study of eight clinical centers.PopulationIn all, 501 women with histopathologically‐confirmed BEOT (n = 165) or MEOT (n = 336) from 2010 to 2018 were enrolled. Three cohorts were constructed: a training cohort (n = 250), an internal validation cohort (n = 92), and an external validation cohort (n = 159).Field Strength/SequencePreoperative MRI within 2 weeks of surgery. Single‐ and multiparameter (MP) machine‐learning assessment models were built utilizing the following four MRI sequences: T2‐weighted imaging (T2WI), fat saturation (FS), diffusion‐weighted imaging (DWI), apparent diffusion coefficient (ADC), and contrast‐enhanced (CE)‐T1WI.AssessmentDiagnostic performance of the models was assessed for both whole tumor (WT) and solid tumor (ST) components. Assessment of the performance of the model in discriminating BEOT vs. early‐stage MEOT was made. Six radiologists of varying experience also interpreted the MR images.Statistical TestsMann–Whitney U‐test: significance of the clinical characteristics; chi‐square test: difference of label; DeLong test: difference of receiver operating characteristic (ROC).ResultsThe MP‐ST model performed better than the MP‐WT model for both the internal validation cohort (area under the curve [AUC] = 0.932 vs. 0.917) and external validation cohort (AUC = 0.902 vs. 0.767). The model showed capability in discriminating BEOT vs. early‐stage MEOT, with AUCs of 0.909 and 0.920, respectively. Radiologist performance was considerably poorer than both the internal (mean AUC = 0.792; range, 0.679–0.924) and external (mean AUC = 0.797; range, 0.744–0.867) validation cohorts.Data ConclusionPerformance of the MRI‐based ML model was robust and superior to subjective assessment of radiologists. If our approach can be implemented in clinical practice, improved preoperative prediction could potentially lead to preserved ovarian function and fertility for some women.Level of EvidenceLevel 4.Technical EfficacyStage 2. J. Magn. Reson. Imaging 2020;52:897–904.

MRI‐Based Multiple Instance Convolutional Neural Network for Increased Accuracy in the Differentiation of Borderline and Malignant Epithelial Ovarian Tumors

BackgroundPreoperative differentiation of borderline from malignant epithelial ovarian tumors (BEOT vs. MEOT) is challenging and can significantly impact surgical management.PurposeTo develop a multiple instance convolutional neural network (MICNN) that can differentiate BEOT from MEOT, and to compare its diagnostic performance with that of radiologists.Study TypeRetrospective study of eight clinical centers.SubjectsBetween January 2010 and June 2018, a total of 501 women (mean age, 48.93 ± 14.05 years) with histopathologically confirmed BEOT (N = 165) or MEOT (N = 336) were divided into the training (N = 342) and validation cohorts (N = 159).Field Strength/SequenceThree axial sequences from 1.5 or 3 T scanner were used: fast spin echo T2‐weighted imaging with fat saturation (T2WI FS), echo planar diffusion‐weighted imaging, and 2D volumetric interpolated breath‐hold examination of contrast‐enhanced T1‐weighted imaging (CE‐T1WI) with FS.AssessmentThree monoparametric MICNN models were built based on T2WI FS, apparent diffusion coefficient map, and CE‐T1WI. Based on these monoparametric models, we constructed an early multiparametric (EMP) model and a late multiparametric (LMP) model using early and late information fusion methods, respectively. The diagnostic performance of the models was evaluated using the receiver operating characteristic (ROC) curve and compared to the performance of six radiologists with varying levels of experience.Statistical TestsWe used DeLong test, chi‐square test, Mann–Whitney U‐test, and t‐test, with significance level of 0.05.ResultsBoth EMP and LMP models differentiated BEOT from MEOT, with an area under the ROC curve (AUC) of 0.855 (95% CI, 0.795–0.915) and 0.884 (95% CI, 0.831–0.938), respectively. The AUC of the LMP model was significantly higher than the radiologists' pooled AUC (0.884 vs. 0.797).Data ConclusionThe developed MICNN models can effectively differentiate BEOT from MEOT and the diagnostic performances (AUCs) were more superior than that of the radiologists' assessments.Level of Evidence3Technical Efficacy Stage2

Noninvasive prediction of residual disease for advanced high-grade serous ovarian carcinoma by MRI-based radiomic-clinical nomogram

To develop a preoperative MRI-based radiomic-clinical nomogram for prediction of residual disease (RD) in patients with advanced high-grade serous ovarian carcinoma (HGSOC). In total, 217 patients with advanced HGSOC were enrolled from January 2014 to June 2019 and randomly divided into a training set (n = 160) and a validation set (n = 57). Finally, 841 radiomic features were extracted from each tumor on T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) sequence, respectively. We used two fusion methods, the maximal volume of interest (MV) and the maximal feature value (MF), to fuse the radiomic features of bilateral tumors, so that patients with bilateral tumors have the same kind of radiomic features as patients with unilateral tumors. The radiomic signatures were constructed by using mRMR method and LASSO classifier. Multivariable logistic regression analysis was used to develop a radiomic-clinical nomogram incorporating radiomic signature and conventional clinico-radiological features. The performance of the nomogram was evaluated on the validation set. In total, 342 tumors from 217 patients were analyzed in this study. The MF-based radiomic signature showed significantly better prediction performance than the MV-based radiomic signature (AUC = 0.744 vs. 0.650, p = 0.047). By incorporating clinico-radiological features and MF-based radiomic signature, radiomic-clinical nomogram showed favorable prediction ability with an AUC of 0.803 in the validation set, which was significantly higher than that of clinico-radiological signature and MF-based radiomic signature (AUC = 0.623, 0.744, respectively). The proposed MRI-based radiomic-clinical nomogram provides a promising way to noninvasively predict the RD status. • MRI-based radiomic-clinical nomogram is feasible to noninvasively predict residual disease in patients with advanced HGSOC. • The radiomic signature based on MF showed significantly better prediction performance than that based on MV. • The radiomic-clinical nomogram showed a favorable prediction ability with an AUC of 0.803.

Deep learning based cervical screening by the cross-modal integration of colposcopy, cytology, and HPV test

To develop and evaluate the colposcopy based deep learning model using all kinds of cervical images for cervical screening, and investigate the synergetic benefits of the colposcopy, the cytology test, and the HPV test for improving cervical screening performance. This study consisted of 2160 women who underwent cervical screening, there were 442 cases with the histopathological confirmed high-grade squamous intraepithelial lesion (HSIL) or cancer, and the remained 1718 women were controls. Three kinds of cervical images were acquired from colposcopy including the saline image of cervix after saline irrigation, the acetic acid image of cervix after applying acetic acid solution, and the iodine image of cervix after applying Lugol's iodine solution. Each kind of image was used to build a single-image based deep learning model by the VGG-16 convolutional neural network, respectively. A multiple-images based deep learning model was built using multivariable logistic regression (MLR) by combining the single-image based models. The performance of the visual inspection was also obtained. The results of the cytology test and HPV test were used to build a Cytology-HPV joint diagnostic model by MLR. Finally, a cross-modal integrated model was built using MLR by combining the multiple-images based deep learning model, the cytology test results, and the HPV test results. The performances of models were tested in an independent test set using the area under the receiver operating characteristic curve (AUC). The saline image, acetic acid image, and iodine image based deep learning models had AUC of 0.760, 0.791, and 0.840. The multiple-images based deep learning model achieved an improved AUC of 0.845. The AUC of the visual inspection was 0.751. The Cytology-HPV joint diagnostic model had an AUC of 0.837, which was higher than the cytology test (AUC = 0.749) and the HPV test (AUC = 0.742). The cross-modal integrated model achieved the best performance with AUC of 0.921. Combining all kinds of cervical images were benefit for improving the performance of the colposcopy based deep learning model, and more accurate cervical screening could be achieved by incorporating the colposcopy based deep learning model, the cytology test results, and the HPV test results.

CIA-net: Cross-modality interaction and aggregation network for ovarian tumor segmentation from multi-modal MRI

Magnetic resonance imaging (MRI) is an essential examination for ovarian cancer, in which ovarian tumor segmentation is crucial for personalized diagnosis and treatment planning. However, ovarian tumors often present with mixed cystic and solid regions in imaging, posing additional difficulties for automatic segmentation. In clinical practice, radiologists use T2-weighted imaging as the main modality to delineate tumor boundaries. In comparison, multi-modal MRI provides complementary information across modalities that can improve tumor segmentation. Therefore, it is important to fuse salient features from other modalities to the main modality. In this paper, we propose a cross-modality interaction and aggregation network (CIA-Net), a hybrid convolutional and Transformer architecture, for automatic ovarian tumor segmentation from multi-modal MRI. CIA-Net divides multi-modal MRI into one main (T2) and three minor modalities (T1, ADC, DWI), each with independent encoders. The novel cross-modality collaboration block selectively aggregates complementary features from minor modalities into the main modality through a progressive context injection module. Additionally, we introduce the progressive neighborhood integrated module to filter intra- and inter-modality noise and redundancies by refining adjacent slices of each modality. We evaluate our proposed method on a diverse, multi-center ovarian tumor dataset comprising 739 patients, and further validate its generalization and robustness on two public benchmarks for brain and cardiac segmentation. Comparative experiments with other cutting-edge techniques demonstrate the effectiveness of CIA-Net, highlighting its potential to be applied in clinical scenarios.