Nishat Bharwani

An Integrated Clinical‐MR Radiomics Model to Estimate Survival Time in Patients With Endometrial Cancer

BackgroundDetermination of survival time in women with endometrial cancer using clinical features remains imprecise. Features from MRI may improve the survival estimation allowing improved treatment planning.PurposeTo identify clinical features and imaging signatures on T2‐weighted MRI that can be used in an integrated model to estimate survival time for endometrial cancer subjects.Study TypeRetrospective.PopulationFour hundred thirteen patients with endometrial cancer as training (N = 330, 66.41 ± 11.42 years) and validation (N = 83, 67.60 ± 11.89 years) data and an independent set of 82 subjects as testing data (63.26 ± 12.38 years).Field Strength/Sequence1.5‐T and 3‐T scanners with sagittal T2‐weighted spin echo sequence.AssessmentTumor regions were manually segmented on T2‐weighted images. Features were extracted from segmented masks, and clinical variables including age, cancer histologic grade and risk score were included in a Cox proportional hazards (CPH) model. A group least absolute shrinkage and selection operator method was implemented to determine the model from the training and validation datasets.Statistical TestsA likelihood‐ratio test and decision curve analysis were applied to compare the models. Concordance index (CI) and area under the receiver operating characteristic curves (AUCs) were calculated to assess the model.ResultsThree radiomic features (two image intensity and volume features) and two clinical variables (age and cancer grade) were selected as predictors in the integrated model. The CI was 0.797 for the clinical model (includes clinical variables only) and 0.818 for the integrated model using training and validation datasets, the associated mean AUC value was 0.805 and 0.853. Using the testing dataset, the CI was 0.792 and 0.882, significantly different and the mean AUC was 0.624 and 0.727 for the clinical model and integrated model, respectively.Data ConclusionThe proposed CPH model with radiomic signatures may serve as a tool to improve estimated survival time in women with endometrial cancer.Evidence Level4Technical EfficacyStage 2

Advancing personalised care in ovarian cancer using CT and MRI radiomics

Radiomics, utilising quantitative feature extraction from CT and MR imaging, offers significant potential in advancing the diagnosis and management of ovarian cancer. Through the analysis of high-dimensional imaging data, radiomics models may capture subtle phenotypic variations in tumour heterogeneity, texture, and shape that extend beyond the capabilities of traditional imaging interpretation. CT-based radiomics excels in evaluating the prognostic significance of peritoneal disease dissemination and treatment response, while MRI-based models provide enhanced soft tissue characterisation, particularly in assessing tumour microstructure, vascularity, and cellularity. Studies demonstrate that these models can improve diagnostic accuracy, predict therapeutic outcomes and assist in risk stratification. However, standardisation of imaging acquisition protocols, feature extraction techniques and validation across diverse patient cohorts remains a challenge for the incorporation of radiomics into routine clinical practice. Evidence strongly supports the incorporation of radiomic features with molecular, genomic and clinical data in developing high-performance integrated radiomics models, which can facilitate precision oncology in ovarian cancer.

Using diffusion-weighted imaging and blood inflammatory markers to preoperatively differentiate between leiomyosarcoma and atypical leiomyomas

Abstract Objectives This study aims to compare apparent diffusion coefficient (ADC) findings between leiomyosarcoma (LMS) and atypical/degenerate leiomyoma (LM) and evaluate the usefulness of this biomarker for diagnosis. Additionally it will explore the potential of preoperative neutrophil-lymphocyte ratio (NLR) as a haematological marker to aid in the differentiation of LMS from atypical LM. Methods Histologically proven LMS and LM patients between 2013 and 2023 were included. For all patients (191 LM, 18 LMS), the preoperative full blood count was analysed, and the NLR calculated. Whole volume of interest (VOI) and focal region of interest (ROI) areas were manually segmented on patients with DW-MRI sequences available (52 LM, 12 LMS). Mann–Whitney and Fishers exact test were used to assess statistical significance and receiver operating characteristic (ROC) curves for diagnostic performance. Results VOI and ROI mean ADC values were significantly lower for LMS than LM, with ROI mean ADC demonstrating greater diagnostic accuracy (area under the curve, [AUC] 0.817 vs 0.755). Applying a threshold ROI mean ADC value of ≤1.00 × 10−3 mm2/s achieved a sensitivity and specificity of 88.3% and 65.4%, respectively. A higher NLR was suggestive of LMS (median 2.8 vs 1.7 for LM). Conclusions ADC, particularly a focal ROI is useful in differentiating LMS from LM. Differences in preoperative blood markers, suggest an inflammatory-malignancy relationship. Future risk stratification models of ADC and haematological parameters should be explored. Advances in knowledge This study adds to few studies comparing using both ROI- and VOI-based methods, and no study has assessed both haematological markers and ADC metrics to aid differentiation.

Diagnostic Accuracy of FEC-PET/CT, FDG-PET/CT, and Diffusion-Weighted MRI in Detection of Nodal Metastases in Surgically Treated Endometrial and Cervical Carcinoma

Abstract Purpose: Preoperative nodal staging is important for planning treatment in cervical cancer and endometrial cancer, but remains challenging. We compare nodal staging accuracy of 18F-ethyl-choline-(FEC)-PET/CT, 18F-fluoro-deoxy-glucose-(FDG)-PET/CT, and diffusion-weighted-MRI (DW-MRI) with conventional morphologic MRI. Experimental Design: A prospective, multicenter observational study of diagnostic accuracy for nodal metastases was undertaken in 5 gyne-oncology centers. FEC-PET/CT, FDG-PET/CT, and DW-MRI were compared with nodal size and morphology on MRI. Reference standard was strictly correlated nodal histology. Eligibility included operable cervical cancer stage ≥ 1B1 or endometrial cancer (grade 3 any stage with myometrial invasion or grade 1–2 stage ≥ II). Results: Among 162 consenting participants, 136 underwent study DW-MRI and FDG-PET/CT and 60 underwent FEC-PET/CT. In 118 patients, 267 nodal regions were strictly correlated at histology (nodal positivity rate, 25%). Sensitivity per patient (n = 118) for nodal size, morphology, DW-MRI, FDG- and FEC-PET/CT was 40%*, 53%, 53%, 63%*, and 67% for all cases (*, P = 0.016); 10%, 10%, 20%, 30%, and 25% in cervical cancer (n = 40); 65%, 75%, 70%, 80% and 88% in endometrial cancer (n = 78). FDG-PET/CT outperformed nodal size (P = 0.006) and size ratio (P = 0.04) for per-region sensitivity. False positive rates were all <10%. Conclusions: All imaging techniques had low sensitivity for detection of nodal metastases and cannot replace surgical nodal staging. The performance of FEC-PET/CT was not statistically different from other techniques that are more widely available. FDG-PET/CT had higher sensitivity than size in detecting nodal metastases. False positive rates were low across all methods. The low false positive rate demonstrated by FDG-PET/CT may be helpful in arbitration of challenging surgical planning decisions.

Diagnostic performance of quantitative measures from [18F]FDG PET/CT, [18F]FEC PET/CT, and DW-MRI in the detection of lymph node metastases in endometrial and cervical cancer: data from the MAPPING study

Abstract Purpose To evaluate the diagnostic performance of quantitative measures derived from [ 18 F]FDG PET/CT, [ 18 F]FEC PET/CT, and DW-MRI in the detection of lymph node metastases in endometrial and cervical cancer with comparison to standard visual PET analysis with histology as the reference standard. Methods Subanalysis of quantitative data from the prospective multicentre MAPPING study. Nodal and tumour SUV max from [ 18 F]FDG PET/CT and [ 18 F]FEC PET/CT and ADC mean from DW-MRI were documented. Nodal-to-tumour ratios (NTR) and SUV max -to-ADC mean ratio (STAR) were calculated. Optimal cut-offs of quantitative measures were compared to visual assessment on a regional basis using histopathology as the reference standard. Results Scans from 112 patients (36 cervical and 76 endometrial cancers; 340 nodal regions) were eligible for quantitative image analysis. Lower ADC mean on DW-MRI was observed in metastatic nodes for cervical cancer but not for endometrial cancer. Quantitative measures were significantly higher in malignant than benign nodal regions on [ 18 F]FDG PET/CT and [ 18 F]FEC PET/CT in endometrial cancer. SUV max cut-offs showed similar performance to visual assessment in the diagnosis of metastatic lymph nodes in endometrial cancer whilst ADC mean cut-offs showed significantly lower specificity than visual assessment. Interobserver agreement was excellent for SUV max measurements on both [ 18 F]FDG PET/CT and [ 18 F]FEC PET/CT, but poor for ADC mean on DW-MRI. Conclusion Quantitative measures from [ 18 F]FDG PET/CT, [ 18 F]FEC PET/CT, or DW-MRI did not outperform visual assessment in the detection of nodal metastases in endometrial cancer. Therefore, the implementation of these quantitative measures as standalone diagnostic tools in routine clinical practice is not recommended.