Andrea G Rockall

Artificial intelligence in women’s cancers: innovation and challenges in clinical translation

CT and MRI in Advanced Ovarian Cancer: Advances in Imaging Techniques

Ovarian cancer (OC) remains one of the leading causes of gynecologic cancer-related mortality, with most patients presenting with disseminated disease, particularly within the peritoneal cavity. Standard treatment includes cytoreductive surgery, platinum-based chemotherapy, and targeted maintenance approaches depending on the patient's and tumor's genetic profile. Despite treatment advancements, approximately 25% of high-grade serous OC cases relapse within a year despite optimal primary treatment with complete tumor clearance at cytoreduction. Advances in contrast-enhanced CT (CE-CT) and MRI have revolutionized the evaluation and treatment planning of advanced OC. CT remains the gold standard for staging and assessing tumor extent, effectively identifying peritoneal, lymphatic, and distant metastases. However, it is less effective in detecting small-volume peritoneal dissemination. MRI, with superior soft-tissue contrast, complements CT by providing a detailed assessment of peritoneal disease, characterizing sonographically indeterminate adnexal masses. Diffusion-weighted imaging and gadolinium-enhanced MRI have improved the diagnostic sensitivity for peritoneal disease but are unable to predict treatment response, recurrence risk, and prognosis. Radiomics, which extracts quantitative tumor features from imaging data, holds promise for personalizing treatment and identifying patients at risk for early recurrence despite optimal therapy. The integration of CT, MRI, and radiomics could enhance surgical planning and improve long-term survival outcomes in patients with advanced OC.

Weibull parametric model for survival analysis in women with endometrial cancer using clinical and T2-weighted MRI radiomic features

AbstractBackgroundSemiparametric survival analysis such as the Cox proportional hazards (CPH) regression model is commonly employed in endometrial cancer (EC) study. Although this method does not need to know the baseline hazard function, it cannot estimate event time ratio (ETR) which measures relative increase or decrease in survival time. To estimate ETR, the Weibull parametric model needs to be applied. The objective of this study is to develop and evaluate the Weibull parametric model for EC patients’ survival analysis.MethodsTraining (n = 411) and testing (n = 80) datasets from EC patients were retrospectively collected to investigate this problem. To determine the optimal CPH model from the training dataset, a bi-level model selection with minimax concave penalty was applied to select clinical and radiomic features which were obtained from T2-weighted MRI images. After the CPH model was built, model diagnostic was carried out to evaluate the proportional hazard assumption with Schoenfeld test. Survival data were fitted into a Weibull model and hazard ratio (HR) and ETR were calculated from the model. Brier score and time-dependent area under the receiver operating characteristic curve (AUC) were compared between CPH and Weibull models. Goodness of the fit was measured with Kolmogorov-Smirnov (KS) statistic.ResultsAlthough the proportional hazard assumption holds for fitting EC survival data, the linearity of the model assumption is suspicious as there are trends in the age and cancer grade predictors. The result also showed that there was a significant relation between the EC survival data and the Weibull distribution. Finally, it showed that Weibull model has a larger AUC value than CPH model in general, and it also has smaller Brier score value for EC survival prediction using both training and testing datasets, suggesting that it is more accurate to use the Weibull model for EC survival analysis.ConclusionsThe Weibull parametric model for EC survival analysis allows simultaneous characterization of the treatment effect in terms of the hazard ratio and the event time ratio (ETR), which is likely to be better understood. This method can be extended to study progression free survival and disease specific survival.Trial registrationClinicalTrials.gov NCT03543215,https://clinicaltrials.gov/, date of registration: 30th June 2017.

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.

Validation analysis of the novel imaging-based prognostic radiomic signature in patients undergoing primary surgery for advanced high-grade serous ovarian cancer (HGSOC)

Abstract Background Predictive models based on radiomics features are novel, highly promising approaches for gynaecological oncology. Here, we wish to assess the prognostic value of the newly discovered Radiomic Prognostic Vector (RPV) in an independent cohort of high-grade serous ovarian cancer (HGSOC) patients, treated within a Centre of Excellence, thus avoiding any bias in treatment quality. Methods RPV was calculated using standardised algorithms following segmentation of routine preoperative imaging of patients (n = 323) who underwent upfront debulking surgery (01/2011-07/2018). RPV was correlated with operability, survival and adjusted for well-established prognostic factors (age, postoperative residual disease, stage), and compared to previous validation models. Results The distribution of low, medium and high RPV scores was 54.2% (n = 175), 33.4% (n = 108) and 12.4% (n = 40) across the cohort, respectively. High RPV scores independently associated with significantly worse progression-free survival (PFS) (HR = 1.69; 95% CI:1.06–2.71; P = 0.038), even after adjusting for stage, age, performance status and residual disease. Moreover, lower RPV was significantly associated with total macroscopic tumour clearance (OR = 2.02; 95% CI:1.56–2.62; P = 0.00647). Conclusions RPV was validated to independently identify those HGSOC patients who will not be operated tumour-free in an optimal setting, and those who will relapse early despite complete tumour clearance upfront. Further prospective, multicentre trials with a translational aspect are warranted for the incorporation of this radiomics approach into clinical routine.

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.

Ovarian Cancer Radiomics Approach in CT Led Evaluation

When patients have suspected or confirmed ovarian cancer standard treatment will involve surgery and chemotherapy. However, as with any treatment, it is challenging to predict treatment response in advance. Before treatment, all patients have a CT scan to describe where the cancer is in order to guide the treatment. There is now a new way to analyse routine scans using advanced computing methods, which may give more information about the ovarian cancer. This is called radiomics which analyses features in scans that are not visible to the naked eye. Our group at Imperial College London has worked on developing radiomic models to better understand ovarian cancer. This study aims to determine whether the information gained from this new approach would help us to tailor patient treatment plans to better meet the patient's individual needs, even more than done already. Furthermore, the aim is to understand how different types of ovarian cancer can correlate with the radiomic findings, which may help develop potential treatments in the future.

Can Staging Magnetic Resonance Imaging (MRI) Features Prognosticate Patients Presenting With Endometrial Cancer?

Aim: Assess the value of MRI features in predicting prognosis in patients with endometrial cancer This study will examine the MRI features of women with confirmed endometrial cancer to see if textural features can prognosticate patients.