Anju Sahdev

Objectives: To evaluate the impact of 5‐alpha reductase inhibitor (5‐ARI) exposure on multiparametric MRI (mpMRI) diagnostic performance for clinically significant prostate cancer (csPCa) and to assess whether standard prostate‐specific antigen density (PSAD)‐gated biopsy thresholds remain safe in this population. Subjects/patients and methods: A systematic review followed PRISMA 2020 (PROSPERO: CRD420251050507). PubMed/MEDLINE, EMBASE and the Cochrane Library were searched from January 2015 to December 2024. Eligible studies reported PI‐RADS, PSAD or lesion‐level apparent diffusion coefficient (ADC) data by exposure status, biopsy‐verified csPCa (ISUP grade group ≥2) and an unexposed comparator (≥100 patients). Risk of bias was assessed using QUADAS‐2 and ROBINS‐I; heterogeneity precluded meta‐analysis. Results: Seven comparative studies met inclusion criteria. Patient‐level csPCa diagnostic accuracy was largely maintained: Sensitivity at a PI‐RADS ≥3 threshold was 93.8% in exposed versus 96.1% in unexposed men, csPCa detection was 39.1% versus 39.3% (p = 0.9), and 5‐ARI exposure was not an independent negative predictor of detection. On blinded matched review, peripheral zone lesion ADC was higher in exposed men (869 vs. 765 mm ² /s, p = 0.04) with lower conspicuity (1.59 vs. 1.78, p = 0.03); that cohort also reported lower adjusted per‐lesion yield (OR 0.32, 95% CI 0.18–0.57), confined to the peripheral zone. A PSAD threshold of >0.15 ng/ml/cc avoided biopsy in 37.0% of exposed versus 30.3% of unexposed men, with missed csPCa in 12.3% and 11.0%, respectively. Conclusion: Across seven heterogeneous observational studies, patient‐level mpMRI performance for csPCa appears largely preserved in men receiving 5‐ARIs, though this lacks pooled support and was not reproduced in the one lesion‐level, zone‐stratified analysis. Standard PSAD‐gated thresholds, evidenced by a single cohort, may perform comparably across populations. Prospective validation is required.

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.

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.

Primary aldosteronism (PA) due to a unilateral aldosterone-producing adenoma is a common cause of hypertension. This can be cured, or greatly improved, by adrenal surgery. However, the invasive nature of the standard pre-surgical investigation contributes to fewer than 1% of patients with PA being offered the chance of a cure. The primary objective of our prospective study of 143 patients with PA (NCT02945904) was to compare the accuracy of a non-invasive test, [¹¹C]metomidate positron emission tomography computed tomography (MTO) scanning, with adrenal vein sampling (AVS) in predicting the biochemical remission of PA and the resolution of hypertension after surgery. A total of 128 patients reached 6- to 9-month follow-up, with 78 (61%) treated surgically and 50 (39%) managed medically. Of the 78 patients receiving surgery, 77 achieved one or more PA surgical outcome criterion for success. The accuracies of MTO at predicting biochemical and clinical success following adrenalectomy were, respectively, 72.7 and 65.4%. For AVS, the accuracies were 63.6 and 61.5%. MTO was not significantly superior, but the differences of 9.1% (95% confidence interval = −6.5 to 24.1%) and 3.8% (95% confidence interval = −11.9 to 9.4) lay within the pre-specified −17% margin for non-inferiority (P = 0.00055 and P = 0.0077, respectively). Of 24 serious adverse events, none was considered related to either investigation and 22 were fully resolved. MTO enables non-invasive diagnosis of unilateral PA.

Purpose To determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods. Materials and Methods A deep learning model for the two most common disease sites of high grade serous ovarian cancer lesions (pelvis/ovaries and omentum) was developed and compared against the well-established “no-new-Net” (nnU-Net) framework and unrevised trainee radiologist segmentations. A total of 451 pre-treatment and post neoadjuvant chemotherapy (NACT) CT scans collected from four different institutions were used for training (n=276), hyper-parameter tuning (n=104) and testing (n=71) of the methods. The performance was evaluated using the Dice similarity coefficient (DSC) and compared using a Wilcoxon test on paired results Results Our model outperforms the nnU-Net framework by a significant margin for both disease (validation: p=1×10-4,1.5×10-6, test: p=0.004, 0.005) and it does not perform significantly different from a trainee radiologist for the pelvic/ovarian lesions (p=0.392). On an independent test set (n=71), the model achieves a performance of 72±19 mean DSC for the pelvic/ovarian and 64±24 for the omental lesions. Conclusion Automated ovarian cancer segmentation on CT using deep neural networks is feasible and achieves performance close to a trainee-level radiologist for pelvic/ovarian lesions. Summary Deep learning-based models were used to assess whether fully automated segmentation is feasible for the main two disease sites in high grade serous ovarian cancer. Key Points First automated approach for pelvic/ovarian and omental ovarian cancer lesion segmentation on CT images. Automated segmentation of ovarian cancer lesions can be comparable with manual segmentation of trainee radiologists with three years of experience in oncological and gynecological imaging. Careful hyper-parameter tuning can provide models significantly outperforming strong state-of-the-art baselines.

Background: After adrenalectomy (ADX) for primary aldosteronism (PA), approximately 30% of patients achieve clinical success (normalisation of home BP); many additional patients report feeling subjectively better. We used the non-randomised MATCH study1 to further assess quality of life (QoL) changes in participants. Objective: Assess QoL using the 36-item Short Form Health Survey (SF-36) after surgical treatment of unilateral PA and medical treatment of (mainly) bilateral PA (MRA group). Method: Prospective analysis of SF-36 in MATCH, measured at baseline and 6 months after treatment. Summary scores (physical component summary (PCS) and mental component summary (MCS)) of 8 subscales were calculated for completed questionnaires. PCS and MCS range from 0-100, with population mean of 50. Results: At baseline, all SF-36 subscales were lower in patients with PA (n=71), with PCS 20% lower (SE 1.3), and MCS 13% lower (SE 1.3) vs the general population. At 6 months, ADX (n=44) conferred notable benefit, with PCS improved by 8.9 points (PPn=23), where there was decrease in PCS -4.41 (PPPPet al. 11C-metomidate PET CT vs Adrenal Vein Sampling for diagnosing surgically curable primary aldosteronism: prospective test validation, and impact of somatic genotype and ethnicity on outcomes, 29 December 2021, PREPRINT Research Square [https://doi.org/10.21203/rs.3.rs-1179128/v1z

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.

5074 Background: Existing clinicopathological tools are unable to accurately identify renal cell carcinoma (RCC) patients who will develop metastases after surgery. As a result, it is unclear how long and how often to follow-up patients post-operatively. Tumor macropathology, as assayed by CT scanning, represents the sum product of tumor biology and microenvironment. We hypothesized that quantitative tumor features extracted from CT scans (termed radiomics) could discriminate between metastatic and non-metastatic RCCs. Methods: This retrospective study incorporated three cohorts of clear-cell RCC patients (n = 279, from TCGA, CPTAC and KiTS19 datasets) treated with nephrectomy. The study cohort was sub-divided into metastatic (n = 54, M1 at diagnosis or recurrence after surgery), high metastatic risk/HMR (n = 85, N1, T3-4, T2G3/4, T1G4) or low metastatic risk/LMR (n = 140, absence of these features) subsets. 3D primary tumor segmentation of arterial contrast CT scans was performed by trained investigators. Features were extracted using pyRadiomics 2.2.0 (n = 839) with gray value and voxel size normalization. For random forest (RF) model training, the cohort was randomly split into training (75%) and validation (25%) sets. Results: Multidimensional clustering of radiomic features by t-SNE analysis showed that metastatic and HMR tumors predominantly cluster together, while LMR tumors cluster separately. Consistent with this, there were no differentially regulated radiomic features (DR-features) between HMR and metastatic tumors. In contrast, we identified 26 DR-features (adjusted p-value < 0.05) between presumed-metastatic (n = 139, HMR and metastatic tumors) and LMR tumors, which were then used as input to a RF binary classifier. In the training set, the trained classifier discriminated between presumed-metastatic and LMR tumors with bootstrapped AUC = 0.81. In the validation set, the classifier discriminated subsets with AUC = 0.80. Conclusions: High-risk and metastatic tumors have similar radiomic properties, suggesting common biology driving metastasis in RCC. We propose a novel radiomic classifier that accurately distinguishes between presumed-metastatic and low-risk tumors. Further work will assess whether this tool can identify patients with micrometastatic disease at diagnosis, who may benefit from adjuvant therapy or closer, long-term surveillance.

Background: Chronic pelvic pain (CPP) symptoms in women are variable and non-specific; establishing a differential diagnosis can be hard. A diagnostic laparoscopy is often performed, although a prior magnetic resonance imaging (MRI) scan may beneficial.Objectives: To estimate the accuracy and added value of MRI in making diagnoses of (1) idiopathic CPP and (2) the main gynaecological causes of CPP. To quantify the impact MRI can have on decision-making with respect to triaging for therapeutic laparoscopy and to conduct an economic evaluation.Design: Comparative test-accuracy study with cost-effectiveness modelling.Setting: Twenty-six UK-based hospitals.Participants: A total of 291 women with CPP.Methods: Pre-index information concerning the patient’s medical history, previous pelvic examinations and ultrasound scans was collected. Women reported symptoms and quality of life at baseline and 6 months. MRI scans and diagnostic laparoscopy (undertaken and interpreted blind to each other) were the index tests. For each potential cause of CPP, gynaecologists indicated their level of certainty that the condition was causing the pelvic pain. The analysis considered both diagnostic laparoscopy as a reference standard for observing structural gynaecological causes and consensus from a two-stage expert independent panel for ascertaining the cause of CPP. The stage 1 consensus was based on pre-index, laparoscopy and follow-up data; for stage 2, the MRI scan report was also provided. The primary analysis involved calculations of sensitivity and specificity for the presence or absence of each structural gynaecological cause of pain. A decision-analytic model was developed, with a 6-month time horizon. Two strategies, laparoscopy or MRI, were considered and populated with study data.Results: Using reference standards of laparoscopic and expert panel diagnoses, MRI scans had high specificity but poor sensitivity for observing deep-infiltrating endometriosis, endometrioma, adhesions and ovarian cysts. MRI scans correctly identified 56% [95% confidence interval (CI) 48% to 64%] of women judged to have idiopathic CPP, but missed 46% (95% CI 37% to 55%) of those considered to have a gynaecological structural cause of CPP. MRI added significant value, over and above the pre-index information, in identifying deep-infiltrating endometriosis (p = 0.006) and endometrioma (p = 0.02) as the cause of pain, but not for other gynaecological structural causes or for identifying idiopathic CPP (p = 0.08). Laparoscopy was significantly more accurate than MRI in diagnosing idiopathic CPP (p < 0.0001), superficial peritoneal endometriosis (p < 0.0001), deep-infiltrating endometriosis (p < 0.0001) and endometrioma of the ovary (p = 0.02) as the cause of pelvic pain. The accuracy of laparoscopy appeared to be able to rule in these diagnoses. Using MRI to identify women who require therapeutic laparoscopy would lead to 369 women in a cohort of 1000 receiving laparoscopy unnecessarily, and 136 women who required laparoscopy not receiving it. The economic analysis highlighted the importance of the time horizon, the prevalence of CPP and the cut-off values to inform the sensitivity and specificity of MRI and laparoscopy on the model results. MRI was not found to be a cost-effective diagnostic approach in any scenario.Conclusions: MRI was dominated by laparoscopy in differential diagnosis of women presenting to gynaecology clinics with CPP. It did not add value to information already gained from history, examination and ultrasound about idiopathic CPP and various gynaecological conditions.Trial registration: Current Controlled Trials ISRCTN13028601.Funding: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full inHealth Technology Assessment; Vol. 22, No. 40. See the NIHR Journals Library website for further project information.

[fluorine-18]-fluoro-2-deoxy-D-glucose positron emission tomography (18F-FDG PET CT) has increasing clinical applications supplementing conventional TVUS, CT and MRI imaging in assessing ovarian, cervical and endometrial cancer. The published literature on the applications of 18F-FDG PET CT shows its use can have significant impact on patient management by improving staging of the cancers, influencing patient selection for treatment and in detecting early recurrent disease. However, the increasing clinical use of PET CT does not always align with the guidelines, recommendations or expert opinion in the use of PET CT. This article summarizes the existing evidence base for the established clinical applications and the emerging roles for 18F-FDG PET CT in the common gynaecological malignancies.