Multiplexed sensitivity-encoding diffusion-weighted imaging (MUSE) in diffusion-weighted imaging for rectal MRI: a quantitative and qualitative analysis at multiple b-values.

PURPOSE: To compare four diffusion-weighted imaging (DWI) sequences for image quality, rectal contour, and lesion conspicuity, and to assess the difference in their signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC). METHODS: In this retrospective study of 36 consecutive patients who underwent 3.0 T rectal MRI from January-June 2020, DWI was performed with single-shot echo planar imaging (ss-EPI) (b800 s/mm2), multiplexed sensitivity encoding (MUSE) (b800 s/mm2), MUSE (b1500 s/mm2), and field-of-view optimized and constrained undistorted single-shot (FOCUS) (b1500 s/mm2). Two radiologists independently scored image quality using a 5-point Likert scale. Inter-reader agreement was assessed using the weighted Cohen's к. SNR, CNR, and ADC measurements were compared using the paired t-test. RESULTS: For both readers, MUSE b800 scored significantly higher for image quality, rectal contour, and lesion conspicuity compared to ss-EPI; MUSE b800 also scored significantly higher for image quality and rectal contour compared to all other sequences. Lesion conspicuity was equally superior for MUSE b800 and MUSE b1500 compared to the other two sequences. There was good to excellent inter-reader agreement for all qualitative features (к = 0.72-0.88). MUSE b800 had the highest SNR; MUSE b1500 had the highest CNR. A significant difference in ADC was observed between ss-EPI compared to the other sequences (p < 0.001) and between MUSE b800 and FOCUS. No significant difference in ADC was found between MUSE b1500 and FOCUS b1500. CONCLUSION: MUSE b800 improved image quality over ss-EPI and both MUSE b800 and b1500 showed better tumor conspicuity compared to conventional ss-EPI.

Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer.

Immunotherapy is used to treat almost all patients with advanced non-small cell lung cancer (NSCLC); however, identifying robust predictive biomarkers remains challenging. Here we show the predictive capacity of integrating medical imaging, histopathologic and genomic features to predict immunotherapy response using a cohort of 247 patients with advanced NSCLC with multimodal baseline data obtained during diagnostic clinical workup, including computed tomography scan images, digitized programmed death ligand-1 immunohistochemistry slides and known outcomes to immunotherapy. Using domain expert annotations, we developed a computational workflow to extract patient-level features and used a machine-learning approach to integrate multimodal features into a risk prediction model. Our multimodal model (area under the curve (AUC) = 0.80, 95% confidence interval (CI) 0.74-0.86) outperformed unimodal measures, including tumor mutational burden (AUC = 0.61, 95% CI 0.52-0.70) and programmed death ligand-1 immunohistochemistry score (AUC = 0.73, 95% CI 0.65-0.81). Our study therefore provides a quantitative rationale for using multimodal features to improve prediction of immunotherapy response in patients with NSCLC using expert-guided machine learning.

Can 18F-FDG PET/CT Radiomics Features Predict Clinical Outcomes in Patients with Locally Advanced Esophageal Squamous Cell Carcinoma?

This study aimed to assess the usefulness of radiomics features of 18F-FDG PET/CT in patients with locally advanced esophageal cancers (ESCC) in predicting outcomes such as clinical tumor (cT) and nodal (cN) categories, PET response to induction chemotherapy (PET response), progression-free survival (PFS), and overall survival (OS). Pretreatment PET/CT images from patients who underwent concurrent chemoradiotherapy from July 2002 to February 2017 were segmented, and data were split into training and test sets. Model development was performed on the training datasets and a maximum of five features were selected. Final diagnostic accuracies were determined using the test dataset. A total of 86 PET/CTs (58 men and 28 women, mean age 65 years) were segmented. Due to small lesion size, 12 patients were excluded. The diagnostic accuracies as derived from the CT, PET, and combined PET/CT test datasets were as follows: cT category-70.4%, 70.4%, and 81.5%, respectively; cN category-69.0%, 86.2%, and 86.2%, respectively; PET response-60.0%, 66.7%, and 70.0%, respectively; PFS-60.7%, 75.0%, and 75.0%, respectively; and OS-51.7%, 55.2%, and 62.1%, respectively. A radiomics assessment of locally advanced ESCC has the potential to predict various clinical outcomes. External validation of these models would be further helpful.

Combined artificial intelligence and radiologist model for predicting rectal cancer treatment response from magnetic resonance imaging: an external validation study.

PURPOSE: To evaluate an MRI-based radiomic texture classifier alone and combined with radiologist qualitative assessment in predicting pathological complete response (pCR) using restaging MRI with internal training and external validation. METHODS: Consecutive patients with locally advanced rectal cancer (LARC) who underwent neoadjuvant therapy followed by total mesorectal excision from March 2012 to February 2016 (Memorial Sloan Kettering Cancer Center/internal dataset, n = 114, 41% female, median age = 55) and July 2014 to October 2015 (Instituto do Câncer do Estado de São Paulo/external dataset, n = 50, 52% female, median age = 64.5) were retrospectively included. Two radiologists (R1, senior; R2, junior) independently evaluated restaging MRI, classifying patients (radiological complete response vs radiological partial response). Model A (n = 33 texture features), model B (n = 91 features including texture, shape, and edge features), and two combination models (model A + B + R1, model A + B + R2) were constructed. Pathology served as the reference standard for neoadjuvant treatment response. Comparison of the classifiers' AUCs on the external set was done using DeLong's test. RESULTS: Models A and B had similar discriminative ability (P = 0.3; Model B AUC = 83%, 95% CI 70%-97%). Combined models increased inter-reader agreement compared with radiologist-only interpretation (κ = 0.82, 95% CI 0.70-0.89 vs k = 0.25, 95% CI 0.11-0.61). The combined model slightly increased junior radiologist specificity, positive predictive value, and negative predictive values (93% vs 90%, 57% vs 50%, and 91% vs 90%, respectively). CONCLUSION: We developed and externally validated a combined model using radiomics and radiologist qualitative assessment, which improved inter-reader agreement and slightly increased the diagnostic performance of the junior radiologist in predicting pCR after neoadjuvant treatment in patients with LARC.

Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer.

Patients with high-grade serous ovarian cancer suffer poor prognosis and variable response to treatment. Known prognostic factors for this disease include homologous recombination deficiency status, age, pathological stage and residual disease status after debulking surgery. Recent work has highlighted important prognostic information captured in computed tomography and histopathological specimens, which can be exploited through machine learning. However, little is known about the capacity of combining features from these disparate sources to improve prediction of treatment response. Here, we assembled a multimodal dataset of 444 patients with primarily late-stage high-grade serous ovarian cancer and discovered quantitative features, such as tumor nuclear size on staining with hematoxylin and eosin and omental texture on contrast-enhanced computed tomography, associated with prognosis. We found that these features contributed complementary prognostic information relative to one another and clinicogenomic features. By fusing histopathological, radiologic and clinicogenomic machine-learning models, we demonstrate a promising path toward improved risk stratification of patients with cancer through multimodal data integration.

MRI radiomics features of mesorectal fat can predict response to neoadjuvant chemoradiation therapy and tumor recurrence in patients with locally advanced rectal cancer.

OBJECTIVE: To interrogate the mesorectal fat using MRI radiomics feature analysis in order to predict clinical outcomes in patients with locally advanced rectal cancer. METHODS: This retrospective study included patients who underwent neoadjuvant chemoradiotherapy for locally advanced rectal cancer from 2009 to 2015. Three radiologists independently segmented mesorectal fat on baseline T2-weighted axial MRI. Radiomics features were extracted from segmented volumes and calculated using CERR software, with adaptive synthetic sampling being employed to combat large class imbalances. Outcome variables included pathologic complete response (pCR), local recurrence, distant recurrence, clinical T-category (cT), post-treatment T category (ypT), and post-treatment N category (ypN). A maximum of eight most important features were selected for model development using support vector machines and fivefold cross-validation to predict each outcome parameter via elastic net regularization. Diagnostic metrics of the final models were calculated, including sensitivity, specificity, PPV, NPV, accuracy, and AUC. RESULTS: The study included 236 patients (54 ± 12 years, 135 men). The AUC, sensitivity, specificity, PPV, NPV, and accuracy for each clinical outcome were as follows: for pCR, 0.89, 78.0%, 85.1%, 52.5%, 94.9%, 83.9%; for local recurrence, 0.79, 68.3%, 80.7%, 46.7%, 91.2%, 78.3%; for distant recurrence, 0.87, 80.0%, 88.4%, 58.3%, 95.6%, 87.0%; for cT, 0.80, 85.8%, 56.5%, 89.1%, 49.1%, 80.1%; for ypN, 0.74, 65.0%, 80.1%, 52.7%, 87.0%, 76.3%; and for ypT, 0.86, 81.3%, 84.2%, 96.4%, 46.4%, 81.8%. CONCLUSION: Radiomics features of mesorectal fat can predict pathological complete response and local and distant recurrence, as well as post-treatment T and N categories. KEY POINTS: • Mesorectal fat contains important prognostic information in patients with locally advanced rectal cancer (LARC). • Radiomics features of mesorectal fat were significantly different between those who achieved complete vs incomplete pathologic response (accuracy 83.9%, 95% CI: 78.6-88.4%). • Radiomics features of mesorectal fat were significantly different between those who did vs did not develop local or distant recurrence (accuracy 78.3%, 95% CI: 72.0-83.7% and 87.0%, 95% CI: 81.6-91.2% respectively).

Unraveling tumor-immune heterogeneity in advanced ovarian cancer uncovers immunogenic effect of chemotherapy.

In metastatic cancer, the degree of heterogeneity of the tumor microenvironment (TME) and its molecular underpinnings remain largely unstudied. To characterize the tumor-immune interface at baseline and during neoadjuvant chemotherapy (NACT) in high-grade serous ovarian cancer (HGSOC), we performed immunogenomic analysis of treatment-naive and paired samples from before and after treatment with chemotherapy. In treatment-naive HGSOC, we found that immune-cell-excluded and inflammatory microenvironments coexist within the same individuals and within the same tumor sites, indicating ubiquitous variability in immune cell infiltration. Analysis of TME cell composition, DNA copy number, mutations and gene expression showed that immune cell exclusion was associated with amplification of Myc target genes and increased expression of canonical Wnt signaling in treatment-naive HGSOC. Following NACT, increased natural killer (NK) cell infiltration and oligoclonal expansion of T cells were detected. We demonstrate that the tumor-immune microenvironment of advanced HGSOC is intrinsically heterogeneous and that chemotherapy induces local immune activation, suggesting that chemotherapy can potentiate the immunogenicity of immune-excluded HGSOC tumors.

Hyperpolarized MRI of Human Prostate Cancer Reveals Increased Lactate with Tumor Grade Driven by Monocarboxylate Transporter 1.

Metabolic imaging using hyperpolarized magnetic resonance can increase the sensitivity of MRI, though its ability to inform on relevant changes to biochemistry in humans remains unclear. In this work, we image pyruvate metabolism in patients, assessing the reproducibility of delivery and conversion in the setting of primary prostate cancer. We show that the time to max of pyruvate does not vary significantly within patients undergoing two separate injections or across patients. Furthermore, we show that lactate increases with Gleason grade. RNA sequencing data demonstrate a significant increase in the predominant pyruvate uptake transporter, monocarboxylate transporter 1. Increased protein expression was also observed in regions of high lactate signal, implicating it as the driver of lactate signal in vivo. Targeted DNA sequencing for actionable mutations revealed the highest lactate occurred in patients with PTEN loss. This work identifies a potential link between actionable genomic alterations and metabolic information derived from hyperpolarized pyruvate MRI.

Association between CT-texture-derived tumor heterogeneity, outcomes, and BRCA mutation status in patients with high-grade serous ovarian cancer.

PURPOSE: To assess the associations between inter-site texture heterogeneity parameters derived from computed tomography (CT), survival, and BRCA mutation status in women with high-grade serous ovarian cancer (HGSOC). MATERIALS AND METHODS: Retrospective study of 88 HGSOC patients undergoing CT and BRCA mutation status testing prior to primary cytoreductive surgery. Associations between texture metrics-namely inter-site cluster variance (SCV), inter-site cluster prominence (SCP), inter-site cluster entropy (SE)-and overall survival (OS), progression-free survival (PFS) as well as BRCA mutation status were assessed. RESULTS: Higher inter-site cluster variance (SCV) was associated with lower PFS (p = 0.006) and OS (p = 0.003). Higher inter-site cluster prominence (SCP) was associated with lower PFS (p = 0.02) and higher inter-site cluster entropy (SE) correlated with lower OS (p = 0.01). Higher values of all three metrics were significantly associated with lower complete surgical resection status in BRCA-negative patients (SE p = 0.039, SCV p = 0.006, SCP p = 0.02), but not in BRCA-positive patients (SE p = 0.7, SCV p = 0.91, SCP p = 0.67). None of the metrics were able to distinguish between BRCA mutation carrier and non-mutation carrier. CONCLUSION: The assessment of tumoral heterogeneity in the era of personalized medicine is important, as increased heterogeneity has been associated with distinct genomic abnormalities and worse patient outcomes. A radiomics approach using standard-of-care CT scans might have a clinical impact by offering a non-invasive tool to predict outcome and therefore improving treatment effectiveness. However, it was not able to assess BRCA mutation status in women with HGSOC.

Temporal changes in MRI appearance of the prostate after focal ablation.

PURPOSE: The purpose of our study was to retrospectively evaluate and categorize temporal changes in MRI appearances of the prostate in patients who underwent focal therapy with MRI follow-up. METHODS: The Institutional Review Board approved this retrospective study and waived the requirement for informed consent. Thirty-seven patients (median age 61; 48-70 years) with low-to-intermediate-risk, clinically organ-confined prostate cancer underwent focal ablation therapy from 2009 to 2014. Two radiologists reviewed post-treatment MRIs (n = 76) and categorized imaging features blinded to the time interval between the focal therapy and the follow-up MRI. Inter-reader agreement was assessed (kappa) and generalized linear regression was used to examine associations between an imaging feature being present/absent and days between ablation and MRI. RESULTS: Inter-reader agreement on MRI features ranged from fair to substantial. Edema was found present at earlier times after ablation (median 16-25 days compared to MRIs without edema, median 252-514 days), as was rim enhancement of the ablation zone (18-22.5 days vs. 409-593 days), a hypointense rim around the ablation zone on T2-weighted images (53-57.5 days vs. 279-409 days) and the presence of an appreciable ablation cavity (48.5-60 days vs. 613-798 days, all p < 0.05). Enhancement of the ablation zone/scar (553-731 days vs. 61.5-162 days) and the formation of a T2-hypointense scar were found to be present on later MRI scans (514-553 days vs. 29-32 days, one reader). CONCLUSIONS: The MRI appearance of the prostate after focal ablation changes substantially over time. Identification of temporal patterns in the appearance of imaging features should help reduce image interpretation variability and errors when assessing post-therapeutic scans.

Metabolic Imaging of the Human Brain with Hyperpolarized 13C Pyruvate Demonstrates 13C Lactate Production in Brain Tumor Patients.

Hyperpolarized (HP) MRI using [1-13C] pyruvate is a novel method that can characterize energy metabolism in the human brain and brain tumors. Here, we present the first dynamically acquired human brain HP 13C metabolic spectra and spatial metabolite maps in cases of both untreated and recurrent tumors. In vivo production of HP lactate from HP pyruvate by tumors was indicative of altered cancer metabolism, whereas production of HP lactate in the entire brain was likely due to baseline metabolism. We correlated our results with standard clinical brain MRI, MRI DCE perfusion, and in one case FDG PET/CT. Our results suggest that HP 13C pyruvate-to-lactate conversion may be a viable metabolic biomarker for assessing tumor response.Significance: Hyperpolarized pyruvate MRI enables metabolic imaging in the brain and can be a quantitative biomarker for active tumors.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/78/14/3755/F1.large.jpg Cancer Res; 78(14); 3755-60. ©2018 AACR.

Reproducibility and Repeatability of Semiquantitative 18F-Fluorodihydrotestosterone Uptake Metrics in Castration-Resistant Prostate Cancer Metastases: A Prospective Multicenter Study.

18F-fluorodihydrotestosterone (18F-FDHT) is a radiolabeled analog of the androgen receptor's primary ligand that is currently being credentialed as a biomarker for prognosis, response, and pharmacodynamic effects of new therapeutics. As part of the biomarker qualification process, we prospectively assessed its reproducibility and repeatability in men with metastatic castration-resistant prostate cancer. Methods: We conducted a prospective multiinstitutional study of metastatic castration-resistant prostate cancer patients undergoing 2 (test/retest) 18F-FDHT PET/CT scans on 2 consecutive days. Two independent readers evaluated all examinations and recorded SUVs, androgen receptor-positive tumor volumes, and total lesion uptake for the most avid lesion detected in each of 32 predefined anatomic regions. The relative absolute difference and reproducibility coefficient (RC) of each metric were calculated between the test and retest scans. Linear regression analyses, intraclass correlation coefficients (ICCs), and Bland-Altman plots were used to evaluate repeatability of 18F-FDHT metrics. The coefficient of variation and ICC were used to assess interobserver reproducibility. Results: Twenty-seven patients with 140 18F-FDHT-avid regions were included. The best repeatability among 18F-FDHT uptake metrics was found for SUV metrics (SUVmax, SUVmean, and SUVpeak), with no significant differences in repeatability among them. Correlations between the test and retest scans were strong for all SUV metrics (R2 ≥ 0.92; ICC ≥ 0.97). The RCs of the SUV metrics ranged from 21.3% (SUVpeak) to 24.6% (SUVmax). The test and retest androgen receptor-positive tumor volumes and TLU, respectively, were highly correlated (R2 and ICC ≥ 0.97), although variability was significantly higher than that for SUV (RCs > 46.4%). The prostate-specific antigen levels, Gleason score, weight, and age did not affect repeatability, nor did total injected activity, uptake measurement time, or differences in uptake time between the 2 scans. Including the most avid lesion per patient, the 5 most avid lesions per patient, only lesions 4.2 mL or more, only lesions with an SUV of 4 g/mL or more, or normalizing of SUV to area under the parent plasma activity concentration-time curve did not significantly affect repeatability. All metrics showed high interobserver reproducibility (ICC > 0.98; coefficient of variation < 0.2%-10.8%). Conclusion: Uptake metrics derived from 18F-FDHT PET/CT show high repeatability and interobserver reproducibility.

CT Features of Ovarian Tumors: Defining Key Differences Between Serous Borderline Tumors and Low-Grade Serous Carcinomas.

OBJECTIVE: The objective of our study was to investigate whether the CT features of serous borderline tumors (SBTs) differ from those of low-grade serous carcinomas (LGSCs) and to evaluate if mutation status is associated with distinct CT phenotypes. MATERIALS AND METHODS: This retrospective study included 59 women, 37 with SBT and 22 with LGSC, who underwent CT before primary surgical resection. Thirty of 59 patients were genetically profiled. Two radiologists (readers 1 and 2) independently and retrospectively reviewed CT examinations for qualitative features and quantified total tumor volumes (TTVs), solid tumor volumes (STVs), and solid proportion of ovarian masses. Univariate and multivariate associations of the CT features with histopathologic diagnoses and mutations were evaluated, and interreader agreement was determined. RESULTS: At multivariate analysis, the presence of bilateral ovarian masses (p = 0.03), the presence of peritoneal disease (PD) (p = 0.002), and higher STV of ovarian masses (p = 0.002) were associated with LGSC. The presence of nodular PD pattern (p < 0.001 each reader) and the presence of PD calcifications (reader 1, p = 0.02; reader 2, p = 0.003) were associated with invasive peritoneal lesions (i.e., LGSC). The presence of bilateral ovarian masses (p = 0.04 each reader), PD (reader 1, p = 0.01; reader 2, p = 0.004), and higher STV (p = 0.03 for each reader) were associated with the absence of BRAF mutation (i.e., wild type [wt]-BRAF). CONCLUSION: The CT features of LGSCs were distinct from those of SBTs. The CT manifestations of LGSC and the wt-BRAF phenotype were similar.

Erratum to: Fertility-sparing for young patients with gynecologic cancer: How MRI can guide patient selection prior to conservative management.

The original version of this article unfortunately contained mistakes. The figures 7D, 7E and 7F were missing in the article and arrows were missing in the figures 6C, 8B and 11C. The year of publication and volume number for references 19, 79 and 87 have been updated. Also, the Table 2 layout has been improved for better readability. The Publisher apologizes for the mistakes and the inconvenience caused.

High-Grade Serous Ovarian Cancer: Associations between BRCA Mutation Status, CT Imaging Phenotypes, and Clinical Outcomes.

Purpose To investigate the associations between BRCA mutation status and computed tomography (CT) phenotypes of high-grade serous ovarian cancer (HGSOC) and to evaluate CT indicators of cytoreductive outcome and survival in patients with BRCA-mutant HGSOC and those with BRCA wild-type HGSOC. Materials and Methods This HIPAA-compliant, institutional review board-approved retrospective study included 108 patients (33 with BRCA mutant and 75 with BRCA wild-type HGSOC) who underwent CT before primary debulking. Two radiologists independently reviewed the CT findings for various qualitative CT features. Associations between CT features, BRCA mutation status, cytoreductive outcome, and progression-free survival (PFS) were evaluated by using logistic regression and Cox proportional hazards regression, respectively. Results Peritoneal disease (PD) pattern, presence of PD in gastrohepatic ligament, mesenteric involvement, and supradiaphragmatic lymphadenopathy at CT were associated with BRCA mutation status (multiple regression: P < .001 for each CT feature). While clinical and CT features were not associated with cytoreductive outcome for patients with BRCA-mutant HGSOC, presence of PD in lesser sac (odds ratio [OR] = 2.40) and left upper quadrant (OR = 1.19), mesenteric involvement (OR = 7.10), and lymphadenopathy in supradiaphragmatic (OR = 2.83) and suprarenal para-aortic (OR = 4.79) regions were associated with higher odds of incomplete cytoreduction in BRCA wild-type HGSOC (multiple regression: P < .001 each CT feature). Mesenteric involvement at CT was associated with significantly shorter PFS for both patients with BRCA-mutant HGSOC (multiple regression: hazard ratio [HR] = 26.7 P < .001) and those with BRCA wild-type HGSOC (univariate analysis: reader 1, HR = 2.42, P < .001; reader 2, HR = 2.61; P < .001). Conclusion Qualitative CT features differed between patients with BRCA-mutant HGSOC and patients with BRCA wild-type HGSOC. CT indicators of cytoreductive outcome varied according to BRCA mutation status. Mesenteric involvement at CT was an indicator of significantly shorter PFS for both patients with BRCA-mutant HGSOC and those with BRCA wild-type HGSOC. © RSNA, 2017 Online supplemental material is available for this article.

Fertility-sparing for young patients with gynecologic cancer: How MRI can guide patient selection prior to conservative management.

Historically, cancer treatment has emphasized measures for the "cure" regardless of the long-term consequences. Advances in cancer detection and treatment have resulted in improved outcomes bringing to the fore various quality of life considerations including future fertility. For many young cancer patients, fertility preservation is now an integral component of clinical decision-making and treatment design. Optimal fertility-sparing options for young patients with gynecologic cancer are influenced by patient age, primary cancer, treatment regimens, and patient preferences. Possible approaches include embryo or oocyte cryopreservation, ovarian transposition, conservative surgery, and conservative medical treatment to delay radical surgery. These may be used alone or in combination to maximize fertility preservation. Awareness of the various fertility-sparing options, eligibility criteria, and the central role of magnetic resonance imaging in the proper selection of patients will enable radiologists to produce complete clinically relevant imaging reports and serve as effective consultants to referring clinicians. Knowledge of the potential imaging pitfalls is essential to avoid misinterpretation and guide appropriate management.

A novel representation of inter-site tumour heterogeneity from pre-treatment computed tomography textures classifies ovarian cancers by clinical outcome.

PURPOSE: To evaluate the associations between clinical outcomes and radiomics-derived inter-site spatial heterogeneity metrics across multiple metastatic lesions on CT in patients with high-grade serous ovarian cancer (HGSOC). METHODS: IRB-approved retrospective study of 38 HGSOC patients. All sites of suspected HGSOC involvement on preoperative CT were manually segmented. Gray-level correlation matrix-based textures were computed from each tumour site, and grouped into five clusters using a Gaussian Mixture Model. Pairwise inter-site similarities were computed, generating an inter-site similarity matrix (ISM). Inter-site texture heterogeneity metrics were computed from the ISM and compared to clinical outcomes. RESULTS: Of the 12 inter-site texture heterogeneity metrics evaluated, those capturing the differences in texture similarities across sites were associated with shorter overall survival (inter-site similarity entropy, similarity level cluster shade, and inter-site similarity level cluster prominence; p ≤ 0.05) and incomplete surgical resection (similarity level cluster shade, inter-site similarity level cluster prominence and inter-site cluster variance; p ≤ 0.05). Neither the total number of disease sites per patient nor the overall tumour volume per patient was associated with overall survival. Amplification of 19q12 involving cyclin E1 gene (CCNE1) predominantly occurred in patients with more heterogeneous inter-site textures. CONCLUSION: Quantitative metrics non-invasively capturing spatial inter-site heterogeneity may predict outcomes in patients with HGSOC. KEY POINTS: • Calculating inter-site texture-based heterogeneity metrics was feasible • Metrics capturing texture similarities across HGSOC sites were associated with overall survival • Heterogeneity metrics were also associated with incomplete surgical resection of HGSOC.

Differentiation of Uterine Leiomyosarcoma from Atypical Leiomyoma: Diagnostic Accuracy of Qualitative MR Imaging Features and Feasibility of Texture Analysis.

PURPOSE: To investigate whether qualitative magnetic resonance (MR) features can distinguish leiomyosarcoma (LMS) from atypical leiomyoma (ALM) and assess the feasibility of texture analysis (TA). METHODS: This retrospective study included 41 women (ALM = 22, LMS = 19) imaged with MRI prior to surgery. Two readers (R1, R2) evaluated each lesion for qualitative MR features. Associations between MR features and LMS were evaluated with Fisher's exact test. Accuracy measures were calculated for the four most significant features. TA was performed for 24 patients (ALM = 14, LMS = 10) with uniform imaging following lesion segmentation on axial T2-weighted images. Texture features were pre-selected using Wilcoxon signed-rank test with Bonferroni correction and analyzed with unsupervised clustering to separate LMS from ALM. RESULTS: Four qualitative MR features most strongly associated with LMS were nodular borders, haemorrhage, "T2 dark" area(s), and central unenhanced area(s) (p ≤ 0.0001 each feature/reader). The highest sensitivity [1.00 (95%CI:0.82-1.00)/0.95 (95%CI: 0.74-1.00)] and specificity [0.95 (95%CI:0.77-1.00)/1.00 (95%CI:0.85-1.00)] were achieved for R1/R2, respectively, when a lesion had ≥3 of these four features. Sixteen texture features differed significantly between LMS and ALM (p-values: <0.001-0.036). Unsupervised clustering achieved accuracy of 0.75 (sensitivity: 0.70; specificity: 0.79). CONCLUSIONS: Combination of ≥3 qualitative MR features accurately distinguished LMS from ALM. TA was feasible. KEY POINTS: • Four qualitative MR features demonstrated the strongest statistical association with LMS. • Combination of ≥3 these features could accurately differentiate LMS from ALM. • Texture analysis was a feasible semi-automated approach for lesion categorization.