Nivolumab for mismatch-repair-deficient or hypermutated gynecologic cancers: a phase 2 trial with biomarker analyses.

Programmed death-1 (PD-1) inhibitors are approved for therapy of gynecologic cancers with DNA mismatch repair deficiency (dMMR), although predictors of response remain elusive. We conducted a single-arm phase 2 study of nivolumab in 35 patients with dMMR uterine or ovarian cancers. Co-primary endpoints included objective response rate (ORR) and progression-free survival at 24 weeks (PFS24). Secondary endpoints included overall survival (OS), disease control rate (DCR), duration of response (DOR) and safety. Exploratory endpoints included biomarkers and molecular correlates of response. The ORR was 58.8% (97.5% confidence interval (CI): 40.7-100%), and the PFS24 rate was 64.7% (97.5% one-sided CI: 46.5-100%), meeting the pre-specified endpoints. The DCR was 73.5% (95% CI: 55.6-87.1%). At the median follow-up of 42.1 months (range, 8.9-59.8 months), median OS was not reached. One-year OS rate was 79% (95% CI: 60.9-89.4%). Thirty-two patients (91%) had a treatment-related adverse event (TRAE), including arthralgia (n = 10, 29%), fatigue (n = 10, 29%), pain (n = 10, 29%) and pruritis (n = 10, 29%); most were grade 1 or grade 2. Ten patients (29%) reported a grade 3 or grade 4 TRAE; no grade 5 events occurred. Exploratory analyses show that the presence of dysfunctional (CD8+PD-1+) or terminally dysfunctional (CD8+PD-1+TOX+) T cells and their interaction with programmed death ligand-1 (PD-L1)+ cells were independently associated with PFS24. PFS24 was associated with presence of MEGF8 or SETD1B somatic mutations. This trial met its co-primary endpoints (ORR and PFS24) early, and our findings highlight several genetic and tumor microenvironment parameters associated with response to PD-1 blockade in dMMR cancers, generating rationale for their validation in larger cohorts.ClinicalTrials.gov identifier: NCT03241745 .

The Prevalence and Radiologic Features of Renal Cancers Associated with FLCN, BAP1, SDH, and MET Germline Mutations.

Purpose To investigate the prevalence of FLCN, BAP1, SDH, and MET mutations in an oncologic cohort and determine the prevalence, clinical features, and imaging features of renal cell carcinoma (RCC) associated with these mutations. Secondarily, to determine the prevalence of encountered benign renal lesions. Materials and Methods From 25 220 patients with cancer who prospectively underwent germline analysis with a panel of more than 70 cancer-predisposing genes from 2015 to 2021, patients with FLCN, BAP1, SDH, or MET mutations were retrospectively identified. Clinical records were reviewed for patient age, sex, race/ethnicity, and renal cancer diagnosis. If RCC was present, baseline CT and MRI examinations were independently assessed by two radiologists. Summary statistics were used to summarize continuous and categorical variables by mutation. Results A total of 79 of 25 220 (0.31%) patients had a germline mutation: FLCN, 17 of 25 220 (0.07%); BAP1, 22 of 25 220 (0.09%); SDH, 39 of 25 220 (0.15%); and MET, one of 25 220 (0.004%). Of these 79 patients, 18 (23%) were diagnosed with RCC (FLCN, four of 17 [24%]; BAP1, four of 22 [18%]; SDH, nine of 39 [23%]; MET, one of one [100%]). Most hereditary RCCs demonstrated ill-defined margins, central nonenhancing area (cystic or necrotic), heterogeneous enhancement, and various other CT and MR radiologic features, overlapping with the radiologic appearance of nonhereditary RCCs. The prevalence of other benign solid renal lesions (other than complex cysts) in patients was up to 11%. Conclusion FLCN, BAP1, SDH, and MET mutations were present in less than 1% of this oncologic cohort. Within the study sample size limits, imaging findings for hereditary RCC overlapped with those of nonhereditary RCC, and the prevalence of other associated benign solid renal lesions (other than complex cysts) was up to 11%. Keywords: Familial Renal Cell Carcinoma, Birt-Hogg-Dubé Syndrome, Carcinoma, Renal Cell, Paragangliomas, Urinary, Kidney © RSNA, 2024.

Improving risk stratification of indeterminate adnexal masses on MRI: What imaging features help predict malignancy in O-RADS MRI 4 lesions?

PURPOSE: Ovarian-Adnexal Reporting and Data System (O-RADS) MRI uses a 5-point scale to establish malignancy risk in sonographically-indeterminate adnexal masses. The management of O-RADS MRI score 4 lesions is challenging, as the prevalence of malignancy is widely variable (5-90%). We assessed imaging features that may sub-stratify O-RADS MRI 4 lesions into malignant and benign subgroups. METHOD: Retrospective single-institution study of women with O-RADS MRI score of 4 adnexal masses between April 2021-August 2022. Imaging findings were assessed independently by 2 radiologists according to the O-RADS lexicon white paper. MRI and clinical findingswere compared between malignant and benign adnexal masses, and inter-reader agreement was calculated. RESULTS: Seventy-four women (median age 52 years, IQR 36-61) were included. On pathology, 41 (55.4%) adnexal masses were malignant. Patients with malignant masses were younger (p = 0.02) with higher CA-125 levels (p = 0.03). Size of solid tissue was greater in malignant masses (p = 0.01-0.04). Papillary projections and larger solid portion were more common in malignant lesions; irregular septations and predominantly solid composition were more frequent in benign lesions (p < 0.01). Solid tissue of malignant lesions was more often hyperintense on T2-weighted and diffusion-weighted imaging (p ≤ 0.03). Other imaging findings were not significantly different (p = 0.09-0.77). Inter-reader agreement was excellent-good for most features (ICC = 0. 662-0.950; k = 0. 650-0.860). CONCLUSION: Various MRI and clinical features differed between malignant and benign O-RADS MRI score 4 adnexal masses. O-RADS MRI 4 lesions may be sub-stratified (high vs low risk) based on solid tissue characteristics and CA-125 levels.

Extraprostatic incidental findings on prostate mpMRI: A pictorial review from the ESUR junior network.

The role of multiparametric MRI (mpMRI) in prostate cancer setting is increasingly consolidated and, as a result, its usage in clinical practice is in exponential growth. However, beyond the prostate gland, several key structures are included in the field of view of mpMRI scans. Consequently, various extra-prostatic incidental findings (IFs) belonging to different anatomical systems can be accidentally recognized. Therefore, it is mandatory for a radiologist to be familiar with the wide range of pathologies potentially encountered, to guide management and avoid patient anxiety and costs due to additional work-up prompted by clinically insignificant extra-prostatic findings. With this pictorial review, we aim to illustrate a wide range of IFs that can be detected when performing mpMRI of the prostate, focusing on their imaging characteristics, differential diagnosis, and clinical relevance. Additionally, we propose the CheckDEEP, the Checklist for DEtection of ExtraProstatic findings, to be used for a thorough evaluation of target areas within each anatomical system.

A preoperative nomogram incorporating CT to predict the probability of ovarian clear cell carcinoma.

OBJECTIVES: To evaluate clinical, laboratory, and radiological variables from preoperative contrast-enhanced computed tomography (CECT) for their ability to distinguish ovarian clear cell carcinoma (OCCC) from non-OCCC and to develop a nomogram to preoperatively predict the probability of OCCC. METHODS: This IRB-approved, retrospective study included consecutive patients who underwent surgery for an ovarian tumor from 1/1/2000 to 12/31/2016 and CECT of the abdomen and pelvis ≤90 days before primary debulking surgery. Using a standardized form, two experienced oncologic radiologists independently analyzed imaging features and provided a subjective 5-point impression of the probability of the histological diagnosis. Nomogram models incorporating clinical, laboratory, and radiological features were created to predict histological diagnosis of OCCC over non-OCCC. RESULTS: The final analysis included 533 patients with surgically confirmed OCCC (n = 61) and non-OCCC (n = 472); history of endometriosis was more often found in patients with OCCC (20% versus 3.6%; p < 0.001), while CA-125 was significantly higher in patients with non-OCCC (351 ng/mL versus 70 ng/mL; p < 0.001). A nomogram model incorporating clinical (age, history of endometriosis and adenomyosis), laboratory (CA-125) and imaging findings (peritoneal implant distribution, morphology, laterality, and diameter of ovarian lesion and of the largest solid component) had an AUC of 0.9 (95% CI: 0.847, 0.949), which was comparable to the AUCs of the experienced radiologists' subjective impressions [0.8 (95% CI: 0.822, 0.891) and 0.9 (95% CI: 0.865, 0.936)]. CONCLUSIONS: A presurgical nomogram model incorporating readily accessible clinical, laboratory, and CECT variables was a powerful predictor of OCCC, a subtype often requiring a distinctive treatment approach.

A core outcome set for future research in ectopic pregnancy: an international consensus development study.

OBJECTIVE: To address methodological deficiencies in published randomized controlled trials and systematic reviews, this study has developed a core outcome set to guide future research in ectopic pregnancy (EP). DESIGN: To identify potential outcomes, we performed a comprehensive literature review and interviews with individuals with lived experience in EP. Potential core outcomes were then entered into a 3-round Delphi survey. A total of 154 participants from 6 continents, comprising health care professionals, researchers, and individuals with lived experience in EP, completed all 3 rounds of the Delphi survey. Outcomes were prioritized at 3 consensus development meetings, and recommendations were developed on how to report these outcomes where possible. SETTING: Not applicable. PATIENT(S): Health care professionals, researchers, and individuals with lived experience in EP. INTERVENTION(S): Not applicable. MAIN OUTCOME MEASURE(S): Consensus for inclusion in core outcome set. RESULT(S): Six outcomes reached full consensus, including treatment success, resolution time, the number of additional interventions, adverse events, mortality and severe morbidity, and treatment satisfaction. CONCLUSION(S): The core outcome set with 6 outcomes for EP will help standardize reporting of clinical trials, facilitate implementation of findings into clinical practice, and enhance patient-centered care.

Sarcomatoid renal cell carcinoma: MRI features and their association with survival.

OBJECTIVE: To evaluate MRI features of sarcomatoid renal cell carcinoma (RCC) and their association with survival. METHODS: This retrospective single-center study included 59 patients with sarcomatoid RCC who underwent MRI before nephrectomy during July 2003-December 2019. Three radiologists reviewed MRI findings of tumor size, non-enhancing areas, lymphadenopathy, and volume (and percentage) of T2 low signal intensity areas (T2LIA). Clinicopathological factors of age, gender, ethnicity, baseline metastatic status, pathological details (subtype and extent of sarcomatoid differentiation), treatment type, and follow-up were extracted. Survival was estimated using Kaplan-Meier method and Cox proportional-hazards regression model was used to identify factors associated with survival. RESULTS: Forty-one males and eighteen females (median age 62 years; interquartile range 51-68) were included. T2LIAs were present in 43 (72.9%) patients. At univariate analysis, clinicopathological factors associated with shorter survival were: greater tumor size (> 10 cm; HR [hazard ratio] = 2.44, 95% CI 1.15-5.21; p = 0.02), metastatic lymph nodes (present; HR = 2.10, 95% CI 1.01-4.37; p = 0.04), extent of sarcomatoid differentiation (non-focal; HR = 3.30, 95% CI 1.55-7.01; p < 0.01), subtypes other than clear cell, papillary, or chromophobe (HR = 3.25, 95% CI 1.28-8.20; p = 0.01), and metastasis at baseline (HR = 5.04, 95% CI 2.40-10.59; p < 0.01). MRI features associated with shorter survival were: lymphadenopathy (HR = 2.24, 95% CI 1.16-4.71; p = 0.01) and volume of T2LIA (> 3.2 mL, HR = 4.22, 95% CI 1.92-9.29); p < 0.01). At multivariate analysis, metastatic disease (HR = 6.89, 95% CI 2.79-16.97; p < 0.01), other subtypes (HR = 9.50, 95% CI 2.81-32.13; p < 0.01), and greater volume of T2LIA (HR = 2.51, 95% CI 1.04-6.05; p = 0.04) remained independently associated with worse survival. CONCLUSION: T2LIAs were present in approximately two thirds of sarcomatoid RCCs. Volume of T2LIA along with clinicopathological factors were associated with survival.

Ovarian cancer during pregnancy.

Adnexal masses during pregnancy are a relatively uncommon entity. Their clinical management is challenging given the overlapping features of certain entities on imaging and histopathology, which can mimic malignancy, and the potential side effects to the mother and fetus, whether expectant management versus surgery is pursued. Ultrasonography with Doppler evaluation is the modality of choice for evaluating adnexal masses during pregnancy. Magnetic resonance imaging is the second-line modality useful when US findings are inconclusive/indeterminate. Most adnexal masses in pregnant patients are benign in origin (e.g., functional cysts, mature cystic teratoma, decidualization of endometrioma), but a few are malignant in origin (e.g., dysgerminoma, granulosa cell tumor). Most cases of adnexal masses are asymptomatic, but complications such as ovarian torsion can occur. This review aims to familiarize the radiologist with the imaging of adnexal lesions during pregnancy so that the radiologist can identify ovarian cancer. Specifically, the review will detail the most common benign and malignant adnexal masses in pregnancy, mimickers, and their corresponding imaging findings on US and MRI.

Fat-containing adnexal masses on MRI: solid tissue volume and fat distribution as a guide for O-RADS Score assignment.

PURPOSE: To explore ways to improve O-RADS MRI scoring for fat-containing adnexal masses, by investigating methods for quantifying solid tissue volume and fat distribution and evaluating their associations with malignancy. METHODS: This retrospective, single-center study included patients with fat-containing adnexal masses on MRI during 2008-2021. Two radiologists independently reviewed overall size (Sizeoverall), size of any solid tissue (Sizeanysolid), size of solid tissue that was not Rokitansky nodule (Sizenon-Rokitansky), and fat distribution. Wilcoxon test, Fisher-exact test, and ROC curve analysis were performed. Reference standard was pathology or follow-up > 24 months. RESULTS: 188 women (median age 35 years) with 163 benign and 25 malignant lesions were included. Sizeoverall (R1, 9.9 cm vs 5.9 cm; R2, 12.4 cm vs 6.0 cm), Sizeanysolid (R1, 5.1 cm vs 1.2 cm; R2, 3.2 cm vs 0.0 cm), Sizenon-Rokitansky (R1, 5.1 cm vs 0.0 cm; R2, 3.1 cm vs 0.0 cm), and fat distribution differed significantly between malignant and benign lesions (p < 0.01). Area under ROC curve was greatest using Sizenon-Rokitansky (R1, 0.83; R2, 0.86) vs Sizeoverall (R1, 0.78; R2, 0.81) or Sizeanysolid (R1, 0.79; R2, 0.81), though differences were non-significant (p = 0.48-0.93). Cutoffs for Sizenon-Rokitansky (R1, ≥ 1.2 cm; R2, ≥ 1.0 cm) yielded sensitivity and specificity of 0.72 and 0.93 (R1) and 0.76 and 0.95 (R2). Among immature teratomas, 85.7% displayed scattered fat. CONCLUSION: Overall size, size of (any or non-Rokitansky-nodule) solid tissue, and fat distribution differed between benign and malignant fat-containing adnexal masses. Incorporating these would constitute simple and practical approaches to refining O-RADS MRI scoring.

Advances in MRI-Based Assessment of Rectal Cancer Post-Neoadjuvant Therapy: A Comprehensive Review.

Rectal cancer presents significant diagnostic and therapeutic challenges, with neoadjuvant therapy playing a pivotal role in improving resectability and patient outcomes. MRI serves as a critical tool in assessing treatment response. However, differentiating viable tumor tissue from therapy-induced changes on MRI remains a complex task. In this comprehensive review, we explore treatment options for rectal cancer based on resectability status, focusing on the role of MRI in guiding therapeutic decisions. We delve into the nuances of MRI-based evaluation of treatment response following neoadjuvant therapy, paying particular attention to emerging techniques like radiomics. Drawing from our insights based on the literature, we provide essential recommendations for post-neoadjuvant therapy management of rectal cancer, all within the context of MRI-based findings.

Characterization of Ovarian Granulosa Cell Tumors using Magnetic Resonance Imaging.

Purpose: To identify the MRI features that aid in the characterization of ovarian granulosa cell tumors. Materials and methods: 11 MR pelvis of an adult woman with pathology-proven ovarian granulosa cell tumors with surgical pathology.We evaluated the patient's age, Ca-125, size, laterality, and with MRI features such as indirect signs (i.e., thickened endometrium > 0.9 cm), morphology (cystic, solid-cystic, or solid), subacute hemorrhage, T2 signal (low or intermediate-to-high), restricted diffusion (B values: 0, 50, 1000 sec/mm3/ADC), and dynamic enhancement (intense or similar to myometrium). Also, the presence of ascites, peritoneal implants, or adenopathy. Results: The final cohort included 11 women with a surgical-pathological diagnosis of granulosa cell tumors. The median age was 52.4 years (range, 17-80). The Ca-125 level was with a median within normal limits. The median size was 9.4 cm. Most cases were unilateral (81.8%) and more frequent on the left (54.5%). MRI Analysis: 36.4% had endometrial thickening. Ovarian granulosa cell tumors were polymorphous: cystic (54.6%), mixed solid-cystic (9.1%), and solid (36.3%). Most GC had intermediate to high signal on T2 (90.9%), restricted diffusion (81.8%), intense enhancement (81.8%), and 36.4% had intraparenchymal bleeding. 9.1% had associated implants/adenopathy/ascites at diagnosis. Conclusion: The MRI features characteristic of ovarian granulosa cell tumors were the polymorphous morphology, an intense enhancement to the myometrium, restricted diffusion, and the presence of intraparenchymal hemorrhage.

Natural Language Processing of Computed Tomography Reports to Label Metastatic Phenotypes With Prognostic Significance in Patients With Colorectal Cancer.

PURPOSE: Natural language processing (NLP) applied to radiology reports can help identify clinically relevant M1 subcategories of patients with colorectal cancer (CRC). The primary purpose was to compare the overall survival (OS) of CRC according to American Joint Committee on Cancer TNM staging and explore an alternative classification. The secondary objective was to estimate the frequency of metastasis for each organ. METHODS: Retrospective study of CRC who underwent computed tomography (CT) chest, abdomen, and pelvis between July 1, 2009, and March 26, 2019, at a tertiary cancer center, previously labeled for the presence or absence of metastasis by an NLP prediction model. Patients were classified in M0, M1a, M1b, and M1c (American Joint Committee on Cancer), or an alternative classification on the basis of the metastasis organ number: M1, single; M2, two; M3, three or more organs. Cox regression models were used to estimate hazard ratios; Kaplan-Meier curves were used to visualize survival curves using the two M1 subclassifications. RESULTS: Nine thousand nine hundred twenty-eight patients with a total of 48,408 CT chest, abdomen, and pelvis reports were included. On the basis of NLP prediction, the median OS of M1a, M1b, and M1c was 4.47, 1.72, and 1.52 years, respectively. The median OS of M1, M2, and M3 was 4.24, 2.05, and 1.04 years, respectively. Metastases occurred most often in liver (35.8%), abdominopelvic lymph nodes (32.9%), lungs (29.3%), peritoneum (22.0%), thoracic nodes (19.9%), bones (9.2%), and pelvic organs (7.5%). Spleen and adrenal metastases occurred in < 5%. CONCLUSION: NLP applied to a large radiology report database can identify clinically relevant metastatic phenotypes and be used to investigate new M1 substaging for CRC. Patients with three or more metastatic disease organs have the worst prognosis, with an OS of 1 year.

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.

Prospective testing of clinical Cerenkov luminescence imaging against standard-of-care nuclear imaging for tumour location.

In oncology, the feasibility of Cerenkov luminescence imaging (CLI) has been assessed by imaging superficial lymph nodes in a few patients undergoing diagnostic 18F-fluoro-2-deoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT). However, the weak luminescence signal requires the removal of ambient light. Here we report the development of a clinical CLI fiberscope with a lightproof enclosure, and the clinical testing of the setup using five different radiotracers. In an observational prospective trial (ClinicalTrials.gov identifier NCT03484884 ) involving 96 patients with existing or suspected tumours, scheduled for routine clinical FDG PET or 131I therapy, the level of agreement of CLI with standard-of-care imaging (PET or planar single-photon emission CT) for tumour location was 'acceptable' or higher (≥3 in the 1-5 Likert scale) for 90% of the patients. CLI correlated with the concentration of radioactive activity, and captured therapeutically relevant information from patients undergoing targeted radiotherapy or receiving the alpha emitter 223Ra, which cannot be feasibly imaged clinically. CLI could supplement radiological scans, especially when scanner capacity is limited.

Developing a Cancer Digital Twin: Supervised Metastases Detection From Consecutive Structured Radiology Reports.

The development of digital cancer twins relies on the capture of high-resolution representations of individual cancer patients throughout the course of their treatment. Our research aims to improve the detection of metastatic disease over time from structured radiology reports by exposing prediction models to historical information. We demonstrate that Natural language processing (NLP) can generate better weak labels for semi-supervised classification of computed tomography (CT) reports when it is exposed to consecutive reports through a patient's treatment history. Around 714,454 structured radiology reports from Memorial Sloan Kettering Cancer Center adhering to a standardized departmental structured template were used for model development with a subset of the reports included for validation. To develop the models, a subset of the reports was curated for ground-truth: 7,732 total reports in the lung metastases dataset from 867 individual patients; 2,777 reports in the liver metastases dataset from 315 patients; and 4,107 reports in the adrenal metastases dataset from 404 patients. We use NLP to extract and encode important features from the structured text reports, which are then used to develop, train, and validate models. Three models-a simple convolutional neural network (CNN), a CNN augmented with an attention layer, and a recurrent neural network (RNN)-were developed to classify the type of metastatic disease and validated against the ground truth labels. The models use features from consecutive structured text radiology reports of a patient to predict the presence of metastatic disease in the reports. A single-report model, previously developed to analyze one report instead of multiple past reports, is included and the results from all four models are compared based on accuracy, precision, recall, and F1-score. The best model is used to label all 714,454 reports to generate metastases maps. Our results suggest that NLP models can extract cancer progression patterns from multiple consecutive reports and predict the presence of metastatic disease in multiple organs with higher performance when compared with a single-report-based prediction. It demonstrates a promising automated approach to label large numbers of radiology reports without involving human experts in a time- and cost-effective manner and enables tracking of cancer progression over time.

Patterns of Metastatic Disease in Patients with Cancer Derived from Natural Language Processing of Structured CT Radiology Reports over a 10-year Period.

Background Patterns of metastasis in cancer are increasingly relevant to prognostication and treatment planning but have historically been documented by means of autopsy series. Purpose To show the feasibility of using natural language processing (NLP) to gather accurate data from radiology reports for assessing spatial and temporal patterns of metastatic spread in a large patient cohort. Materials and Methods In this retrospective longitudinal study, consecutive patients who underwent CT from July 2009 to April 2019 and whose CT reports followed a departmental structured template were included. Three radiologists manually curated a sample of 2219 reports for the presence or absence of metastases across 13 organs; these manually curated reports were used to develop three NLP models with an 80%-20% split for training and test sets. A separate random sample of 448 manually curated reports was used for validation. Model performance was measured by accuracy, precision, and recall for each organ. The best-performing NLP model was used to generate a final database of metastatic disease across all patients. For each cancer type, statistical descriptive reports were provided by analyzing the frequencies of metastatic disease at the report and patient levels. Results In 91 665 patients (mean age ± standard deviation, 61 years ± 15; 46 939 women), 387 359 reports were labeled. The best-performing NLP model achieved accuracies from 90% to 99% across all organs. Metastases were most frequently reported in abdominopelvic (23.6% of all reports) and thoracic (17.6%) nodes, followed by lungs (14.7%), liver (13.7%), and bones (9.9%). Metastatic disease tropism is distinct among common cancers, with the most common first site being bones in prostate and breast cancers and liver among pancreatic and colorectal cancers. Conclusion Natural language processing may be applied to cancer patients' CT reports to generate a large database of metastatic phenotypes. Such a database could be combined with genomic studies and used to explore prognostic imaging phenotypes with relevance to treatment planning. © RSNA, 2021 Online supplemental material is available for this article.

New imaging modalities to distinguish rare uterine mesenchymal cancers from benign uterine lesions.

PURPOSE OF REVIEW: Uterine sarcomas are rare and are often challenging to differentiate on imaging from benign mimics, such as leiomyoma. As functional MRI techniques have improved and new adjuncts, such as machine learning and texture analysis, are now being investigated, it is helpful to be aware of the current literature on imaging features that may sometimes allow for preoperative distinction. RECENT FINDINGS: MRI, with both conventional and functional imaging, is the modality of choice for evaluating uterine mesenchymal tumors, especially in differentiating uterine leiomyosarcoma from leiomyoma through validated diagnostic algorithms. MRI is sometimes helpful in differentiating high-grade stromal sarcoma from low-grade stromal sarcoma or differentiating endometrial stromal sarcoma from endometrial carcinoma. However, imaging remains nonspecific for evaluating rarer neoplasms, such as uterine tumor resembling ovarian sex cord tumor or perivascular epithelioid cell tumor, primarily because of the small number and power of relevant studies. SUMMARY: Through advances in MRI techniques and novel investigational imaging adjuncts, such as machine learning and texture analysis, imaging differentiation of malignant from benign uterine mesenchymal tumors has improved and could help reduce morbidity relating to misdiagnosis or diagnostic delays.

The role of imaging in pelvic exenteration for gynecological cancers.

Pelvic exenteration (PE) is one of the most challenging gynecologic oncologic surgeries and is an overriding term for different procedures that entail radical en bloc resection of the female reproductive organs and removal of additional adjacent affected pelvic organs (bladder, rectum, anus, etc.) with concomitant surgical reconstruction to restore bodily functions. Multimodality cross-sectional imaging with MRI, PET/CT, and CT plays an integral part in treatment decision-making, not only for the appropriate patient selection but also for surveillance after surgery. The purpose of this review is to provide a brief background on pelvic exenteration in gynecologic cancers and to familiarize the reader with the critical radiological aspects in the evaluation of patients for this complex procedure. The focus of this review will be on how imaging can aid in treatment planning and guide management.

Breast lactating adenoma, an example of the utility of the radiological-pathological correlation.

Lactating adenomas are benign breast tumors, of which etiology, pathogenesis, and management are not yet fully evident in the literature. The primary goal of the radiological evaluation is to make the differential diagnosis with malignant conditions. We present a case of a 34-year-old pregnant woman referred to our service with a progressively increasing mass in the right breast, in whom the histopathology was consistent with a lactating adenoma.