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In oncology, the feasibility of Cerenkov luminescence imaging (CLI) has been assessed by imaging superficial lymph nodes in a few patients undergoing diagnostic

BackgroundRandomised controlled trials (RCTs) evaluating ectopic pregnancy have different outcomes which are defined and measured in diverse ways, which limits their ability to inform evidence-based clinical practice.ObjectiveTo address methodological deficiencies in published RCTs and systematic reviews, this study has developed a core outcome set to guide future research in ectopic pregnancy.DesignTo identify potential outcomes, we performed a comprehensive literature review and interviews with individuals with lived experience in ectopic pregnancy. Potential core outcomes were then entered into a three-round Delphi survey. 154 participants from six continents, comprising healthcare professionals, researchers, and individuals with lived experience in ectopic pregnancy, completed all three rounds of the Delphi survey. Outcomes were prioritised at three consensus development meetings and recommendations were developed on how to report these outcomes where possible.SubjectsHealthcare professionals, researchers, and individuals with lived experience in ectopic pregnancyResultsSix outcomes reached full consensus, including treatment success, resolution time, number of additional interventions, adverse events, mortality and severe morbidity, and treatment satisfaction.ConclusionThe core outcome set with six outcomes for ectopic pregnancy will help standardise reporting of clinical trials, facilitate implementation of findings into clinical practice, and enhance patient centred care.

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.

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.This retrospective, single-center study included patients with fat-containing adnexal masses on MRI during 2008-2021. Two radiologists independently reviewed overall size (Size188 women (median age 35 years) with 163 benign and 25 malignant lesions were included. SizeOverall 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.

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.