Neoadjuvant chemotherapy for high-grade serous ovarian cancer: radiologic-pathologic correlation of response assessment and predictors of progression.

PURPOSE: Neoadjuvant chemotherapy is often administered for high-grade serous ovarian carcinoma (HGSC) prior to cytoreductive surgery. We evaluated treatment response by CT (simplified peritoneal carcinomatosis index [S-PCI]), pathology (chemotherapy response score [CRS]), laboratory markers (serum CA-125), and surgical outcomes, to identify predictors of disease-free survival. METHODS: For this retrospective, HIPAA-compliant, IRB-approved study, we identified 396 women with HGSC receiving neoadjuvant chemotherapy between 2010 and 2019. Two hundred and ninety-nine patients were excluded (surgery not performed; imaging/pathology unavailable). Pre- and post-treatment abdominopelvic CTs were assigned CT S-PCI scores 0-24 (higher score indicating more tumor). Specimens were assigned CRS of 1-3 (minimal to complete response). Clinical data were obtained via chart review. Univariate, multivariate, and survival analyses were performed. RESULTS: Ninety-seven women were studied, with mean age of 65 years ± 10. Interreader agreement was good to excellent for CT S-PCI scores (ICC 0.64-0.77). Despite a significant decrease in CT S-PCI scores after treatment (p < 0.001), mean decrease in CT S-PCI did not differ significantly among CRS categories (p = 0.20) or between patients who were optimally versus suboptimally debulked (p = 0.29). In a survival analysis, lower CRS (more viable tumor) was associated with shorter time to progression (p < 0.001). A joint Cox proportional-hazard models showed that only residual pathologic disease (CRS 1/2) (HR 4.19; p < 0.001) and change in CA-125 (HR 1.79; p = 0.01) predicted progression. CONCLUSION: HGSC response to neoadjuvant therapy by CT S-PCI did not predict pathologic CRS score, optimal debulking, or progression, revealing discordance between imaging, pathologic, biochemical, and surgical assessments of tumor response.

Imaging Care for Transgender and Gender Diverse Patients: Best Practices and Recommendations.

Transgender and gender diverse (TGD) people experience health disparities, and many avoid necessary medical care because of fears of discrimination or mistreatment. Disparate care is further compounded by limited understanding of gender-affirming hormone therapy (GAHT) and gender-affirming surgery among the medical community. Specific to radiology, TGD patients report more negative imaging experiences than negative general health encounters, highlighting the need for guidance and best practices for inclusive imaging care. A patient's imaging journey provides numerous opportunities for improvement. Inclusive practice in a radiology department starts with ordering and scheduling the examination, facilitated by staff education on appropriate use of a patient's chosen name, gender identity, and pronouns. Contemporary electronic health record systems have the capacity for recording detailed sexual orientation and gender identity data, but staff must be trained to solicit and use this information. A welcoming environment can help TGD patients to feel safe during the imaging experience and may include institutional nondiscrimination policies, gender-neutral signage, and all-gender single-user dressing rooms and bathrooms. Image acquisition should be performed using trauma-informed and patient-centered care. Finally, radiologists should be aware of reporting considerations for TGD patients, such as avoiding the use of gender in reports when it is not medically relevant and using precise, respectful language for findings related to GAHT and gender-affirming surgical procedures. As a field, radiology has a range of opportunities for improving care delivery for TGD patients, and the authors summarize recommended best practices. See the invited commentary by Stowell in this issue. © RSNA, 2023 Quiz questions for this article are available in the supplemental material.

A deep learning framework for automated detection and quantitative assessment of liver trauma.

BACKGROUND: Both early detection and severity assessment of liver trauma are critical for optimal triage and management of trauma patients. Current trauma protocols utilize computed tomography (CT) assessment of injuries in a subjective and qualitative (v.s. quantitative) fashion, shortcomings which could both be addressed by automated computer-aided systems that are capable of generating real-time reproducible and quantitative information. This study outlines an end-to-end pipeline to calculate the percentage of the liver parenchyma disrupted by trauma, an important component of the American Association for the Surgery of Trauma (AAST) liver injury scale, the primary tool to assess liver trauma severity at CT. METHODS: This framework comprises deep convolutional neural networks that first generate initial masks of both liver parenchyma (including normal and affected liver) and regions affected by trauma using three dimensional contrast-enhanced CT scans. Next, during the post-processing step, human domain knowledge about the location and intensity distribution of liver trauma is integrated into the model to avoid false positive regions. After generating the liver parenchyma and trauma masks, the corresponding volumes are calculated. Liver parenchymal disruption is then computed as the volume of the liver parenchyma that is disrupted by trauma. RESULTS: The proposed model was trained and validated on an internal dataset from the University of Michigan Health System (UMHS) including 77 CT scans (34 with and 43 without liver parenchymal trauma). The Dice/recall/precision coefficients of the proposed segmentation models are 96.13/96.00/96.35% and 51.21/53.20/56.76%, respectively, in segmenting liver parenchyma and liver trauma regions. In volume-based severity analysis, the proposed model yields a linear regression relation of 0.95 in estimating the percentage of liver parenchyma disrupted by trauma. The model shows an accurate performance in avoiding false positives for patients without any liver parenchymal trauma. These results indicate that the model is generalizable on patients with pre-existing liver conditions, including fatty livers and congestive hepatopathy. CONCLUSION: The proposed algorithms are able to accurately segment the liver and the regions affected by trauma. This pipeline demonstrates an accurate performance in estimating the percentage of liver parenchyma that is affected by trauma. Such a system can aid critical care medical personnel by providing a reproducible quantitative assessment of liver trauma as an alternative to the sometimes subjective AAST grading system that is used currently.

Characteristics of gadolinium-based contrast media cancellation at the point of care: a 15-month assessment of FDA-inspired medication guides on gadolinium retention.

PURPOSE: To assess the effect of United States Food and Drug Administration (FDA)-inspired patient handouts, regarding gadolinium retention, on the frequency of patient-initiated cancellations of gadolinium-based contrast media (GBCM) at the point of care. METHODS: This is a single center retrospective cohort review of MRI examinations protocolled to receive GBCM but performed without GBCM from October 2017 to January 2019. The study period spans an FDA-inspired safety intervention (December 2017 to May 2018) to provide patients with a handout on gadolinium retention. Examinations were coded according to rationale for GBCM cancellation. Process control charts were created to statistically evaluate trends in GBCM cancellations over time. RESULTS: From October 2017 to January 2019, GBCM was canceled at the point of care for 0.22% (131/58,837; 95% CI 0.19-0.26) of examinations in which GBCM was indicated. This did not significantly vary during the study period. No cancellations (0/131) were due to stated concern for gadolinium retention. However, 38% (50/131; 95% CI 30-47) were canceled due to kidney impairment and 21% (28/131; 95% CI 13-30) were canceled for an unknown reason. CONCLUSION: FDA-inspired handouts informing patients about gadolinium retention did not significantly change the frequency of GBCM cancellation. GBCM cancellations at the point of care remain uncommon.