Relationship Between Pretreatment Body Composition and Clinical Outcomes in Patients With Metastatic Renal Cell Carcinoma Receiving First-Line Ipilimumab Plus Nivolumab.

INTRODUCTION: Biomarkers are needed to identify patients with metastatic renal cell carcinoma (mRCC) most likely to benefit from immune checkpoint inhibitors. We examined associations between radiographically assessed body composition (BC) variables and body mass index (BMI) with clinical outcomes for patients with mRCC receiving first-line ipilimumab + nivolumab (ipi/nivo). PATIENTS AND METHODS: We retrospectively reviewed all patients with mRCC treated with first-line ipi/nivo at one institution before June 1, 2021 with an analyzable baseline computed tomography (CT) scan. BC variables (skeletal muscle index [SMI], subcutaneous adipose tissue index [SATI], and visceral adipose tissue index [VATI]) were measured using baseline CT scans. Relationships between BC variables and clinical outcomes were examined using Cox proportional hazard regression models. RESULTS: Ninety-nine patients were analyzed (74% male, 64% overweight/obese, 75% low SMI). Controlling for age, IMDC risk, and sex (for BMI analyses), high vs. low SMI (HR=2.433, CI: 1.397-4.238, P=.0017), high vs. low SATI (HR=1.641, CI: 1.023-2.632, P=.0398), and obese BMI (≥ 30 kg/m2) vs. normal/overweight BMI (<30 kg/m2) (HR=1.859, CI: 1.156-2.989, P=.0105) were significantly associated with progression-free survival (PFS). Median overall survival (OS) for low SMI patients was higher (42.74 months, CI: 26.84, NR) than median OS for high SMI patients (27.01 months, CI: 15.28, NR) (adjusted HR=1.728, CI: 0.909-3.285, P=.0952). No BC variables were significantly associated with OS or objective response rate. CONCLUSIONS: Low SMI and low SATI were associated with significantly better PFS for patients with mRCC receiving first-line ipi/nivo. Radiographic BC variables may be useful prognostic biomarkers in this setting.

Reconciling discordance between PI-RADS 4 lesions and targeted biopsy: Early experience of a multidisciplinary quality improvement protocol with PI-RADS 4 subcategorization.

PURPOSE: PI-RADS 4 lesions are considered to have a "high" likelihood of clinically-significant prostate cancer (csPCa). However, patients undergoing targeted biopsy have a range of histologic findings. Understanding discordant cases is critical to improve diagnostic accuracy and inform subsequent management. We studied early findings from implementation of a multidisciplinary Quality Improvement (QI) protocol for reconciling discordance and evaluate the potential heterogeneity of PI-RADS 4. METHODS: Patients with mpMRI PI-RADS 4 lesions undergoing fusion-targeted biopsy from January 2017 to May 2021 were retrospectively reviewed. The discordant targeted biopsy pathology (benign/GG1) was evaluated utilizing a QI protocol and all lesions were subcategorized based on ADC values. Positive Predictive Value (PPV) for PI-RADS 4 lesions overall and the Cancer Detection Rate (CDR) for subcategorized lesions were calculated. RESULTS: 248 patients with 286 lesions were reviewed. Prior to re-review, PI-RADS 4 PPV for ≥ GG1 and ≥ GG2 lesions were 0.55 and 0.34, increasing to 0.67 and 0.43 following reconciliation. Lesion subcategorization based on ADC value as higher suspicion (4+) and lower suspicion (4-) resulted in 158 and 117 lesions, with reverse-fusion analysis revealing that 61% and 17% of lesions contained csPCa, respectively. Subgroup analysis among PI-RADS 4+ lesions led to an increase in the CDR to 75% and 61% for ≥ GG1 and ≥ GG2. CONCLUSION: Use of multidisciplinary QI protocol to review discordance cases of PI-RADS 4 improves diagnostic accuracy and guides subsequent management. Our findings highlight the known heterogeneity of this category with reference to csPCa CDR, suggesting the potential value of PI-RADS 4 subcategorization.

Utility of a Rule-Based Algorithm in the Assessment of Standardized Reporting in PI-RADS.

RATIONALE AND OBJECTIVES: Adoption of the Prostate Imaging Reporting & Data System (PI-RADS) has been shown to increase detection of clinically significant prostate cancer on prostate mpMRI. We propose that a rule-based algorithm based on Regular Expression (RegEx) matching can be used to automatically categorize prostate mpMRI reports into categories as a means by which to assess for opportunities for quality improvement. MATERIALS AND METHODS: All prostate mpMRIs performed in the Duke University Health System from January 2, 2015, to January 29, 2021, were analyzed. Exclusion criteria were applied, for a total of 5343 male patients and 6264 prostate mpMRI reports. These reports were then analyzed by our RegEx algorithm to be categorized as PI-RADS 1 through PI-RADS 5, Recurrent Disease, or "No Information Available." A stratified, random sample of 502 mpMRI reports was reviewed by a blinded clinical team to assess performance of the RegEx algorithm. RESULTS: Compared to manual review, the RegEx algorithm achieved overall accuracy of 92.6%, average precision of 88.8%, average recall of 85.6%, and F1 score of 0.871. The clinical team also reviewed 344 cases that were classified as "No Information Available," and found that in 150 instances, no numerical PI-RADS score for any lesion was included in the impression section of the mpMRI report. CONCLUSION: Rule-based processing is an accurate method for the large-scale, automated extraction of PI-RADS scores from the text of radiology reports. These natural language processing approaches can be used for future initiatives in quality improvement in prostate mpMRI reporting with PI-RADS.

Reconciling Discordance Between Prostate Biopsy Histology and Magnetic Resonance Imaging Suspicion - Implementation of a Quality Improvement Protocol of Imaging Re-review and Reverse-fusion Target Analysis.

There is uncertainty with how to proceed when targeted prostate biopsy of suspicious multiparametric magnetic resonance imaging (mpMRI) lesions return without clinically significant prostate cancer (csPCa). While possible, there are error sources that could contribute to such discordance including the mpMRI read, mpMRI-ultrasound fusion, biopsy technique, and histologic classification. Consequences are potentially significant; mistakenly missing csPCa can lead to delays in curative treatment. Conversely, in cases of incorrect mpMRI interpretation, the patient may be subjected to unnecessary workup/burden. At our institution, we implemented a quality improvement (QI) initiative triggered after a discordant case occurs. This multidisciplinary review process incorporates mpMRI re-review and assessment of accurate lesion-sampling, termed "reverse-fusion." Herein, we describe the protocol, present sample cases, and discuss clinical implications.

Automated versus manual analysis of body composition measures on computed tomography in patients with bladder cancer.

PURPOSE: Manual measurement of body composition on computed tomography (CT) is time-consuming, limiting its clinical use. We validate a software program, Automatic Body composition Analyzer using Computed tomography image Segmentation (ABACS), for the automated measurement of body composition by comparing its performance to manual segmentation in a cohort of patients with bladder cancer. METHOD: We performed a retrospective analysis of 285 patients treated for bladder cancer at the Duke University Health System from 1996 to 2017. Abdominal CT images were manually segmented at L3 using Slice-O-Matic. Automated segmentation was performed with ABACS on the same L3-level images. Measures of interest were skeletal muscle (SM) area, subcutaneous adipose tissue (SAT) area, and visceral adipose tissue (VAT) area. SM index, SAT index, and VAT index were calculated by dividing component areas by patient height2 (m2). Patients were dichotomized as sarcopenic, having excessive subcutaneous fat, or having excessive visceral fat using published cut-off values. Agreement between manual and automated segmentation was assessed using the Pearson product-moment correlation coefficient (PPMCC), the interclass correlation coefficient (ICC3), and the kappa statistic (κ). RESULTS: There was strong agreement between manual and automatic segmentation, with PPMCCs > 0.90 and ICC3s > 0.90 for SM, SAT, and VAT areas. Categorization of patients as sarcopenic (κ = 0.73), having excessive subcutaneous fat (κ = 0.88), or having excessive visceral fat (κ = 0.90) displayed high agreement between methods. CONCLUSIONS: Automated segmentation of body composition measures on CT using ABACS performs similarly to manual analysis and may expedite data collection in body composition research.