Francesco Alessandrino1, Rahul Gujrathi1, Amin H Nassar2, Arwa Alzaghal1, Arvind Ravi3, Bradley McGregor2, Guru Sonpavde2, Atul B Shinagare4. 1. Department of Imaging, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. 2. Lank Center for Genitourinary Oncology, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, USA. 3. Lank Center for Genitourinary Oncology, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Broad Institute, Cambridge, MA, USA. 4. Department of Imaging, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: ashinagare@bwh.harvard.edu.
Abstract
BACKGROUND: Reliable biomarkers to predict the response of metastatic urothelial cancer (mUC) to programmed death-1 and programmed death-ligand 1 (PD-1/PD-L1) inhibitors are being investigated. Texture analysis represents tumor heterogeneity and may serve as a predictor of response in mUC. OBJECTIVE: To assess the predictive ability of computed tomography (CT) texture analysis for progression-free survival (PFS) in patients with mUC treated with PD-1/PD-L1 inhibitors. DESIGN, SETTING, AND PARTICIPANTS: Forty-two postplatinum patients with mUC treated with PD-1/PD-L1 inhibitors from 2013 to 2018, including those with measurable disease per RECIST 1.1 who had contrast-enhanced baseline or first follow-up CT within 3mo after starting treatment, were included. PFS was calculated based on serial follow-up CT scans. Eleven patients with follow-up of <12mo without progression were excluded. Texture features of measurable lesions on baseline and first follow-up CT were extracted using commercially available software (TexRAD; Feedback Plc, Cambridge, UK) using different spatial scaling factors (0, 2-6). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Stepwise logistic regression analysis was conducted to identify patients with PFS <12mo, and performance was assessed using receiver operator characteristic curves. RESULTS AND LIMITATIONS: Of 31 included patients, 18 had PFS <12mo. Twenty-five baseline CT and 29 first follow-up CT scans met the inclusion criteria. In patients with PFS <12mo, entropy and mean were higher on first follow-up CT (p=0.02 and p=0.005, respectively). A predictive model including mean and entropy on first follow-up CT yielded 95% sensitivity, 80% specificity, 90% positive predictive value, 89% negative predictive value, and 90% accuracy (area under the curve=0.963) to identify patients with PFS <12mo. Limitations include retrospective nature and small sample size. CONCLUSIONS: CT texture analysis can help predict early progression with high accuracy soon after starting PD-1/PD-L1 inhibitors. Studies investigating the correlation of texture analysis with survival endpoints may help validate texture analysis as a biomarker of PD-1/PD-L1 inhibitors' treatment response. PATIENT SUMMARY: Computed tomography texture analysis can help predict durability of response in patients with metastatic urothelial cancer early during treatment with programmed death-1 and programmed death-ligand 1 (PD-1/PD-L1) inhibitors.
BACKGROUND: Reliable biomarkers to predict the response of metastatic urothelial cancer (mUC) to programmed death-1 and programmed death-ligand 1 (PD-1/PD-L1) inhibitors are being investigated. Texture analysis represents tumor heterogeneity and may serve as a predictor of response in mUC. OBJECTIVE: To assess the predictive ability of computed tomography (CT) texture analysis for progression-free survival (PFS) in patients with mUC treated with PD-1/PD-L1 inhibitors. DESIGN, SETTING, AND PARTICIPANTS: Forty-two postplatinum patients with mUC treated with PD-1/PD-L1 inhibitors from 2013 to 2018, including those with measurable disease per RECIST 1.1 who had contrast-enhanced baseline or first follow-up CT within 3mo after starting treatment, were included. PFS was calculated based on serial follow-up CT scans. Eleven patients with follow-up of <12mo without progression were excluded. Texture features of measurable lesions on baseline and first follow-up CT were extracted using commercially available software (TexRAD; Feedback Plc, Cambridge, UK) using different spatial scaling factors (0, 2-6). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Stepwise logistic regression analysis was conducted to identify patients with PFS <12mo, and performance was assessed using receiver operator characteristic curves. RESULTS AND LIMITATIONS: Of 31 included patients, 18 had PFS <12mo. Twenty-five baseline CT and 29 first follow-up CT scans met the inclusion criteria. In patients with PFS <12mo, entropy and mean were higher on first follow-up CT (p=0.02 and p=0.005, respectively). A predictive model including mean and entropy on first follow-up CT yielded 95% sensitivity, 80% specificity, 90% positive predictive value, 89% negative predictive value, and 90% accuracy (area under the curve=0.963) to identify patients with PFS <12mo. Limitations include retrospective nature and small sample size. CONCLUSIONS: CT texture analysis can help predict early progression with high accuracy soon after starting PD-1/PD-L1 inhibitors. Studies investigating the correlation of texture analysis with survival endpoints may help validate texture analysis as a biomarker of PD-1/PD-L1 inhibitors' treatment response. PATIENT SUMMARY: Computed tomography texture analysis can help predict durability of response in patients with metastatic urothelial cancer early during treatment with programmed death-1 and programmed death-ligand 1 (PD-1/PD-L1) inhibitors.
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