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UNLABELLED: Continuous tobacco use in patients with cancer is linked to substantial healthcare costs due to increased risks and complications, whereas quitting smoking leads to improved treatment outcomes and cost reductions. Addressing the need for empirical evidence on the economic impact of smoking cessation, this study examined the association between smoking cessation and healthcare cost utilization among a sample of 930 patients with cancer treated at The University of Texas MD Anderson Cancer Centers Tobacco Research and Treatment Program (TRTP). Applying conditional quantile regression and propensity scores to address confounding, our findings revealed that abstinence achieved through the TRTP significantly reduced the median cost during a 3-month period post-quitting by $1,095 [β = -$1,095, P = 0.007, 95% confidence interval (CI), = (-$1,886 to -$304)]. Sensitivity analysis corroborated these conclusions, showing a pronounced cost reduction when outlier data were excluded. The long-term accrued cost savings from smoking cessation could potentially offset the cost of participation in the TRTP program, underscoring its cost effectiveness. An important implication of this study is that by reducing smoking rates, healthcare systems can more efficiently allocate resources, enhance patient health outcomes, and lessen the overall cancer burden. PREVENTION RELEVANCE: This study emphasizes the dual impact of smoking cessation programs in patients with cancer: quitting smoking and reducing healthcare costs. It highlights the importance of integrating cessation programs into cancer prevention strategies, ensuring both individual health benefits and broader, system-wide economic efficiencies. See related Spotlight, p. 197.

PURPOSE: To evaluate the performance of a biopsy decision support algorithmic model, the intelligent-augmented breast cancer risk calculator (iBRISK), on a multicenter patient dataset. MATERIALS AND METHODS: iBRISK was previously developed by applying deep learning to clinical risk factors and mammographic descriptors from 9700 patient records at the primary institution and validated using another 1078 patients. All patients were seen from March 2006 to December 2016. In this multicenter study, iBRISK was further assessed on an independent, retrospective dataset (January 2015-June 2019) from three major health care institutions in Texas, with Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions. Data were dichotomized and trichotomized to measure precision in risk stratification and probability of malignancy (POM) estimation. iBRISK score was also evaluated as a continuous predictor of malignancy, and cost savings analysis was performed. RESULTS: The iBRISK models accuracy was 89.5%, area under the receiver operating characteristic curve (AUC) was 0.93 (95% CI: 0.92, 0.95), sensitivity was 100%, and specificity was 81%. A total of 4209 women (median age, 56 years [IQR, 45-65 years]) were included in the multicenter dataset. Only two of 1228 patients (0.16%) in the low POM group had malignant lesions, while in the high POM group, the malignancy rate was 85.9%. iBRISK score as a continuous predictor of malignancy yielded an AUC of 0.97 (95% CI: 0.97, 0.98). Estimated potential cost savings were more than $420 million. CONCLUSION: iBRISK demonstrated high sensitivity in the malignancy prediction of BI-RADS 4 lesions. iBRISK may safely obviate biopsies in up to 50% of patients in low or moderate POM groups and reduce biopsy-associated costs.Keywords: Mammography, Breast, Oncology, Biopsy/Needle Aspiration, Radiomics, Precision Mammography, AI-augmented Biopsy Decision Support Tool, Breast Cancer Risk Calculator, BI-RADS 4 Mammography Risk Stratification, Overbiopsy Reduction, Probability of Malignancy (POM) Assessment, Biopsy-based Positive Predictive Value (PPV3) Supplemental material is available for this article. Published under a CC BY 4.0 license.See also the commentary by McDonald and Conant in this issue.

BACKGROUND: Telehealth restrictions were relaxed under the COVID-19 public health emergency. We examined telehealth use before and during the pandemic among patients with newly diagnosed cancers and the association between state policies and telehealth use. METHODS: The study cohort was constructed from Optums deidentified Clinformatics Data Mart and included patients with lymphoma, female breast cancer, colorectal cancer, prostate cancer, and lung cancer diagnosed between March 1, 2019, and March 31, 2021. We performed an interrupted time series analysis to examine the trend of cancer-related telehealth use within 1 month of diagnosis relative to the timing of the COVID-19 public health emergency and multivariable logistic regressions to examine factors-specifically, state parity laws and regulations on cross-state practice-associated with telehealth. RESULTS: Of 110 461 patients, the rate of telehealth use peaked at 33.4% in April 2020, then decreased to 12% to 15% between September 2020 and March 2021. Among the 53 982 patients diagnosed since March 2020, telehealth use was statistically significantly lower for privately insured patients residing in states with coverage-only parity or no or unspecified parity than those in states with coverage and payment parity (adjusted rate = 20.2%, 19.1%, and 23.3%, respectively). The adjusted rate was lower for patients in states with cross-state telehealth policy limitations than for those in states without restrictions (14.9% vs 17.8%). CONCLUSIONS: Telehealth use by patients diagnosed with cancer during the pandemic was higher among those living in states with more generous parity and less restrictive rules for cross-state practice. Policy makers contemplating whether to permanently relax certain telehealth policies must consider the impact on vulnerable patient populations who can benefit from telehealth.