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Purpose: In breast cancer, improved treatment approaches that reduce injury to lung tissue and early diagnosis and intervention for lung toxicity are increasingly important in survivorship. The aims of this study are to (1) compare lung tissue radiographic changes in women treated with conventional photon radiation therapy and those treated with proton therapy (PT), (2) assess the volume of lung irradiated to 5 Gy (V5) and 20 Gy (V20) by treatment modality, and (3) quantify the effects of V5, V20, time, and smoking history on the severity of tissue radiographic changes. Patients and Methods: A prospective observational study of female breast cancer patients was conducted to monitor postradiation subclinical lung tissue radiographic changes. Repeated follow-up x-ray computed tomography scans were acquired through 2 years after treatment. In-house software was used to quantify an internally normalized measure of pulmonary tissue density change over time from the computed tomography scans, emphasizing the 6- and 12-month time points. Results: Compared with photon therapy, PT was associated with significantly lower lung V5 and V20. Lung V20 (but not V5) correlated significantly with increased subclinical lung tissue radiographic changes 6 months after treatment, and neither correlated with lung effects at 12 months. Significant lung tissue density changes were present in photon therapy patients at 6 and 12 months but not in PT patients. Significant lung tissue density change persisted at 12 months in ever-smokers but not in never-smokers. Conclusion: Patients treated with PT had significantly lower radiation exposure to the lungs and less statistically significant tissue density change, suggesting decreased injury and/or improved recovery compared to photon therapy. These findings motivate additional studies in larger, randomized, and more diverse cohorts to further investigate the contributions of treatment modality and smoking regarding the short- and long-term radiographic effects of radiation on lung tissue.
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Background: Precision estimation of cardiovascular risk remains the cornerstone of atherosclerotic cardiovascular disease (ASCVD) prevention. While coronary artery calcium (CAC) scoring is the best available non-invasive quantitative modality to evaluate risk of ASCVD, it excludes risk related to prior myocardial infarction, cardiomyopathy, and arrhythmia which are implicated in ASCVD. The high-dimensional and inter-correlated nature of ECG data makes it a good candidate for analysis using machine learning techniques and may provide additional prognostic information not captured by CAC. In this study, we aimed to develop a quantitative ECG risk score (eRiS) to predict major adverse cardiovascular events (MACE) alone, or when added to CAC. Further, we aimed to construct and validate a novel nomogram incorporating ECG, CAC and clinical factors for ASCVD. Methods: We analyzed 5,864 patients with at least 1 cardiovascular risk factor who underwent CAC scoring and a standard ECG as part of the CLARIFY study (ClinicalTrials.gov Identifier: NCT04075162). Events were defined as myocardial infarction, coronary revascularization, stroke or death. A total of 649 ECG features, consisting of measurements such as amplitude and interval measurements from all deflections in the ECG waveform (53 per lead and 13 overall) were automatically extracted using a clinical software (GE Muse™ Cardiology Information System, GE Healthcare). The data was split into 4 training (Str) and internal validation (Sv) sets [Str (1): Sv (1): 50:50; Str (2): Sv (2): 60:40; Str (3): Sv (3): 70:30; Str (4): Sv (4): 80:20], and the results were compared across all the subsets. We used the ECG features derived from Str to develop eRiS. A least absolute shrinkage and selection operator-Cox (LASSO-Cox) regularization model was used for data dimension reduction, feature selection, and eRiS construction. A Cox-proportional hazards model was used to assess the benefit of using an eRiS alone (Mecg), CAC alone (Mcac) and a combination of eRiS and CAC (Mecg+cac) for MACE prediction. A nomogram (Mnom) was further constructed by integrating eRiS with CAC and demographics (age and sex). The primary endpoint of the study was the assessment of the performance of Mecg, Mcac, Mecg+cac and Mnom in predicting CV disease-free survival in ASCVD. Findings: Over a median follow-up of 14 months, 494 patients had MACE. The feature selection strategy preserved only about 18% of the features that were consistent across the various strata (Str). The Mecg model, comprising of eRiS alone was found to be significantly associated with MACE and had good discrimination of MACE (C-Index: 0.7, p = <2e-16). eRiS could predict time-to MACE (C-Index: 0.6, p = <2e-16 across all Sv). The Mecg+cac model was associated with MACE (C-index: 0.71). Model comparison showed that Mecg+cac was superior to Mecg (p = 1.8e-10) or Mcac (p < 2.2e-16) alone. The Mnom, comprising of eRiS, CAC, age and sex was associated with MACE (C-index 0.71). eRiS had the most significant contribution, followed by CAC score and other clinical variables. Further, Mnom was able to identify unique patient risk-groups based on eRiS, CAC and clinical variables. Conclusion: The use of ECG features in conjunction with CAC may allow for improved prognostication and identification of populations at risk. Future directions will involve prospective validation of the risk score and the nomogram across diverse populations with a heterogeneity of treatment effects.