Late presentation of myocardial infarction (MI) during the COVID-19 pandemic.

A 65-year-old man presented to emergency department with progressive worsening dyspnoea, which was preceded by crushing, substernal chest pain 3 weeks prior that lasted for over 2 days. At the time the patient thought that this was a symptom of COVID-19 so he stayed at home and self-quarantined, until his symptoms worsened to the point of needing hospitalisation. The patient was found to have had myocardial infarction, with coronary angiography showing 100% occlusion of the Left Anterior Descending artery (LAD). Medical management was recommended given late presentation and risk of reperfusion injury.

Machine learning highlights the deficiency of conventional dosimetric constraints for prevention of high-grade radiation esophagitis in non-small cell lung cancer treated with chemoradiation.

BACKGROUND AND PURPOSE: Radiation esophagitis is a clinically important toxicity seen with treatment for locally-advanced non-small cell lung cancer. There is considerable disagreement among prior studies in identifying predictors of radiation esophagitis. We apply machine learning algorithms to identify factors contributing to the development of radiation esophagitis to uncover previously unidentified criteria and more robust dosimetric factors. MATERIALS AND METHODS: We used machine learning approaches to identify predictors of grade ≥ 3 radiation esophagitis in a cohort of 202 consecutive locally-advanced non-small cell lung cancer patients treated with definitive chemoradiation from 2008 to 2016. We evaluated 35 clinical features per patient grouped into risk factors, comorbidities, imaging, stage, histology, radiotherapy, chemotherapy and dosimetry. Univariate and multivariate analyses were performed using a panel of 11 machine learning algorithms combined with predictive power assessments. RESULTS: All patients were treated to a median dose of 66.6 Gy at 1.8 Gy per fraction using photon (89.6%) and proton (10.4%) beam therapy, most often with concurrent chemotherapy (86.6%). 11.4% of patients developed grade ≥ 3 radiation esophagitis. On univariate analysis, no individual feature was found to predict radiation esophagitis (AUC range 0.45-0.55, p ≥ 0.07). In multivariate analysis, all machine learning algorithms exhibited poor predictive performance (AUC range 0.46-0.56, p ≥ 0.07). CONCLUSIONS: Contemporary machine learning algorithms applied to our modern, relatively large institutional cohort could not identify any reliable predictors of grade ≥ 3 radiation esophagitis. Additional patients are needed, and novel patient-specific and treatment characteristics should be investigated to develop clinically meaningful methods to mitigate this survival altering toxicity.

Predicting radiation pneumonitis in locally advanced stage II-III non-small cell lung cancer using machine learning.

BACKGROUND AND PURPOSE: Radiation pneumonitis (RP) is a radiotherapy dose-limiting toxicity for locally advanced non-small cell lung cancer (LA-NSCLC). Prior studies have proposed relevant dosimetric constraints to limit this toxicity. Using machine learning algorithms, we performed analyses of contributing factors in the development of RP to uncover previously unidentified criteria and elucidate the relative importance of individual factors. MATERIALS AND METHODS: We evaluated 32 clinical features per patient in a cohort of 203 stage II-III LA-NSCLC patients treated with definitive chemoradiation to a median dose of 66.6 Gy in 1.8 Gy daily fractions at our institution from 2008 to 2016. Of this cohort, 17.7% of patients developed grade ≥2 RP. Univariate analysis was performed using trained decision stumps to individually analyze statistically significant predictors of RP and perform feature selection. Applying Random Forest, we performed multivariate analysis to assess the combined performance of important predictors of RP. RESULTS: On univariate analysis, lung V20, lung mean, lung V10 and lung V5 were found to be significant RP predictors with the greatest balance of specificity and sensitivity. On multivariate analysis, Random Forest (AUC = 0.66, p = 0.0005) identified esophagus max (20.5%), lung V20 (16.4%), lung mean (15.7%) and pack-year (14.9%) as the most common primary differentiators of RP. CONCLUSIONS: We highlight Random Forest as an accurate machine learning method to identify known and new predictors of symptomatic RP. Furthermore, this analysis confirms the importance of lung V20, lung mean and pack-year as predictors of RP while also introducing esophagus max as an important RP predictor.

Automated data extraction and ensemble methods for predictive modeling of breast cancer outcomes after radiation therapy.

PURPOSE: The purpose of this study was to compare the effectiveness of ensemble methods (e.g., random forests) and single-model methods (e.g., logistic regression and decision trees) in predictive modeling of post-RT treatment failure and adverse events (AEs) for breast cancer patients using automatically extracted EMR data. METHODS: Data from 1967 consecutive breast radiotherapy (RT) courses at one institution between 2008 and 2015 were automatically extracted from EMRs and oncology information systems using extraction software. Over 230 variables were extracted spanning the following variable segments: patient demographics, medical/surgical history, tumor characteristics, RT treatment history, and AEs tracked using CTCAEv4.0. Treatment failure was extracted algorithmically by searching posttreatment encounters for evidence of local, nodal, or distant failure. Individual models were trained using decision trees, logistic regression, random forests, and boosted decision trees to predict treatment failures and AEs. Models were fit on 75% of the data and evaluated for probability calibration and area under the ROC curve (AUC) on the remaining test set. The impact of each variable segment was assessed by retraining without the segment and measuring change in AUC (ΔAUC). RESULTS: All AUC values were statistically significant (P < 0.05). Ensemble methods outperformed single-model methods across all outcomes. The best ensemble method outperformed decision trees and logistic regression by an average AUC of 0.053 and 0.034, respectively. Model probabilities were well calibrated as evidenced by calibration curves. Excluding the patient medical history variable segment led to the largest AUC reduction in all models (Average ΔAUC = -0.025), followed by RT treatment history (-0.021) and tumor information (-0.015). CONCLUSION: In this largest such study in breast cancer performed to date, automatically extracted EMR data provided a basis for reliable outcome predictions across multiple statistical methods. Ensemble methods provided substantial advantages over single-model methods. Patient medical history contributed the most to prediction quality.

A prospective study of proton reirradiation for recurrent and secondary soft tissue sarcoma.

BACKGROUND AND PURPOSE: Proton reirradiation for sarcoma has not been previously described. We hypothesized that this strategy would provide favorable toxicity and survival outcomes. MATERIAL AND METHODS: Patients with soft tissue sarcoma in a previously-irradiated field were enrolled on a prospective trial of proton reirradiation. The primary endpoint was provider-reported acute toxicity. Secondary endpoints included late toxicities, local control, and overall survival. RESULTS: 23 patients underwent proton reirradiation. Median time between radiation courses was 40.7months (range 10-272). No grade 4-5 toxicities were observed. One patient (4%) experienced acute grade 3 dysphagia. Common grade 2 acute toxicities were fatigue (26%), anorexia (17%), and urinary incontinence (13%). There were two grade 3 late wound infections (10%) and one grade 3 late wound complication (5%). Grade 2 late complications included lymphedema (10%), fracture (5%), and fibrosis (5%). At a median follow-up of 36months, the 3-year cumulative incidence of local failure was 41% (95% CI [20-63%]). Median overall survival and progression-free survival were 44 and 29months, respectively. In extremity patients, amputation was spared in 7/10 (70%). CONCLUSIONS: Proton reirradiation of recurrent/secondary soft tissue sarcomas is well tolerated. While longer follow-up is needed, early survival outcomes in this high-risk population are encouraging.

Cones respond to light in the absence of transducin ß subunit.

Mammalian cones respond to light by closing a cGMP-gated channel via a cascade that includes a heterotrimeric G-protein, cone transducin, comprising Gαt2, Gß3 and Gγt2 subunits. The function of Gßγ in this cascade has not been examined. Here, we investigate the role of Gß3 by assessing cone structure and function in Gß3-null mouse (Gnb3(-/-)). We found that Gß3 is required for the normal expression of its partners, because in the Gnb3(-/-) cone outer segments, the levels of Gαt2 and Gγt2 are reduced by fourfold to sixfold, whereas other components of the cascade remain unaltered. Surprisingly, Gnb3(-/-) cones produce stable responses with normal kinetics and saturating response amplitudes similar to that of the wild-type, suggesting that cone phototransduction can function efficiently without a Gß subunit. However, light sensitivity was reduced by approximately fourfold in the knock-out cones. Because the reduction in sensitivity was similar in magnitude to the reduction in Gαt2 level in the cone outer segment, we conclude that activation of Gαt2 in Gnb3(-/-) cones proceeds at a rate approximately proportional to its outer segment concentration, and that activation of phosphodiesterase and downstream cascade components is normal. These results suggest that the main role of Gß3 in cones is to establish optimal levels of transducin heteromer in the outer segment, thereby indirectly contributing to robust response properties.