Identifying Coronary Artery Calcification Using Chest X-ray Radiographs and Machine Learning: The Role of the Radiomics Score.

PURPOSE: To evaluate the ability of radiomics score (RS)-based machine learning to identify moderate to severe coronary artery calcium (CAC) on chest x-ray radiographs (CXR). MATERIALS AND METHODS: We included 559 patients who underwent a CAC scan with CXR obtained within 6 months and divided them into training (n = 391) and validation (n = 168) cohorts. We extracted radiomic features from annotated cardiac contours in the CXR images and developed an RS through feature selection with the least absolute shrinkage and selection operator regression in the training cohort. We evaluated the incremental value of the RS in predicting CAC scores when combined with basic clinical factor in the validation cohort. To predict a CAC score ≥100, we built an RS-based machine learning model using random forest; the input variables were age, sex, body mass index, and RS. RESULTS: The RS was the most prominent factor for the CAC score ≥100 predictions (odds ratio = 2.33; 95% confidence interval: 1.62-3.44; P < 0.001) compared with basic clinical factor. The machine learning model was tested in the validation cohort and showed an area under the receiver operating characteristic curve of 0.808 (95% confidence interval: 0.75-0.87) for a CAC score ≥100 predictions. CONCLUSIONS: The use of an RS-based machine learning model may have the potential as an imaging marker to screen patients with moderate to severe CAC scores before diagnostic imaging tests, and it may improve the pretest probability of detecting coronary artery disease in clinical practice.

Prediction of the development of new coronary atherosclerotic plaques with radiomics.

BACKGROUND: Radiomics is expected to identify imaging features beyond the human eye. We investigated whether radiomics can identify coronary segments that will develop new atherosclerotic plaques on coronary computed tomography angiography (CCTA). METHODS: From a prospective multinational registry of patients with serial CCTA studies at ≥ 2-year intervals, segments without identifiable coronary plaque at baseline were selected and radiomic features were extracted. Cox models using clinical risk factors (Model 1), radiomic features (Model 2) and both clinical risk factors and radiomic features (Model 3) were constructed to predict the development of a coronary plaque, defined as total PV â€‹≥ â€‹1 â€‹mm3, at follow-up CCTA in each segment. RESULTS: In total, 9583 normal coronary segments were identified from 1162 patients (60.3 â€‹± â€‹9.2 years, 55.7% male) and divided 8:2 into training and test sets. At follow-up CCTA, 9.8% of the segments developed new coronary plaque. The predictive power of Models 1 and 2 was not different in both the training and test sets (C-index [95% confidence interval (CI)] of Model 1 vs. Model 2: 0.701 [0.690-0.712] vs. 0.699 [0.0.688-0.710] and 0.696 [0.671-0.725] vs. 0.0.691 [0.667-0.715], respectively, all p â€‹> â€‹0.05). The addition of radiomic features to clinical risk factors improved the predictive power of the Cox model in both the training and test sets (C-index [95% CI] of Model 3: 0.772 [0.762-0.781] and 0.767 [0.751-0.787], respectively, all p â€‹< â€‹00.0001 compared to Models 1 and 2). CONCLUSION: Radiomic features can improve the identification of segments that would develop new coronary atherosclerotic plaque. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov NCT0280341.

Gefitinib-induced paronychia: response to autologous platelet-rich plasma.

BACKGROUND: Paronychia has been reported in as many as 10% of patients treated with gefitinib. Although conservative management and treatment with topical or systemic antibiotics are beneficial, no effective method exists for intractable cases. Platelet-rich plasma (PRP)consists of a high concentration of platelets that promote wound healing through chemotaxis, cell proliferation,angiogenesis, and tissue remodeling. OBSERVATIONS: We herein report a refractory case of gefitinib-induced paronychia successfully treated with autologous PRP. A 68-year-old woman who had been diagnosed as having lung adenocarcinoma with multiple bone and brain metastases initiated gefitinib therapy at an oral dose of 250 mg/d. After 1 month, multiple paronychia with periungual granulation appeared on the nailfold of the first, second, and third toenails of both feet.Because the paronychia recurred repeatedly despite use of a topical antibiotic, topical corticosteroid, and short term systemic antibiotic, she started PRP treatment. After 3 months, the lesion showed marked improvement with minimal pain or discharge. CONCLUSION: This case highlights the therapeutic challenges of using PRP to promote tissue repair in intractable gefitinib-induced paronychia and merits further investigation.