Using Machine Learning to Predict Response to Image-guided Therapies for Hepatocellular Carcinoma.

Interventional oncology is a rapidly growing field with advances in minimally invasive image-guided local-regional treatments for hepatocellular carcinoma (HCC), including transarterial chemoembolization, transarterial radioembolization, and thermal ablation. However, current standardized clinical staging systems for HCC are limited in their ability to optimize patient selection for treatment as they rely primarily on serum markers and radiologist-defined imaging features. Given the variation in treatment responses, an updated scoring system that includes multidimensional aspects of the disease, including quantitative imaging features, serum markers, and functional biomarkers, is needed to optimally triage patients. With the vast amounts of numerical medical record data and imaging features, researchers have turned to image-based methods, such as radiomics and artificial intelligence (AI), to automatically extract and process multidimensional data from images. The synthesis of these data can provide clinically relevant results to guide personalized treatment plans and optimize resource utilization. Machine learning (ML) is a branch of AI in which a model learns from training data and makes effective predictions by teaching itself. This review article outlines the basics of ML and provides a comprehensive overview of its potential value in the prediction of treatment response in patients with HCC after minimally invasive image-guided therapy.

Ethiodized oil as an imaging biomarker after conventional transarterial chemoembolization.

Conventional transarterial chemoembolization (cTACE) utilizing ethiodized oil as a chemotherapy carrier has become a standard treatment for intermediate-stage hepatocellular carcinoma (HCC) and has been adopted as a bridging and downstaging therapy for liver transplantation. Water-in-oil emulsion made up of ethiodized oil and chemotherapy solution is retained in tumor vasculature resulting in high tissue drug concentration and low systemic chemotherapy doses. The density and distribution pattern of ethiodized oil within the tumor on post-treatment imaging are predictive of the extent of tumor necrosis and duration of response to treatment. This review describes the multiple roles of ethiodized oil, particularly in its role as a biomarker of tumor response to cTACE. CLINICAL RELEVANCE: With the increasing complexity of locoregional therapy options, including the use of combination therapies, treatment response assessment has become challenging; Ethiodized oil deposition patterns can serve as an imaging biomarker for the prediction of treatment response, and perhaps predict post-treatment prognosis. KEY POINTS: • Treatment response assessment after locoregional therapy to hepatocellular carcinoma is fraught with multiple challenges given the varied post-treatment imaging appearance. • Ethiodized oil is unique in that its' radiopacity can serve as an imaging biomarker to help predict treatment response. • The pattern of deposition of ethiodozed oil has served as a mechanism to detect portions of tumor that are undertreated and can serve as an adjunct to enhancement in order to improve management in patients treated with intraarterial embolization with ethiodized oil.

COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data.

OBJECTIVES: We aimed to develop deep learning models using longitudinal chest X-rays (CXRs) and clinical data to predict in-hospital mortality of COVID-19 patients in the intensive care unit (ICU). METHODS: Six hundred fifty-four patients (212 deceased, 442 alive, 5645 total CXRs) were identified across two institutions. Imaging and clinical data from one institution were used to train five longitudinal transformer-based networks applying five-fold cross-validation. The models were tested on data from the other institution, and pairwise comparisons were used to determine the best-performing models. RESULTS: A higher proportion of deceased patients had elevated white blood cell count, decreased absolute lymphocyte count, elevated creatine concentration, and incidence of cardiovascular and chronic kidney disease. A model based on pre-ICU CXRs achieved an AUC of 0.632 and an accuracy of 0.593, and a model based on ICU CXRs achieved an AUC of 0.697 and an accuracy of 0.657. A model based on all longitudinal CXRs (both pre-ICU and ICU) achieved an AUC of 0.702 and an accuracy of 0.694. A model based on clinical data alone achieved an AUC of 0.653 and an accuracy of 0.657. The addition of longitudinal imaging to clinical data in a combined model significantly improved performance, reaching an AUC of 0.727 (p = 0.039) and an accuracy of 0.732. CONCLUSIONS: The addition of longitudinal CXRs to clinical data significantly improves mortality prediction with deep learning for COVID-19 patients in the ICU. KEY POINTS: • Deep learning was used to predict mortality in COVID-19 ICU patients. • Serial radiographs and clinical data were used. • The models could inform clinical decision-making and resource allocation.

Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data.

OBJECTIVES: Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients. METHODS: An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19. RESULTS: A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients' to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001). CONCLUSIONS: Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment. KEY POINT: • AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.

End-to-end artificial intelligence platform for the management of large vessel occlusions: A preliminary study.

OBJECTIVES: In this study, we developed a deep learning pipeline that detects large vessel occlusion (LVO) and predicts functional outcome based on computed tomography angiography (CTA) images to improve the management of the LVO patients. METHODS: A series identifier picked out 8650 LVO-protocoled studies from 2015 to 2019 at Rhode Island Hospital with an identified thin axial series that served as the data pool. Data were annotated into 2 classes: 1021 LVOs and 7629 normal. The Inception-V1 I3D architecture was applied for LVO detection. For outcome prediction, 323 patients undergoing thrombectomy were selected. A 3D convolution neural network (CNN) was used for outcome prediction (30-day mRS) with CTA volumes and embedded pre-treatment variables as inputs. RESULT: For LVO-detection model, CTAs from 8,650 patients (median age 68 years, interquartile range (IQR): 58-81; 3934 females) were analyzed. The cross-validated AUC for LVO vs. not was 0.74 (95% CI: 0.72-0.75). For the mRS classification model, CTAs from 323 patients (median age 75 years, IQR: 63-84; 164 females) were analyzed. The algorithm achieved a test AUC of 0.82 (95% CI: 0.79-0.84), sensitivity of 89%, and specificity 66%. The two models were then integrated with hospital infrastructure where CTA was collected in real-time and processed by the model. If LVO was detected, interventionists were notified and provided with predicted clinical outcome information. CONCLUSION: 3D CNNs based on CTA were effective in selecting LVO and predicting LVO mechanical thrombectomy short-term prognosis. End-to-end AI platform allows users to receive immediate prognosis prediction and facilitates clinical workflow.

Evaluation of a convolutional neural network for ovarian tumor differentiation based on magnetic resonance imaging.

OBJECTIVES: There currently lacks a noninvasive and accurate method to distinguish benign and malignant ovarian lesion prior to treatment. This study developed a deep learning algorithm that distinguishes benign from malignant ovarian lesion by applying a convolutional neural network on routine MR imaging. METHODS: Five hundred forty-five lesions (379 benign and 166 malignant) from 451 patients from a single institution were divided into training, validation, and testing set in a 7:2:1 ratio. Model performance was compared with four junior and three senior radiologists on the test set. RESULTS: Compared with junior radiologists averaged, the final ensemble model combining MR imaging and clinical variables had a higher test accuracy (0.87 vs 0.64, p < 0.001) and specificity (0.92 vs 0.64, p < 0.001) with comparable sensitivity (0.75 vs 0.63, p = 0.407). Against the senior radiologists averaged, the final ensemble model also had a higher test accuracy (0.87 vs 0.74, p = 0.033) and specificity (0.92 vs 0.70, p < 0.001) with comparable sensitivity (0.75 vs 0.83, p = 0.557). Assisted by the model's probabilities, the junior radiologists achieved a higher average test accuracy (0.77 vs 0.64, Δ = 0.13, p < 0.001) and specificity (0.81 vs 0.64, Δ = 0.17, p < 0.001) with unchanged sensitivity (0.69 vs 0.63, Δ = 0.06, p = 0.302). With the AI probabilities, the junior radiologists had higher specificity (0.81 vs 0.70, Δ = 0.11, p = 0.005) but similar accuracy (0.77 vs 0.74, Δ = 0.03, p = 0.409) and sensitivity (0.69 vs 0.83, Δ = -0.146, p = 0.097) when compared with the senior radiologists. CONCLUSIONS: These results demonstrate that artificial intelligence based on deep learning can assist radiologists in assessing the nature of ovarian lesions and improve their performance. KEY POINTS: • Artificial Intelligence based on deep learning can assess the nature of ovarian lesions on routine MRI with higher accuracy and specificity than radiologists. • Assisted by the deep learning model's probabilities, junior radiologists achieved better performance that matched those of senior radiologists.

Development of Brain Metastases in Patients With Non-Small Cell Lung Cancer and No Brain Metastases at Initial Staging Evaluation: Cumulative Incidence and Risk Factor Analysis.

BACKGROUND. Although established guidelines give indications for performing staging brain MRI at initial diagnosis of non-small cell lung cancer (NSCLC), guidelines are lacking for performing surveillance brain MRI for patients without brain metastases at presentation. OBJECTIVE. The purpose of this study is to estimate the cumulative incidence of and risk factors for brain metastasis development in patients with NSCLC without brain metastases at initial presentation. METHODS. This retrospective study included 1495 patients with NSCLC (mean [± SD] age, 65 ± 10 years; 920 men and 575 women) without brain metastases at initial evaluation that included brain MRI. Follow-up brain MRI was ordered at the discretion of the referring physicians. MRI examinations were reviewed in combination with clinical records for brain metastasis development; patients not undergoing MRI were deemed to have not had metastases develop through last clinical follow-up. The cumulative incidence of brain metastases was determined, with death considered a competing risk, and was stratified by clinical stage group, cell type, and epidermal growth factor receptor (EGFR) gene mutation status. Univariable and multivariable Cox proportional hazards regression analyses were performed. RESULTS. A total of 258 of 1495 patients (17.3%) underwent follow-up brain MRI, and 72 (4.8%) had brain metastases develop at a median of 12.3 months after initial diagnosis of NSCLC. Of the 72 patients who had metastases develop, 44.4% had no neurologic symptoms, and 58.3% had stable primary thoracic disease. The cumulative incidence of brain metastases at 6, 12, 18, and 24 months after initial evaluation was 0.6%, 2.1%, 4.2%, and 6.8%, respectively. Cumulative incidence at 6, 12, 18, and 24 months was higher (p < .001) in patients with clinical stage III-IV disease (1.3%, 3.9%, 7.7%, and 10.9%, respectively) than in those with clinical stage I-II disease (0.0%, 0.8%, 1.2%, and 2.6%, respectively), and it was higher (p < .001) in patients with EGFR mutation-positive adenocarcinoma (0.7%, 2.5%, 6.3%, and 12.3%, respectively) than in those with EGFR mutation-negative adenocarcinoma (0.4%, 1.8%, 2.9%, and 4.4%, respectively). Among 1109 patients with adenocarcinoma, independent risk factors for the development of brain metastasis were clinical stage III-IV (hazard ratio [HR], 9.39; p < .001) and EGFR mutation-positive status (HR, 1.78; p = .04). The incidence of brain metastasis over the study interval was 8.7% among patients with clinical stage III-IV disease and 17.4% among those with EGFR mutation-positive adenocarcinoma. CONCLUSION. Clinical stage III-IV and EGFR mutation-positive adenocarcinoma are independent risk factors for brain metastasis development. CLINICAL IMPACT. For patients with clinical stage III-IV disease or EGFR mutation-positive adenocarcinoma, surveillance brain MRI performed 12 months after initial evaluation may be warranted.

Facial De-identification of Head CT Scans.

Online supplemental material is available for this article.

Diagnostic Yield of Staging Brain MRI in Patients with Newly Diagnosed Non-Small Cell Lung Cancer.

Background Existing guidelines are inconsistent regarding the indications for staging brain MRI in patients with newly diagnosed, early-stage non-small cell lung cancer (NSCLC). Purpose To evaluate the diagnostic yield of staging brain MRI in the initial evaluation of lung cancer. Materials and Methods This retrospective, observational, single-institution study included patients with newly diagnosed NSCLC who underwent staging chest CT and staging brain MRI from November 2017 to October 2018. Diagnostic yield was defined as the proportion of patients with brain metastases among all patients. Yield was stratified into clinical stage groups per the eighth edition of the American Joint Committee on Cancer staging guidelines, based on staging chest CT and in adenocarcinoma with epidermal growth factor receptor (EGFR) gene mutation and anaplastic lymphoma kinase (ALK) gene rearrangement. Subgroup analyses were performed on the basis of cell types and molecular markers. The χ2 test was performed to compare the diagnostic yields, and Bonferroni correction was used to account for multiple testing between stage groups. Results A total of 1712 patients (mean age, 64 years ± 10 [standard deviation]; 1035 men) were included. The diagnostic yield of staging brain MRI in newly diagnosed NSCLC was 11.9% (203 of 1712; 95% confidence interval [CI]: 10.4%, 13.5%). In clinical stage IA, IB, and II disease, the diagnostic yields were 0.3% (two of 615; 95% CI: 0.0%, 1.2%), 3.8% (seven of 186; 95% CI: 1.5%, 7.6%), and 4.7% (eight of 171; 95% CI: 2.0%, 9.0%), respectively. The diagnostic yield was higher in patients with adenocarcinoma (13.6%; 176 of 1297; 95% CI: 11.8%, 15.6%) than squamous cell carcinoma (5.9%; 21 of 354; 95% CI: 3.7%, 8.9%) and in patients with EGFR mutation-positive adenocarcinoma (17.5%; 85 of 487; 95% CI: 14.2%, 21.1%) than with EGFR mutation-negative adenocarcinoma (10.6%; 68 of 639; 95% CI: 8.4%, 13.3%) (P < .001 for both). Conclusion The diagnostic yield of staging brain MRI in clinical stage IA non-small cell lung cancer was low, but staging brain MRI had a higher diagnostic yield in clinical stage IB and epidermal growth factor receptor mutation-positive adenocarcinoma. © RSNA, 2020 Online supplemental material is available for this article.

Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT.

Background Coronavirus disease 2019 (COVID-19) and pneumonia of other diseases share similar CT characteristics, which contributes to the challenges in differentiating them with high accuracy. Purpose To establish and evaluate an artificial intelligence (AI) system for differentiating COVID-19 and other pneumonia at chest CT and assessing radiologist performance without and with AI assistance. Materials and Methods A total of 521 patients with positive reverse transcription polymerase chain reaction results for COVID-19 and abnormal chest CT findings were retrospectively identified from 10 hospitals from January 2020 to April 2020. A total of 665 patients with non-COVID-19 pneumonia and definite evidence of pneumonia at chest CT were retrospectively selected from three hospitals between 2017 and 2019. To classify COVID-19 versus other pneumonia for each patient, abnormal CT slices were input into the EfficientNet B4 deep neural network architecture after lung segmentation, followed by a two-layer fully connected neural network to pool slices together. The final cohort of 1186 patients (132 583 CT slices) was divided into training, validation, and test sets in a 7:2:1 and equal ratio. Independent testing was performed by evaluating model performance in separate hospitals. Studies were blindly reviewed by six radiologists without and then with AI assistance. Results The final model achieved a test accuracy of 96% (95% confidence interval [CI]: 90%, 98%), a sensitivity of 95% (95% CI: 83%, 100%), and a specificity of 96% (95% CI: 88%, 99%) with area under the receiver operating characteristic curve of 0.95 and area under the precision-recall curve of 0.90. On independent testing, this model achieved an accuracy of 87% (95% CI: 82%, 90%), a sensitivity of 89% (95% CI: 81%, 94%), and a specificity of 86% (95% CI: 80%, 90%) with area under the receiver operating characteristic curve of 0.90 and area under the precision-recall curve of 0.87. Assisted by the probabilities of the model, the radiologists achieved a higher average test accuracy (90% vs 85%, Δ = 5, P < .001), sensitivity (88% vs 79%, Δ = 9, P < .001), and specificity (91% vs 88%, Δ = 3, P = .001). Conclusion Artificial intelligence assistance improved radiologists' performance in distinguishing coronavirus disease 2019 pneumonia from non-coronavirus disease 2019 pneumonia at chest CT. © RSNA, 2020 Online supplemental material is available for this article.

Performance of Radiologists in Differentiating COVID-19 from Non-COVID-19 Viral Pneumonia at Chest CT.

Background Despite its high sensitivity in diagnosing coronavirus disease 2019 (COVID-19) in a screening population, the chest CT appearance of COVID-19 pneumonia is thought to be nonspecific. Purpose To assess the performance of radiologists in the United States and China in differentiating COVID-19 from viral pneumonia at chest CT. Materials and Methods In this study, 219 patients with positive COVID-19, as determined with reverse-transcription polymerase chain reaction (RT-PCR) and abnormal chest CT findings, were retrospectively identified from seven Chinese hospitals in Hunan Province, China, from January 6 to February 20, 2020. Two hundred five patients with positive respiratory pathogen panel results for viral pneumonia and CT findings consistent with or highly suspicious for pneumonia, according to original radiologic interpretation within 7 days of each other, were identified from Rhode Island Hospital in Providence, RI. Three radiologists from China reviewed all chest CT scans (n = 424) blinded to RT-PCR findings to differentiate COVID-19 from viral pneumonia. A sample of 58 age-matched patients was randomly selected and evaluated by four radiologists from the United States in a similar fashion. Different CT features were recorded and compared between the two groups. Results For all chest CT scans (n = 424), the accuracy of the three radiologists from China in differentiating COVID-19 from non-COVID-19 viral pneumonia was 83% (350 of 424), 80% (338 of 424), and 60% (255 of 424). In the randomly selected sample (n = 58), the sensitivities of three radiologists from China and four radiologists from the United States were 80%, 67%, 97%, 93%, 83%, 73%, and 70%, respectively. The corresponding specificities of the same readers were 100%, 93%, 7%, 100%, 93%, 93%, and 100%, respectively. Compared with non-COVID-19 pneumonia, COVID-19 pneumonia was more likely to have a peripheral distribution (80% vs 57%, P < .001), ground-glass opacity (91% vs 68%, P < .001), fine reticular opacity (56% vs 22%, P < .001), and vascular thickening (59% vs 22%, P < .001), but it was less likely to have a central and peripheral distribution (14% vs 35%, P < .001), pleural effusion (4% vs 39%, P < .001), or lymphadenopathy (3% vs 10%, P = .002). Conclusion Radiologists in China and in the United States distinguished coronavirus disease 2019 from viral pneumonia at chest CT with moderate to high accuracy. © RSNA, 2020 Online supplemental material is available for this article. A translation of this abstract in Farsi is available in the supplement. ترجمه چکیده این مقاله به فارسی، در ضمیمه موجود است.

Deep Learning Based on MRI for Differentiation of Low- and High-Grade in Low-Stage Renal Cell Carcinoma.

Pretreatment determination of renal cell carcinoma aggressiveness may help to guide clinical decision-making. PURPOSE: To evaluate the efficacy of residual convolutional neural network using routine MRI in differentiating low-grade (grade I-II) from high-grade (grade III-IV) in stage I and II renal cell carcinoma. STUDY TYPE: Retrospective. POPULATION: In all, 376 patients with 430 renal cell carcinoma lesions from 2008-2019 in a multicenter cohort were acquired. The 353 Fuhrman-graded renal cell carcinomas were divided into a training, validation, and test set with a 7:2:1 split. The 77 WHO/ISUP graded renal cell carcinomas were used as a separate WHO/ISUP test set. FIELD STRENGTH/SEQUENCE: 1.5T and 3.0T/T2 -weighted and T1 contrast-enhanced sequences. ASSESSMENT: The accuracy, sensitivity, and specificity of the final model were assessed. The receiver operating characteristic (ROC) curve and precision-recall curve were plotted to measure the performance of the binary classifier. A confusion matrix was drawn to show the true positive, true negative, false positive, and false negative of the model. STATISTICAL TESTS: Mann-Whitney U-test for continuous data and the chi-square test or Fisher's exact test for categorical data were used to compare the difference of clinicopathologic characteristics between the low- and high-grade groups. The adjusted Wald method was used to calculate the 95% confidence interval (CI) of accuracy, sensitivity, and specificity. RESULTS: The final deep-learning model achieved a test accuracy of 0.88 (95% CI: 0.73-0.96), sensitivity of 0.89 (95% CI: 0.74-0.96), and specificity of 0.88 (95% CI: 0.73-0.96) in the Fuhrman test set and a test accuracy of 0.83 (95% CI: 0.73-0.90), sensitivity of 0.92 (95% CI: 0.84-0.97), and specificity of 0.78 (95% CI: 0.68-0.86) in the WHO/ISUP test set. DATA CONCLUSION: Deep learning can noninvasively predict the histological grade of stage I and II renal cell carcinoma using conventional MRI in a multiinstitutional dataset with high accuracy. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

Ablation Therapy for Advanced Stage Non-Small Cell Lung Cancer: A National Cancer Database Study.

PURPOSE: To compare overall survival (OS) of ablation with no treatment for patients with advanced stage non-small cell lung cancer. METHODS: Patients with clinical stage IIIB (T1-4N3M0, T4N2M0) and stage IV (T1-4N0-3M1) non-small cell lung cancer, in accordance with the American Joint Committee on Cancer, 7th edition, who did not receive treatment or who received ablation as their sole primary treatment besides chemotherapy from 2004 to 2014, were identified from the National Cancer Data Base. OS was estimated using the Kaplan-Meier method and evaluated by log-rank test, univariate and multivariate Cox proportional hazard regression, and propensity score-matched analysis. Relative survival analyses comparing age- and sex-matched United States populations were performed. RESULTS: A total of 140,819 patients were included. The 1-, 2-, 3- and 5-year survival rates relative to age- and sex-matched United States population were 28%, 18%, 12%, and 10%, respectively, for ablation (n = 249); and 30%, 15%, 9%, and 5%, respectively for no treatment (n = 140,570). Propensity score matching resulted in 249 patients in the ablation group versus 498 patients in the no-treatment group. After matching, ablation was associated with longer OS than that in the no-treatment group (median, 5.9 vs 4.7 months, respectively; hazard ratio, 0.844; 95% confidence interval, 0.719-0.990; P = .037). These results persisted in patients with an initial tumor size of ≤3 cm. CONCLUSIONS: Preliminary results suggest ablation may be associated with longer OS in patients with late-stage non-small cell lung cancer than survival in those who received no treatment.

Deep Learning Based on MR Imaging for Predicting Outcome of Uterine Fibroid Embolization.

PURPOSE: To develop and validate a deep learning model based on routine magnetic resonance (MR) imaging obtained before uterine fibroid embolization to predict procedure outcome. MATERIALS AND METHODS: Clinical data were collected on patients treated with uterine fibroid embolization at the Hospital of the University of Pennsylvania from 2007 to 2018. Fibroids for each patient were manually segmented by an abdominal radiologist on a T1-weighted contrast-enhanced (T1C) sequence and a T2-weighted sequence of MR imaging obtained before and after embolization. A residual convolutional neural network (ResNet) model to predict clinical outcome was trained using MR imaging obtained before the procedure. RESULTS: Inclusion criteria were met by 727 fibroids in 409 patients. At clinical follow-up, 85.6% (n = 350) of 409 patients (590 of 727 fibroids; 81.1%) experienced symptom resolution or improvement, and 14.4% (n = 59) of 409 patients (137 of 727 fibroids; 18.9%) had no improvement or worsening symptoms. The T1C trained model achieved a test accuracy of 0.847 (95% confidence interval [CI], 0.745-0.914), sensitivity of 0.932 (95% CI, 0.833-0.978), and specificity of 0.462 (95% CI, 0.232-0.709). In comparison, the average of 4 radiologists achieved a test accuracy of 0.722 (95% CI, 0.609-0.813), sensitivity of 0.852 (95% CI, 0.737-0.923), and specificity of 0.135 (95% CI, 0.021-0.415). CONCLUSIONS: This study demonstrates that deep learning based on a ResNet model achieves good accuracy in predicting outcome of uterine fibroid embolization. If further validated, the model may help clinicians better identify patients who can most benefit from this therapy and aid clinical decision making.

Clinical and imaging findings of discharged patients with SARS-CoV-2 positive anal swab samples: a descriptive study.

BACKGROUND: To explore the clinical features and CT findings of clinically cured coronavirus disease 2019 (COVID-19) patients with viral RNA positive anal swab results after discharge. METHODS: Forty-two patients with COVID-19 who were admitted to Yongzhou Central Hospital, Hunan, China, between January 20, 2020, and March 2, 2020, were tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using anal swab viral RT-PCR. In this report, we present the clinical characteristics and chest CT features of six patients with positive anal swab results and compare the clinical, laboratory, and CT findings between the positive and negative groups. RESULTS: The anal swab positivity rate for SARS-CoV-2 RNA in discharged patients was 14.3% (6/42). All six patients were male. In the positive group, 40% of the patients (2/5) had a positive stool occult blood test (OBT), but none had diarrhea. The median duration of fever and major symptoms (except fever) in the positive patients was shorter than that of the negative patients (1 day vs. 6 days, 4.5 days vs. 10.5 days, respectively). The incidence of asymptomatic cases in the positive group (33.3%) was also higher than that of the negative group (5.6%). There were no significant differences in the CT manifestation or evolution of the pulmonary lesions between the two groups. CONCLUSION: In our case series, patients with viral RNA positive anal swabs did not exhibit gastrointestinal symptoms, and their main symptoms disappeared early. They had similar CT features to the negative patients, which may be easier to be ignored. A positive OBT may indicate gastrointestinal damage caused by SARS-CoV-2 infection.

Machine learning reveals multimodal MRI patterns predictive of isocitrate dehydrogenase and 1p/19q status in diffuse low- and high-grade gliomas.

PURPOSE: Isocitrate dehydrogenase (IDH) and 1p19q codeletion status are importantin providing prognostic information as well as prediction of treatment response in gliomas. Accurate determination of the IDH mutation status and 1p19q co-deletion prior to surgery may complement invasive tissue sampling and guide treatment decisions. METHODS: Preoperative MRIs of 538 glioma patients from three institutions were used as a training cohort. Histogram, shape, and texture features were extracted from preoperative MRIs of T1 contrast enhanced and T2-FLAIR sequences. The extracted features were then integrated with age using a random forest algorithm to generate a model predictive of IDH mutation status and 1p19q codeletion. The model was then validated using MRIs from glioma patients in the Cancer Imaging Archive. RESULTS: Our model predictive of IDH achieved an area under the receiver operating characteristic curve (AUC) of 0.921 in the training cohort and 0.919 in the validation cohort. Age offered the highest predictive value, followed by shape features. Based on the top 15 features, the AUC was 0.917 and 0.916 for the training and validation cohort, respectively. The overall accuracy for 3 group prediction (IDH-wild type, IDH-mutant and 1p19q co-deletion, IDH-mutant and 1p19q non-codeletion) was 78.2% (155 correctly predicted out of 198). CONCLUSION: Using machine-learning algorithms, high accuracy was achieved in the prediction of IDH genotype in gliomas and moderate accuracy in a three-group prediction including IDH genotype and 1p19q codeletion.

A Comparison of Cryoablation with Heat-Based Thermal Ablation for Treatment of Clinical T1a Renal Cell Carcinoma: A National Cancer Database Study.

PURPOSE: To compare the overall survival (OS) of patients receiving cryoablation versus heat-based thermal ablation for clinical T1a renal cell carcinoma (RCC) in a large national cohort. MATERIALS AND METHODS: Patients with RCC from 2004 to 2014 who were treated with ablation were identified from the National Cancer Database. OS was estimated with the use of the Kaplan-Meier method and evaluated by means of log-rank test, univariate and multivariate Cox proportional hazard regression, and propensity score-matched analysis. RESULTS: A total of 3,936 patients who received cryoablation and 2,322 who received heat-based thermal ablation met the inclusion criteria. The mean age was 67 ± 12 year, and the mean size of tumors was 25 ± 8 mm. The 3-, 5-, and 10-year survival rates were, respectively, 91%, 82%, and 62% for cryoablation and 89%, 81%, and 55% for heat-based thermal ablation. After propensity score matching, cryoablation was associated with longer OS compared with heat-based thermal ablation (median 11.3 vs 10.4 years; hazard ratio 1.175, 95% CI 1.03-1.341; P = .016). For patients with tumors ≤2 cm, propensity score-matched analyses demonstrated no significant difference between the 2 treatment groups (P = .772). CONCLUSIONS: Overall, cryoablation may be associated with longer OS compared with heat-based thermal ablation in cT1a RCC. No significant difference in survival rates was observed between the 2 treatments for patients with tumor sizes ≤2 cm. Owing to the inherent limitations of this study, further study with details on technology, local outcome, and complications is needed.

Comparison of Mohs Surgery and Surgical Excision in the Treatment of Localized Sebaceous Carcinoma.

BACKGROUND: It remains controversial if Mohs surgery is superior to surgical excision in treating localized sebaceous carcinoma. OBJECTIVE: To compare Mohs surgery and surgical excision for treating patients with localized sebaceous carcinoma. MATERIALS AND METHODS: The US National Cancer Database was used to identify patients with histologically confirmed Stage 0 to 2 sebaceous carcinoma from 2004 to 2014. Clinicopathologic and socioeconomic factors were compared between treatment groups using the chi-square test. Overall survival (OS) was evaluated by log-rank test, multivariable Cox proportional hazard regression, and propensity score-matched analysis. Relative survival analyses compared with age- and sex-matched US population were performed. RESULTS: Of 1,265 patients, 234 received Mohs surgery and 1,031 received surgical excision. Mohs surgery had a higher rate of negative margin (p = .004). On multivariate Cox regression analysis, Mohs surgery was associated with longer OS than surgical excision (HR: 0.703, 95% CI: 0.496-0.995, p = .047). The survival benefit of Mohs surgery persisted on relative survival analysis and propensity score-matched analysis (p = .0385), after matching the 2 groups on patient and disease characteristics. CONCLUSION: Patients who received Mohs surgery had significantly longer OS when compared with those who received surgical excision. Prospective clinical trials comparing these treatment paradigms are warranted.

Disease site as a determinant of survival outcome in patients with systemic anaplastic lymphoma kinase positive anaplastic large cell lymphoma with extranodal involvement: an analysis of 1306 cases from the US National Cancer Database.

Systemic anaplastic lymphoma kinase positive (ALK+) anaplastic large cell lymphoma with extranodal involvement (ALCL-E) is a rare form of non-Hodgkin lymphoma. No large study in the literature has compared the survival outcomes among different primary extranodal sites of involvement in ALK+ ALCL-E. We identified 1306 patients with ALK+ ALCL-E diagnosed between 2004 and 2014 in the US National Cancer Database, among whom 387 had primary extranodal site in the chest/abdomen/pelvis, 103 in the bone, 62 in the central nervous system, 134 in the head and neck and 620 in the cutaneous/soft tissue. Younger age, lower Charlson-Deyo score, lower clinical stage, receipt of chemotherapy and receipt of radiotherapy were predictors of longer overall survival. Patients with extranodal involvement of central nervous system and chest/abdomen/pelvis had shorter overall survival than those with involvement of head and neck, bone, and cutaneous/subcutaneous tissue after adjusting for confounding variables. We recommend treating these patients upfront with more aggressive therapy.

Ethnic disparity in primary cutaneous CD30+ T-cell lymphoproliferative disorders: an analysis of 1496 cases from the US National Cancer Database.

Primary cutaneous CD30+ T cell lymphoproliferative disorders (PCLPD), the second most common type of primary cutaneous T cell lymphomas, accounts for approximately 25-30% of cutaneous T-cell lymphoma cases. However, only small retrospective studies have been reported. We aimed to identify prognostic factors and evaluate the overall survival (OS) of patients with PCLPD stratified by ethnicity. We identified 1496 patients diagnosed with PCLPD between 2004 and 2014 in the US National Cancer Database. Chi-square test and anova were used to evaluate differences in demographic and disease characteristics, socioeconomic factors and treatments received. OS was evaluated with the log-rank test, Cox proportional hazard regression analysis, and propensity score matching. The study included 1267 Caucasians, 153 African Americans (AA), 43 Asians, and 33 of other/unknown ethnicity. Older age, higher Charlson-Deyo score, higher clinical stage and receipt of chemotherapy were predictors of shorter OS. Primary disease site on a lower extremity was associated with shorter OS, while a head and neck location was associated with longer OS. AA patients had shorter OS when compared to Caucasian patients on multivariate analysis. This ethnic disparity persisted on propensity-score matched analysis and after matching Caucasian and AA patients on demographic and disease characteristics, socioeconomic factors and treatments received, and age and gender-matched relative survival analyses.