Purpose This study aimed to summarize the computed tomography (CT) findings of PMME and differentiate it from esophageal SCC and leiomyoma using CT analysis. Methods This was a retrospective study including 23 patients with PMME, 69 patients with SCC, and 21 patients with leiomyoma in our hospital. Qualitative CT morphological characteristics of each lesion included the location, tumor range, ulcer, enhanced pattern, and so on. For quantitative CT analysis, thickness, length and area of tumor, size of largest lymph node, number of metastatic lymph node, and CT value of tumor in plain, arterial, and delayed phases were measured. The associated factors for differentiating PMME from SCC and leiomyoma were examined with univariate and multivariate analysis. Receive operating characteristic curve (ROC) was used to determine the performance of CT models in discriminating PMME from SCC and leiomyoma. Results The thickness, mean CT value in arterial phase, and range of tumor were the independent factors for diagnosing PMME from SCC. These parameters were used to establish a diagnostic CT model with area under the ROC (AUC) of 0.969, and accuracy of 90.2%. In pathology, interstitial vessels in PMME were more abundant than that of SCC, and the stromal fibrosis was more obvious in SCC. PMME commonly exhibited intraluminal expansively growth pattern and SCC often showed infiltrative pattern. The postcontrast attenuation difference in maximum CT attenuation value between plain and arterial phases was the independent factor for diagnosing PMME from leiomyoma. This parameter was applied to differentiate PMME from leiomyoma with AUC of 0.929 and accuracy of 86.4%. Conclusion The qualitative and quantitative CT analysis had excellent performance for differentiating PMME from SCC and esophageal leiomyoma. Graphical abstract

BackgroundPositron emission tomography (PET)/MRI combines the characteristics of metabolism imaging and high soft tissue resolution, and could provide high diagnostic efficacy for assessment of pleural invasion (PI) of lung cancer.PurposeTo investigate the application of 18F‐fluorodeoxyglucose (FDG) PET/MRI for predicting PI of lung cancer with the maximum diameter ≤3 cm.Study TypeProspective.PopulationA total of 44 patients with non‐small cell lung cancer (NSCLC), age from 39 to 79 years old, including 19 (56.82%) females.Field Strength/SequenceA 3‐T, hybrid PET/MRI including axial fast spin echo respiratory‐triggered T2 fat‐suppressed imaging (T2FS) and echo planar imaging diffusion‐weighted imaging (DWI).AssessmentThe maximum standardized uptake value (SUVmax) of all lesions was measured on PET images. Localized effusion outside the contact between the nodules and the pleura on T2FS and signal at the contact between the nodules and the pleura on DWI were evaluated by experienced physicians through visual assessment of the MRI sequences.Statistical TestsThree models (models 1–3) were developed, incorporating CT, CT and PET, PET and MRI features, and Lasso regression was used in feature selection. The receiver operating characteristic (ROC) curve for PI diagnosis was visualized for each model, and the area under the curve (AUC) was calculated. The DeLong test was used to compare the different AUCs. A P value < 0.05 was considered statistically significant.ResultsThe AUC of models 1–3 was 0.762, 0.829, and 0.915, respectively. The DeLong test showed a statistically significant difference between the AUCs of model 1 vs. model 3, while the differences between the AUCs of model 1 vs. model 2 (P = 0.253) and model 2 vs. model 3 (P = 0.075) were not statistically significant.Data conclusion18F‐FDG PET/MRI might show high predictive value for lung adenocarcinoma smaller than 3 cm with PI.Evidence Level1Technical EfficacyStage 2

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Objective. The diagnosis of primary malignant melanoma of the esophagus (PMME) before treatment is essential for clinical decision-making. However, PMME may be misdiagnosed as esophageal squamous cell carcinoma (ESCC) sometimes. This research is aimed at devising a radiomics nomogram model of CT for distinguishing PMME from ESCC. Methods. In this retrospective analysis, 122 individuals with proven pathologically PMME ( n = 28 ) and ESCC ( n = 94 ) were registered from our hospital. PyRadiomics was applied to derive radiomics features from plain and enhanced CT images after resampling image into an isotropic resolution of 0.625 × 0.625 × 0.625   m m 3 . The diagnostic efficiency of the model was evaluated by an independent validation group. Results. For the purpose of differentiation between PMME and ESCC, a radiomics model was constructed using 5 radiomics features obtained from nonenhanced CT and 4 radiomics features derived from enhanced CT. A radiomics model including multiple radiomics features showed excellent discrimination efficiency with AUCs of 0.975 and 0.906 in the primary and validation cohorts, respectively. Then, a radiomics nomogram model was developed. The decision curve analysis has shown remarkable performance of this nomogram model for distinguishing PMME from ESCC. Conclusions. The proposed radiomics nomogram model based on CT could be used for distinguishing PMME from ESCC. Moreover, this model also contributed to helping clinicians determine an appropriate treatment strategy for esophageal neoplasms.

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