ObjectiveTo investigate whether radiomic features can be surrogate biomarkers for epidermal growth factor receptor (EGFR) mutation statuses.Materials and methodsTwo hundred ninety six consecutive patients, who underwent CT examinations before operation within 3 months and had EGFR mutations tested, were enrolled in this retrospective study. CT texture features were extracted using an open-source software with whole volume segmentation. The association between CT texture features and EGFR mutation statuses were analyzed.ResultsIn the 296 patients, there were 151 patients with EGFR mutations (51%). Logistic analysis identified that lower age (Odds Ratio[OR]: 0.968,95% confidence interval [CI]:0.946~0.990, p = 0.005) and a radiomic feature named GreyLevelNonuniformityNormalized (OR: 0.012, 95% CI:0.000~0.352, p = 0.01) were predictors for exon 19 mutation; higher age (OR: 1.027, 95%CI:1.003~1.052,p = 0.025), female sex (OR: 2.189, 95%CI:1.264~3.791, p = 0.005) and a radiomic feature named Maximum2DDiameterColumn (OR: 0.968, 95%CI:0.946~0.990], p = 0.005) for exon 21 mutation; and female sex (OR: 1.883,95%CI:1.064~3.329, p = 0.030), non-smoking status (OR: 2.070, 95%CI:1.090~3.929, p = 0.026) and a radiomic feature termed SizeZone NonUniformityNormalized (OR: 0.010, 95% CI:0.0001~0.852, p = 0.042) for EGFR mutations. Areas under the curve (AUCs) of combination with clinical and radiomic features to predict exon 19 mutation, exon 21 mutation and EGFR mutations were 0.655, 0.675 and 0.664, respectively.ConclusionSeveral radiomic features are associated with EGFR mutation statuses of lung adenocarcinoma. Combination with clinical files, moderate diagnostic performance can be obtained to predict EGFR mutation status of lung adenocarcinoma. Radiomic features might harbor potential surrogate biomarkers for identification of EGRF mutation statuses.

Background Lymphovascular space invasion (LVSI) of endometrial carcinoma (EMC) is one of the important prognostic factors, which is not usually visible subjectively. Therefore, clinicians lack imaging‐based evidence about LVSI for preoperative treatment decision‐making. Purpose To develop a multiparametric MRI (mpMRI)‐based radiomics nomogram for predicting LVSI in EMC and provide decision‐making support to clinicians. Study Type Retrospective. Population In all, 144 patients with histologically confirmed EMC, 101 patients in a training cohort, and 43 patients in a test cohort. Field Strength/Sequence T2WI, contrast enhanced‐T1WI, and diffusion‐weighted imaging (DWI) at 3.0T MRI. Assessment Tumors were independently segmented images by two radiologists. Two pathologists reviewed the tissue specimens of the tumors to identify the existence of LVSI in consensus. Statistical Tests The intraclass correlation coefficient was used to test the reliability and least absolute shrinkage and selection operator (LASSO) regression for features selection and then developed a radiomics signature named Rad‐score. A nomogram was developed in the training cohort. The diagnostic performance of the nomogram model was assessed by area under the curve (AUC) of the receiver operator characteristic (ROC) in the training and test cohort, respectively. Results LVSI was identified in 32 patients (22.2%). Older age and high grade were correlated with LVSI at univariate analysis. There were five radiomics features that were identified as independent risk factors for LVSI by LASSO regression. Based on age, grade, and Rad‐score, the AUC values of the nomogram model to predict LVSI in the training and test cohort were 0.820 (95% confidence interval [CI]: 0.725, 0.916; sensitivity: 82.6%, specificity: 72.9%), 0.807 (95% CI: 0.673, 0.941; sensitivity: 77.8%, specificity: 78.6%), respectively. Data Conclusion The radiomic‐based machine‐learning model using a nomogram algorithm achieved high diagnostic performance for predicting LVSI of EMC preoperatively, which could enhance risk stratification and provide support for therapeutic decision‐making. Level of Evidence 2. Technical Efficacy Stage 3. J. Magn. Reson. Imaging 2020;52:1257–1262.

In this pictorial review, MR imaging findings of deep infiltrating endometriosis (DIE) are illustrated together with surgical correlation. DIE can appear as irregular nodules or plaques with similar signal intensity to muscle on both T1-weighted and T2-weighted images. Hemorrhage foci and strands or stellate margins are also often noted. Restriction of diffusion can be seen on diffusion-weighted image. Fibrosis and adhesions often result in morphologic changes, such as alimentary tract tortuosity, irregular or nodular thickening of uterosacral ligaments, and partial or complete obliteration of the pouch of Douglas. After intravenous gadolinium contrast agent administration, homo- or heterogeneous mild to moderate enhancement can be observed. MR imaging can depict endometriosis lesions and extension of DIE at different anatomic locations, which is well consistent with surgical findings. Combining signal and morphological abnormalities, MR imaging can diagnose and assess the extension of DIE with high accuracy. MR imaging findings of DIE facilitate surgeons at treatment decision making and patient communication.

Trauma is one of the leading causes of death for men and women under the age of 45 years old, and abdominal injuries contribute to a large number of these deaths. Prompt diagnosis is very important for treatment decision making and can be life-saving. CT has become an essential imaging modality in emergency medicine. In this pictorial review, we present our experience of CT in blunt abdominal trauma and describe CT findings of common injuries, including hemoperitoneum, solid viscera, hollow viscera, mesenteric and diaphragmatic injuries. Unenhanced CT is routinely used, tailored protocols should be reserved for patients with questionable or subtle findings at unenhanced CT, especially for bowel and mesenteric injuries. The decision can be made by radiologists based on initial findings or by referring clinicians based by clinical presentations or deterioration of patients' condition.

Ki67 is a commonly used proliferation marker in pathological diagnosis of tumors; however, its prognostic value in colon cancer is controversial. A total of 312 consecutive patients with stage I–III colon cancer who underwent radical surgery with or without adjuvant chemotherapy were included in the present study. Ki67 expression was assessed using immunohistochemistry and was classified according to 25% intervals. The association between Ki67 expression and clinicopathological features was analyzed. Long-term postoperative survival, including disease-free survival (DFS) and overall survival, was calculated, and its association with Ki67 was analyzed. High Ki67 expression (>50%) was associated with improved DFS in patients treated with adjuvant chemotherapy postoperatively, but not in patients who received surgery alone (P=0.138). Ki67 expression was significantly associated with histological differentiation of the tumor (P=0.01), while it was not associated with other clinicopathological factors. Multivariate analysis demonstrated that pathological T and N stage were independent prognostic factors. In conclusion, high Ki67 expression was associated with a good therapeutic outcome in patients receiving adjuvant chemotherapy in colon cancer.