Predicting anaplastic lymphoma kinase rearrangement status in patients with non-small cell lung cancer using a machine learning algorithm that combines clinical features and CT images.

Purpose: To develop an appropriate machine learning model for predicting anaplastic lymphoma kinase (ALK) rearrangement status in non-small cell lung cancer (NSCLC) patients using computed tomography (CT) images and clinical features. Method and materials: This study included 193 patients with NSCLC (154 in the training cohort, 39 in the validation cohort), 68 of whom tested positive for ALK rearrangements and 125 of whom tested negative. From the nonenhanced CT scans, 157 radiomic characteristics were extracted, and 8 clinical features were collected. Five machine learning (ML) models were assessed to find the best classification model for predicting ALK rearrangement status. A radiomic signature was developed using the least absolute shrinkage and selection operator (LASSO) algorithm. The predictive performance of the models based on radiomic features, clinical features, and their combination was assessed by receiver operating characteristic (ROC) curves. Results: The support vector machine (SVM) model had the highest AUC of 0.914 for classification. The clinical features model had an AUC=0.805 (95% CI 0.731-0.877) and an AUC=0.735 (95% CI 0.566-0.863) in the training and validation cohorts, respectively. The CT image-based ML model had an AUC=0.953 (95% CI 0.913-1.0) in the training cohort and an AUC=0.890 (95% CI 0.778-0.971) in the validation cohort. For predicting ALK rearrangement status, the ML model based on CT images and clinical features performed better than the model based on only clinical information or CT images, with an AUC of 0.965 (95% CI 0.826-0.882) in the primary cohort and an AUC of 0.914 (95% CI 0.804-0.893) in the validation cohort. Conclusion: Our findings revealed that ALK rearrangement status could be accurately predicted using an ML-based classification model based on CT images and clinical data.

Use of intravoxel incoherent motion MRI to assess placental perfusion in normal and Fetal Growth Restricted pregnancies on their third trimester.

INTRODUCTION: Intravoxel Incoherent Motion (IVIM) MRI is a non-invasive, in vivo techniques which can assess placental perfusion quantitatively, and be useful for evaluating placental microcirculation. Our primary aim was to investigate whether fetal growth restriction (FGR) pregnancies have different placental perfusion and diffusion compared with normal pregnancies using IVIM. A secondary aim was to investigate correlations between placental IVIM parameters and gestational age in normal pregnancy. METHODS: This study population included 17 FGR pregnancies and 36 normal pregnancies between 28 + 3 to 38 + 0 weeks. All women underwent a MRI examination including an IVIM sequence with 9 b-values on a 3.0 T MRI system. The standard diffusion coefficeint (D), pseudodiffusion (D*) and perfusion fraction (f) were calculated. RESULTS: Placental f was significantly lower in the FGR group than that in the normal group (33.96 ± 2.62(%) vs 38.48 ± 5.31(%), p = 0.002). Placental D and D* in two groups showed no statistical significance (P > 0.05). Placental f moderately increased with increasing gestational age in normal pregnancies (r = 0.411, p = 0.013), and there existed a negative correlation between D values and gestational age (r = -0.390, p = 0.019). DISCUSSION: The f values are able to distinguish FGR from normal pregnancies. It can be uses as a feasible index to evaluate placenta perfusion. Gestational age-associated changes in placental IVIM parameters likely reveal trajectories of microvascular perfusion fraction and diffusion characteristics in the normal developing placenta.

Development and Validation of a Prognostic Nomogram Based on Residual Tumor in Patients With Nondisseminated Nasopharyngeal Carcinoma.

OBJECTIVES: To investigate the prognostic value of residual tumor based on Magnetic resonance imaging(MRI) and establish an effective prognostic nomogram model referring to clinical,pathological and other related factors for predicting prognosis in nasopharyngeal carcinoma. METHODS: Overall, 538 patients with non-metastatic, histologically-confirmed nasopharyngeal carcinoma were retrospectively examined. Data from 397 patients were used for the construction and validation of a nomogram based on the presence of residual tumor. A concordance index (C-index) was employed to assess the predictive accuracy and discriminative ability of the nomogram. RESULTS: The 3-year survival rates in the non-residual and residual tumor cohorts were as follows: progression-free survival, 73.4% vs. 61.0%, P = 0.009; locoregional recurrence-free survival, 81.9% vs. 72.0%, P = 0.02; and distant metastasis-free survival, 80.7% vs. 73.5%, P = 0.11. Nine significant factors were included in the nomogram model. The calibration curve for the probability of progression-free survival showed that the nomogram-based predictive values had good concordance with the actual observations. CONCLUSION: The results showed that the patients in the residual tumor cohorts had a worse prognosis.The proposed nomogram may predict the prognosis and guide clinical decision-making concerning local residual tumors in nasopharyngeal carcinoma patients. Patients with a high risk of progression require more timely and aggressive treatment.

[Three-dimensional spatial measurement versus conventional CT planning in laparoscopic partial nephrectomy for renal tumors].

OBJECTIVE: To analyze the advantages of spatial measurement of anatomical parameters in a 3D model in surgical planning for laparoscopic partial nephrectomy (LPN). METHODS: From February, 2016 to October, 2017, 37 patients diagnosed with T1 renal mass underwent LPN based on 3D reconstruction after enhanced CT scanning using the Uromedix-3D system (group A), and another 38 patients received LPN with conventional CT planning (group B). The anatomical parameters were measured in the reconstructed 3D model and the demographic data, surgical outcome and postoperative data were compared between the two groups. RESULTS: In group A, the average time for 3D model reconstruction was (29.3∓9.7) min; the length, width and depth of the renal defect in 3D model were 3.2∓1.1 cm, 2.6∓0.9 cm and 1.7∓0.7 cm, respectively; The distance of the tumor from the collecting system was 3.8∓2.2 mm; The mean R.E.N.A.L score of the patients was 7∓1.5, and 3 patients had accessory renal artery and 2 had early branching of the renal artery. LPNs were completed via the retroperitoneal approach in all the 75 patients without conversion to open or total nephrectomy. Group A and group B showed significant differences in warm ischemic time (26.7∓6.4 vs 31.9∓7.0 min), tumor-excision time (8.4∓2.6 vs 10.4∓2.8 min), renal defect suture time (18.3∓3.9 vs 21.5∓3.4 min), 24-h volume of retroperitoneal drainage (88.6∓40.2 vs 134.3∓58.3 mL) and 48-h volume of retroperitoneal drainage (127.9∓54.5 vs 198.1∓86.3 mL), but not in the demographic data, operation time, intraoperative blood loss or postoperative hospital stay. CONCLUSIONS: 3D reconstruction of the renal masses can be completed efficiently and accurately using this system. Compared with conventional CT-based measurement, 3D spatial measurement of the anatomical structures helps to increase the precision in the performance of LPN and reduce the warm ischemia time.

[Mammography and magnetic resonance imaging for diagnosis of the intraductal papilloma of the breast].

OBJECTIVE: To investigate the features of intraductal papilloma of the breast in mammography and magnetic resonance imaging (MRI) and assess the diagnostic values of the two imaging modalities. METHODS: Fifteen patients with intraductal papilloma of the breast confirmed surgically and pathologically underwent X-ray examination of the breast, and 11 of them also received enhanced MRI. The imaging findings by mammography and MRI were compared. RESULTS: Enhanced MRI clearly displayed the location and morphology of the intraductal papilloma, and 7 patients showed smooth tumor margins and 2 showed irregular margins. On T(1)WI, the lesions were isointense or slightly hypointense, and appeared isointense or slightly hyperintense on T(2)WI. Some of the intraductal papillomas were seen encapsulated in the dilated ductal. The varying enhancement features of the lesions increased the difficulty in distinguishing from carcinoma. Mammography identified intraductal papillomas only in 2 of the 15 cases (13%) with lesion feature similar to that found by MRI. Fine cluster calcification was found in 1 case. CONCLUSION: MRI can more accurately define the location of the lesion than X-ray. In spite of some resemblance in the MRI findings between intraductal papillomas and breast carcinoma, MRI still serves as a useful diagnostic modality for intraductal papilloma that shows some characteristic findings.

[Mammographic findings of gynecomastia].

OBJECTIVE: To investigate the features of gynecomastia displayed by mammography. METHODS: Twelve patients with gynecomastia were examined with a high-performance GITTO-TECH mammograph (IMS Company, Italy), and the results were compared with those obtained from pathological examination. RESULTS: The 12 cases were pathologically confirmed as gynecomastia, 10 of which were also identified by mammography while 2 misdiagnosed as male breast cancer. CONCLUSION: Diagnosis of gynecomastia can be established when typical features are presented in mammography, and fine needle aspiration biopsy can be performed when possible for discrimination from male breast cancer.