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.

A method for three-dimensional surface reconstruction of volume data field.

OBJECTIVE: To explore a novel method of three-dimensional (3D) reconstruction based on vector field smoothing, for the purpose of 3D surface reconstruction of DICOM format volume data sets. METHODS: 3D external surface of three sets of volume data, namely craniocerebral volume data, pelvis volume data, and rat embryo volume data, were respectively extracted by Marching Cubes algorithm using small triangle flakes to approach the original 3D structure surfaces. Vector field smoothing was performed on the extracted 3D surfaces. The reconstructed 3D structures were rendered from different angles of view through arbitrary rotation. RESULTS: High-quality results of 3D surface reconstruction were obtained for each set of volume data, demonstrating fine 3D surface details and high fidelity. CONCLUSION: This method can improve 3D surface reconstruction from DICOM volume data sets, promising high quality, fidelity and reality.

Registration of serial tissue section images.

OBJECTIVE: This study aims to tackle the problem of image registration during computer-assisted three-dimensional (3D) reconstruction of serial tissue sections. METHODS: We proposed segmentation-counting algorithm for computerized image registration on the basis of joint histogram. This approach utilizes thresholding of the 2 images to be registered, and the criterion function is defined as the counting in a specific region of the joint histogram. The registration parameters can be obtained by optimizing the criterion function. RESULTS: In the trial application of this approach in image registration for the serial tissue sections of mouse wse embryos, a more efficient result was achieved. CONCLUSION: The method can rapidly accomplish the image registration task for serial tissue sections with simpler calculation processes.

Elastic registration of medical images through multiquadric method.

OBJECTIVE: To improve the precision and reliability of elastic registration of the medical images and to simplify the registration process. METHODS: Previous study concerning elastic registration mostly focused on manual selection of the landmarks and then use of adequate interpolating for elastic transformation. The landmarks extraction, however, was prone to error that often showed impact on the registration results, besides the difficulty and time consumption of manual identification of the landmarks. On the basis of Multiquadric method that allowed smooth adjustment of the parameters, we utilized a semi-automatic method to extract the landmarks by combining these 2 steps, and proposed a novel registration method. RESULTS: Using this method for medical image elastic registration, rapid and accurate registration between standard and deformed images was achieved. CONCLUSION: The method proposed presently is accurate, convenient and reliable.