Prognostic prediction of left ventricular myocardial noncompaction using machine learning and cardiac magnetic resonance radiomics.

Background: Although there are many studies on the prognostic factors of left ventricular myocardial noncompaction (LVNC), the determinants are varied and not entirely consistent. This study aimed to build predictive models using radiomics features and machine learning to predict major adverse cardiovascular events (MACEs) in patients with LVNC. Methods: In total, 96 patients with LVNC were included and randomly divided into training and test cohorts. A total of 105 cine cardiac magnetic resonance (CMR)-derived radiomics features and 35 clinical characteristics were extracted. Five different oversampling algorithms were compared for selection of the optimal imbalanced processing. Feature importance was assessed with extreme gradient boosting (XGBoost). We compared the performance of 5 machine learning classification methods with different sample:feature ratios to determine the optimal hybrid classification strategy. Subsequently, radiomics, clinical, and combined radiomics-clinical models were developed and compared. Results: The machine learning pipeline included an adaptive synthetic (ADASYN) algorithm for imbalanced processing, XGBoost feature selection with a sample:feature ratio of 10, and support vector machine (SVM) modeling. The areas under the receiver operating characteristic curves (AUCs) of the radiomics model, clinical model, and combined model in the validation cohort were 0.87 (sensitivity 83.33%, specificity 64.29%), 0.65 (sensitivity 16.67%, specificity 78.57%), and 0.92 (specificity 33.33%, sensitivity 100.00%), respectively. The radiomics model performed similarly to the clinical and combined models (P=0.124 and P=0.621, respectively). The performance of the combined model was significantly better than that of the clinical model (P=0.003). Conclusions: The machine learning-based cine CMR radiomics model performed well at predicting MACEs in patients with LVNC. Adding radiomics features offered incremental prognostic value over clinical factors alone.

Non-coding RNAS and colorectal cancer liver metastasis.

More than 50% of colorectal cancer (CRC) deaths are attributed to metastasis, and the liver is the most common distant metastatic site of CRC. The molecular mechanisms underlying CRC liver metastasis are very complicated and remain largely unknown. Accumulated evidence has shown that non-coding RNAs (NcRNAs) play critical roles in tumor development and progression. Here we reviewed the roles and underlying mechanisms of NcRNAs in CRC liver metastasis.

Pre-treatment ADC image-based random forest classifier for identifying resistant rectal adenocarcinoma to neoadjuvant chemoradiotherapy.

OBJECTIVE: To develop a predicting model for tumor resistance to neoadjuvant chemoradiotherapy (NCRT) in locally advanced rectal cancer (LARC) by using pre-treatment apparent diffusion coefficient (ADC) image-derived radiomics features. METHOD: A total of 89 patients with LARC were randomly assigned into training (N = 66) and testing cohorts (N = 23) at the ratio of 3:1. Radiomics features were derived from manually determined tumor region of pre-treatment ADC images. Random forest algorithm was used to determine the most relevant features and then to construct a predicting model for identifying resistant tumor. Stability and diagnostic performance of the random forest model was evaluated with the testing cohort. RESULTS: The top 10 most relevant features (entropymean, inverse variance, energymean, small area emphasis, ADCmin, ADCmean, sdGa02, small gradient emphasis, age, and size) were determined from clinical characteristics and 133 radiomics features. In the prediction of resistant tumor of the testing cohort, the random forest model constructed based on these most relevant features achieved an area under the receiver operating characteristic curve of 0.83, with the highest accuracy of 91.3%, a sensitivity of 88.9%, and a specificity of 92.8%. CONCLUSION: The random forest classifier based on radiomics features derived from pre-treatment ADC images have the potential to predict tumor resistance to NCRT in patients with LARC, and the use of predicting model may facilitate individualized management of rectal cancer.

PIK3CD induces cell growth and invasion by activating AKT/GSK-3ß/ß-catenin signaling in colorectal cancer.

The catalytic subunit p110δ of phosphoinositide 3-kinase (PI3K) encoded by PIK3CD has been implicated in some human solid tumors. However, its roles in colorectal cancer (CRC) remain largely unknown. Here we found that PIK3CD was overexpressed in colon cancer tissues and CRC cell lines and was an independent predictor for overall survival (OS) of patients with colon cancer. The ectopic overexpression of PIK3CD significantly promoted CRC cell growth, migration and invasion in vitro and tumor growth in vivo. In contrast, inhibition of PIK3CD by specific small-interfering RNA or idelalisib dramatically suppressed CRC cell growth, migration and invasion in vitro and tumor growth in vivo. Moreover, PIK3CD overexpression increased AKT activity, nuclear translocation of ß-catenin and T-cell factor/lymphoid enhancer factor (TCF/LEF) transcriptional activity and decreased glycogen synthase kinase 3ß (GSK-3ß) activity, whereas PIK3CD inhibition exhibited the opposite effects. Furthermore, PIK3CD-mediated cell growth, migration and invasion were reversed by blockade of AKT signaling or depletion of ß-catenin. In addition, PIK3CD expression in colon cancer tissues positively correlated with ß-catenin abnormal expression, which was an independent predictor for OS of colon cancer patients. Taken together, our findings demonstrate that PIK3CD is an independent prognostic factor in CRC and that PIK3CD induces CRC cell growth, migration and invasion by activating AKT/GSK-3ß/ß-catenin signaling, suggesting that PIK3CD might be a novel prognostic biomarker and a potential therapeutic target for CRC.