Radiomics from Various Tumour Volume Sizes for Prognosis Prediction of Head and Neck Squamous Cell Carcinoma: A Voted Ensemble Machine Learning Approach.

Background: Traditionally, cancer prognosis was determined by tumours size, lymph node spread and presence of metastasis (TNM staging). Radiomics of tumour volume has recently been used for prognosis prediction. In the present study, we evaluated the effect of various sizes of tumour volume. A voted ensemble approach with a combination of multiple machine learning algorithms is proposed for prognosis prediction for head and neck squamous cell carcinoma (HNSCC). Methods: A total of 215 HNSCC CT image sets with radiotherapy structure sets were acquired from The Cancer Imaging Archive (TCIA). Six tumour volumes, including gross tumour volume (GTV), diminished GTV, extended GTV, planning target volume (PTV), diminished PTV and extended PTV were delineated. The extracted radiomics features were analysed by decision tree, random forest, extreme boost, support vector machine and generalized linear algorithms. A voted ensemble machine learning (VEML) model that optimizes the above algorithms was used. The receiver operating characteristic area under the curve (ROC-AUC) were used to compare the performance of machine learning methods, including accuracy, sensitivity and specificity. Results: The VEML model demonstrated good prognosis prediction ability for all sizes of tumour volumes with reference to GTV and PTV with high accuracy of up to 88.3%, sensitivity of up to 79.9% and specificity of up to 96.6%. There was no significant difference between the various target volumes for the prognostic prediction of HNSCC patients (chi-square test, p > 0.05). Conclusions: Our study demonstrates that the proposed VEML model can accurately predict the prognosis of HNSCC patients using radiomics features from various tumour volumes.

Assessment of left and right ventricular systolic and diastolic synchronicity in normal subjects by tissue Doppler echocardiography and the effects of age and heart rate.

OBJECTIVES: This study examined inter- and intraventricular synchronicity in systole and diastole by tissue Doppler imaging (TDI), and investigated if these parameters and the regional velocities were affected by age and heart rate. METHODS: TDI was performed in 106 normal subjects (64.3 +/- 9.5 years, 60% male) using three apical views and a six-basal, six mid-segmental model. The regional parameters measured off line in both ventricles included peak isovolumic contraction velocity IVC(M), peak sustained systolic velocity (SM), peak early diastolic velocity (EM), peak late diastolic velocity (AM), and the E/AM ratio, as well as their time to these peak velocities: T(IVC), T(S), T(E), and T(A). RESULTS: The systole and diastole within the left ventricle (LV) was highly synchronized without difference in T (IVC), TS, TE, and TA. However, the right ventricle (RV) was about 20 msec later than the LV for T(IVC) and TS. For regional velocities, IVC(M), S(M), E(M), and A(M) were significantly higher in basal than mid-segments (all P < 0.001). In the base of the LV, SM, and EM were the highest at the lateral segment and the lowest at the anterolateral segment. Age and heart rate did not affect systolic velocities or the timing of events. In diastole, age correlated negatively with EM(r =-0.36 to -0.48, P