Preliminary study of tumor heterogeneity in imaging predicts two year survival in pancreatic cancer patients - Fig 2

<p>(a) Extracted CT slice after acquisition, (b) magnified view of tumor region with (top) and without (bottom) the manually drawn boundary, (c) 3-D view of manually segmented pancreas with tumor, (d) 2-D slices of tumor.</p

Exemplar tumors with rendered texture features displayed by converting data into gray levels with range [0, 255].

<p>Resultant matrices rendered from GLCM, RLM, ACM1, and ACM2. Histogram used in the derivation of IH features. LBP and FD values at each pixel. Gradient angle computed with Sobel operator on each pixel used in ACM1 and ACM2 features. Gradient magnitude computed with Sobel operator on each pixel used in ACM2 features.</p

The area under ROC, classification accuracy (as a percentage), sensitivity, and specificity obtained with the proposed method using leave-one-image-out technique.

<p>The maximum <i>AUC</i> and <i>Ac</i> were highlighted with bold face. ‘***’ corresponds no outcome due to no features selected.</p

The area under ROC, classification accuracy (as a percentage), sensitivity, and specificity obtained with fMRMR feature selection and naive Bayes classification using leave-one-image-out and three-fold cross-validation techniques.

<p>The maximum AUC and <i>Ac</i> are highlighted with bold face.</p

List of features selected with >0.5 probability by the model.

<p>List of features selected with >0.5 probability by the model.</p

Correlation of pre-treatment patient factors with survival.

<p>Correlation of pre-treatment patient factors with survival.</p

ROC curves obtained with different feature sets extracted from the tumor region using (a) leave-one-image-out and (b) three-fold cross-validation techniques.

<p>ROC curves obtained with different feature sets extracted from the tumor region using (a) leave-one-image-out and (b) three-fold cross-validation techniques.</p

The area under ROC, classification accuracy (as a percentage), sensitivity, and specificity obtained with fMRMR feature selection and SVM classification using leave-one-image-out and three-fold cross-validation techniques.

<p>The maximum AUC and <i>Ac</i> are highlighted with bold face.</p

Schematic of the methods.

<p>Schematic of the methods.</p