Prediction of recurrence of non-small cell lung cancer

An apparatus, comprising:

• a processor;
• a memory;
• an input/output interface;
• a set of circuits, where the set of circuits includes an image acquisition circuit, a segmentation circuit, a feature extraction circuit, and a classification circuit;
• and
• an interface to connect the processor, the memory, the input/output interface and the set of circuits:
• where the image acquisition circuit accesses a set of images of a region of tissue demonstrating non-small cell lung cancer (NSCLC), where a member of the set of images is a digitized whole slide image (WSI) of a hematoxylin and eosin (H&E) stained pathology slice of an NSCLC tumor, where the member of the set images includes a set of features;
• where the segmentation circuit detects a cellular nucleus in the member of the set of images, and where the segmentation circuit generates a segmented nucleus based on the detected cellular nucleus;
• where the feature extraction circuit extracts a set of discriminative features from the segmented nucleus, and where the feature extraction circuit provides the set of discriminative features to the classification circuit;
• and
• where the classification circuit computes a probability that the region of tissue will experience NSCLC recurrence using a quadratic discriminant analysis (QDA), a linear discriminant analysis (LDA), or a random forest, where the probability is based, at least in part, on the set of discriminative features.

A method for generating a personalized treatment plan for a patient demonstrating non-small cell lung cancer (NSCLC), the method comprising:

• accessing a diagnostic image of a region of tissue demonstrating early stage NSCLC;
• defining a cellular nucleus represented in the diagnostic image using a watershed segmentation approach;
• extracting a set of discriminative features from the cellular nucleus represented in the image, where the set of discriminative features includes a texture feature, a shape feature, a nuclear architecture feature, or an intensity feature;
• computing a probability that the region of tissue will experience NSCLC recurrence using a machine learning classifier, where the machine learning classifier is a quadratic discriminant analysis classifier, a support vector machine (SVM), a linear discriminant classifier, or a random forest classifier, where the probability is based, at least in part, on the set of discriminative features;
• and
• generating a personalized treatment plan based, at least in part, on the probability and the diagnostic image.