Lung cancer lesion detection in histopathology images using graph-based sparse PCA network.

Early detection of lung cancer is critical for improvement of patient survival. To address the clinical need for efficacious treatments, genetically engineered mouse models (GEMM) have become integral in identifying and evaluating the molecular underpinnings of this complex disease that may be exploited as therapeutic targets. Assessment of GEMM tumor burden on histopathological sections performed by manual inspection is both time consuming and prone to subjective bias. Therefore, an interplay of needs and challenges exists for computer-aided diagnostic tools, for accurate and efficient analysis of these histopathology images. In this paper, we propose a simple machine learning approach called the graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E). Our method comprises four steps: 1) cascaded graph-based sparse PCA, 2) PCA binary hashing, 3) block-wise histograms, and 4) support vector machine (SVM) classification. In our proposed architecture, graph-based sparse PCA is employed to learn the filter banks of the multiple stages of a convolutional network. This is followed by PCA hashing and block histograms for indexing and pooling. The meaningful features extracted from this GS-PCA are then fed to an SVM classifier. We evaluate the performance of the proposed algorithm on H&E slides obtained from an inducible K-rasG12D lung cancer mouse model using precision/recall rates, Fß-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC) and show that our algorithm is efficient and provides improved detection accuracy compared to existing algorithms.

Chemotherapy-Induced Alopecia: Examining Patient Perceptions and Adherence to Home Haircare Recommendations.

BACKGROUND: Chemotherapy-induced alopecia is one of the most distressing side effects experienced by patients with cancer. Although most chemotherapy-induced alopecia is temporary, this side effect can cause significant anxiety and may lead to refusal of curative treatment. OBJECTIVES: The purpose of this study was to examine patient perceptions and measure adherence to haircare recommendations throughout the course of treatment while using scalp cooling therapy in addition to learning which haircare recommendations were the most onerous. METHODS: This was a cross-sectional observational and descriptive study that used repeated-measures survey data. Participants completed electronic surveys during each treatment corresponding with the current treatment phase. FINDINGS: Final survey results revealed that most participants adhered to haircare recommendations with little difficulty and that the recommendations had an insignificant impact on daily lives.