MRI-Based Deep Learning Segmentation and Radiomics of Sarcoma in Mice.

Small-animal imaging is an essential tool that provides noninvasive, longitudinal insight into novel cancer therapies. However, considerable variability in image analysis techniques can lead to inconsistent results. We have developed quantitative imaging for application in the preclinical arm of a coclinical trial by using a genetically engineered mouse model of soft tissue sarcoma. Magnetic resonance imaging (MRI) images were acquired 1 day before and 1 week after radiation therapy. After the second MRI, the primary tumor was surgically removed by amputating the tumor-bearing hind limb, and mice were followed for up to 6 months. An automatic analysis pipeline was used for multicontrast MRI data using a convolutional neural network for tumor segmentation followed by radiomics analysis. We then calculated radiomics features for the tumor, the peritumoral area, and the 2 combined. The first radiomics analysis focused on features most indicative of radiation therapy effects; the second radiomics analysis looked for features that might predict primary tumor recurrence. The segmentation results indicated that Dice scores were similar when using multicontrast versus single T2-weighted data (0.863 vs 0.861). One week post RT, larger tumor volumes were measured, and radiomics analysis showed greater heterogeneity. In the tumor and peritumoral area, radiomics features were predictive of primary tumor recurrence (AUC: 0.79). We have created an image processing pipeline for high-throughput, reduced-bias segmentation of multiparametric tumor MRI data and radiomics analysis, to better our understanding of preclinical imaging and the insights it provides when studying new cancer therapies.

Decreasing cytokeratin 17 expression in head and neck cancer predicts nodal metastasis and poor prognosis: The first evidence.

OBJECTIVES: Cytokeratins (CKs) are mainly expressed in epithelial carcinomas and are valuable for making diagnoses and identifying metastatic status. Changes in the expression of individual CKs in certain carcinoma may be relevant to establishing a prognosis. However, the prognostic significance of CKs in head and neck squamous cell carcinoma (HNSCC) remains elusive. Herein, we investigated the diverse and unique expression patterns of Cytokeratin 13 (CK13) and Cytokeratin 17 (CK17) and assessed the role of CK17 as a predictor for HNSCC metastasis and prognosis. METHODS: CK13 and CK17 expressions were evaluated using immunohistochemical tissue microarray (TMA) analysis with 106 patients of HNSCC. To clarify the characterisation of CK17 expression with respect to its ability in predicting metastatic disease, an in vitro study of cells migration/invasion assays was conducted. Furthermore, the correlation of CK17 expression to clinicopathologic variables and prognosis was analyzed using a serial statistical method. RESULTS: CK13 was predominately expressed in non-cancerous tissues and was lost in HNSCC. Decreasing expression of CK17 correlated with cancerous cell migration and invasion (P < .0001) in an in vitro study. CK17 expression was lower in the N1 and N2 nodal metastases category compared to the N0 stage. Moreover, Kaplan-Meier survival analyses showed that a lower CK17 expression was associated with a poorer survival connotation in HNSCC patients (P < .05) with 10-year follow-up. CONCLUSION: Our findings provide the first evidence that CK17 under-expression might be a potential predictor of nodal metastasis and adverse prognosis.