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The incidence rate of diagnosed thyroid cancer has increased over the last decades. Although ultrasonic imaging has increased the malignancy detection rate, current ultrasonography markers do not provide a sufficient level of diagnostic accuracy to replace the fine needle aspiration biopsy. Recently, studies have reported that significant differences were observed in the values of quantitative ultrasound (QUS) parameters derived from a thyroid cancer rodent model between normal/benign and malignant tissues. In the present study, the performance of a multi-parametric classification for the differentiation of thyroid cancer in this rodent model has been evaluated. The experimental database consisted of 32 mice having different predispositions to developing thyroid abnormalities; 6 of them developed thyroid cancer papillary carcinoma (PTC), 5 follicular variant papillary thyroid carcinoma (FV-PTC), 6 developed benign tumors (c-cell adenoma) and 15 did not develop any thyroid abnormalities. Backscattered data was obtained from excised thyroid tissues using a 40 MHz, f/3 single element transducer. A total of five QUS parameters were derived from the ultrasound data: two from backscatter coefficients (i.e., the effective scatterer diameter (ESD) and effective acoustic concentration (EAC)), two from envelope statistics (i.e., the μ and k parameters), and one from ultrasound attenuation (i.e., attenuation coefficient slope). A two-class classification between normal/benign and malignant cases was performed using linear discriminant analysis with both one- and two-dimensional feature spaces. When using a two-dimensional feature space, it was found that the combination of EAC and 10/μ resulted in both a sensitivity and specificity of 100%.

Understanding the spatial distribution of prostate cancer and how it changes according to prostate specific antigen (PSA) values, Gleason score, and other clinical parameters may help comprehend the disease and increase the overall success rate of biopsies. This work aims to build 3D spatial distributions of prostate cancer and examine the extent and location of cancer as a function of independent clinical parameters. The border of the gland and cancerous regions from wholemount histopathological images are used to reconstruct 3D models showing the localization of tumor. This process utilizes color segmentation and interpolation based on mathematical morphological distance. 58 glands are deformed into one prostate atlas using a combination of rigid, affine, and b-spline deformable registration techniques. Spatial distribution is developed by counting the number of occurrences in a given position in 3D space from each registered prostate cancer. Finally a difference between proportions is used to compare different spatial distributions. Results show that prostate cancer has a significant difference (SD) in the right zone of the prostate between populations with PSA greater and less than 5ng/ml. Age does not have any impact in the spatial distribution of the disease. Positive and negative capsule-penetrated cases show a SD in the right posterior zone. There is SD in almost all the glands between cases with tumors larger and smaller than 10% of the whole prostate. A larger database is needed to improve the statistical validity of the test. Finally, information from whole-mount histopathological images may provide better insight into prostate cancer.

A methodology to study the relationship between clinical variables [e.g., prostate specific antigen (PSA) or Gleason score] and cancer spatial distribution is described. Three-dimensional (3-D) models of 216 glands are reconstructed from digital images of whole mount histopathological slices. The models are deformed into one prostate model selected as an atlas using a combination of rigid, affine, and B-spline deformable registration techniques. Spatial cancer distribution is assessed by counting the number of tumor occurrences among all glands in a given position of the 3-D registered atlas. Finally, a difference between proportions is used to compare different spatial distributions. As a proof of concept, we compare spatial distributions from patients with PSA greater and less than [Formula: see text] and from patients older and younger than 60 years. Results suggest that prostate cancer has a significant difference in the right zone of the prostate between populations with PSA greater and less than [Formula: see text]. Age does not have any impact in the spatial distribution of the disease. The proposed methodology can help to comprehend prostate cancer by understanding its spatial distribution and how it changes according to clinical parameters. Finally, this methodology can be easily adapted to other organs and pathologies.