Detection and Grading of Prostate Cancer in Temporal Enhanced Ultrasound using Convolutional Neural Networks

Prostate cancer is the most common non-dermatological cancer amongst men in the developed world. The current definitive diagnosis is core needle biopsy guided by transrectal ultrasound. However, this method suffers from low sensitivity and specificity in detecting cancer. Recently, a new ultrasound based tissue typing approach has been proposed, known as temporal enhanced ultrasound (TeUS). In this approach, a set of temporal ultrasound frames is collected from a stationary tissue location without any intentional mechanical excitation. The main aim of this thesis is to implement a deep learning-based solution for prostate cancer detection and grading using TeUS data. In the proposed solution, convolutional neural networks are trained to extract high-level features from time domain TeUS data in temporally and spatially adjacent frames in nine in vivo prostatectomy cases. This approach avoids information loss due to feature extraction and also improves cancer detection rate. The output likelihoods of two TeUS arrangements are then combined to form our novel decision support system. This deep learning-based approach results in the area under the receiver operating characteristic curve (AUC) of 0.80 and 0.73 for prostate cancer detection and grading, respectively, in leave-one-patient-out cross-validation. Recently, multi-parametric magnetic resonance imaging (mp-MRI) has been utilized to improve detection rate of aggressive prostate cancer. In this thesis, for the first time, we present the fusion of mp-MRI and TeUS for characterization of prostate cancer to compensates the deficiencies of each image modalities and improve cancer detection rate. The results obtained using TeUS are fused with those attained using consolidated mp-MRI maps from multiple MR modalities and cancer delineations on those by multiple clinicians. The proposed fusion approach yields the AUC of 0.86 in prostate cancer detection. The outcomes of this thesis emphasize the viable potential of TeUS as a tissue typing method. Employing this ultrasound-based intervention, which is non-invasive and inexpensive, can be a valuable and practical addition to enhance the current prostate cancer detection.

Genetic Polymorphisms in Replication-related Genes and Risk of Breast Cancer Among European and East Asian Women

Background: The role of common, low to intermediate risk alleles in breast cancer need to be examined due to their relatively high prevalence. Among many cellular pathways, replication has a pivotal role in cell division and frequently targeted during carcinogenesis. Replication is governed by a host of genes involved in a number of different pathways. This study investigates the effects of replication-gene variants in relation to breast cancer and how this relationship is affected by ethnicity, menopausal status and breast tumour subtype. Methods: Data from a case-control study with 997 incident breast cancer cases and 1,050 age frequency matched controls in Vancouver, British Columbia and Kingston, Ontario were used. Unconditional logistic regression was used to calculate odds ratios between 45 replication gene variants and breast cancer risk, assuming an additive genetic model adjusted for age and centre, presented for Europeans and East Asians separately. Polytomous logistic regression was used to assess odds ratios between each SNP and four breast cancer subtypes defined by hormone receptor status among Europeans. All analyses were stratified by menopausal status. The Benjamini–Hochberg false discovery rate (FDR) was used to address multiple comparisons. Results: Among Europeans, the SNPs in FGFR2, TOX3 and 11q13 loci were associated with breast cancer after controlling for multiple comparisons. Test of heterogeneity showed the SNPs rs1045185, rs4973768, rs672888, rs1219648, rs2420946 among Europeans and rs889312 among East Asians conferred differential risk across the tumour subtypes. Conclusions: Specific SNPs in replication genes were associated with breast cancer, and the risk level differed by tumour subtype defined by ER/PR/Her2 status and ethnicity.