Whole Slide Images (WSIs) have been widely used in computational pathology (CPath) for various tasks. However, obtaining high-quality annotations remains a major bottleneck. Task-aware unsupervised anomaly detection models offer a promising alternative, as they are trained solely on task-specific normal data and can be adapted to clinically defined objectives, such as cancer detection, depending on the problem formulation. Despite this potential, anomaly detection models have not been thoroughly explored in the context of WSIs. Existing approaches often directly adopt techniques from other domains, leading to suboptimal performance due to domain discrepancies and the unique characteristics of WSIs. Given that feature reconstruction-based methods have become popular in anomaly detection research, this study first analyzes the designs of such models in the context of conditional reconstruction, revealing the potential directions to adapt and further improve these models. Based on our analysis, we revisit and refine them to better accommodate the distinct properties of WSIs. Moreover, we propose an Explicit Conditional Reconstruction framework, termed as ECR4AD, which can significantly enhance model performance. Our method is comprehensively evaluated on four datasets covering breast and prostate cancer metastasis detection, as well as Gleason grading of prostate cancer, all conducted at the tile level on images extracted from WSIs. The experimental results show that ECR4AD consistently achieves substantial improvements in AUROC across all datasets, demonstrating its effectiveness for tile-level task-aware unsupervised anomaly detection in CPath. The code can be found at https://github.com/uobinxiao/wsi_anomaly_detection.git.

BACKGROUND: Chromothripsis, a newly discovered type of complex genomic rearrangement, has been implicated in the evolution of several types of cancers. To date, it has been described in bone cancer, SHH-medulloblastoma and acute myeloid leukemia, amongst others, however there are still no formal or automated methods for detecting or annotating it in high throughput sequencing data. As such, findings of chromothripsis are difficult to compare and many cases likely escape detection altogether. RESULTS: We introduce ShatterProof, a software tool for detecting and quantifying chromothriptic events. ShatterProof takes structural variation calls (translocations, copy-number variations, short insertions and loss of heterozygosity) produced by any algorithm and using an operational definition of chromothripsis performs robust statistical tests to accurately predict the presence and location of chromothriptic events. Validation of our tool was conducted using clinical data sets including matched normal, prostate cancer samples in addition to the colorectal cancer and SCLC data sets used in the original description of chromothripsis. CONCLUSIONS: ShatterProof is computationally efficient, having low memory requirements and near linear computation time. This allows it to become a standard component of sequencing analysis pipelines, enabling researchers to routinely and accurately assess samples for chromothripsis. Source code and documentation can be found at http://search.cpan.org/~sgovind/Shatterproof.

Prostate cancer is a leading cause of cancer death for men in the United States. Fortunately, the survival rate for early diagnosed patients is relatively high. Therefore, in vivo imaging plays an important role for the detection and treatment of the disease. Accurate prostate cancer localization with noninvasive imaging can be used to guide biopsy, radiotherapy, and surgery as well as to monitor disease progression. Magnetic resonance imaging (MRI) performed with an endorectal coil provides higher prostate cancer localization accuracy, when compared to transrectal ultrasound (TRUS). However, in general, a single type of MRI is not sufficient for reliable tumor localization. As an alternative, multispectral MRI, i.e., the use of multiple MRI-derived datasets, has emerged as a promising noninvasive imaging technique for the localization of prostate cancer; however almost all studies are with human readers. There is a significant inter and intraobserver variability for human readers, and it is substantially difficult for humans to analyze the large dataset of multispectral MRI. To solve these problems, this study presents an automated localization method using cost-sensitive support vector machines (SVMs) and shows that this method results in improved localization accuracy than classical SVM. Additionally, we develop a new segmentation method by combining conditional random fields (CRF) with a cost-sensitive framework and show that our method further improves cost-sensitive SVM results by incorporating spatial information. We test SVM, cost-sensitive SVM, and the proposed cost-sensitive CRF on multispectral MRI datasets acquired from 21 biopsy-confirmed cancer patients. Our results show that multispectral MRI helps to increase the accuracy of prostate cancer localization when compared to single MR images; and that using advanced methods such as cost-sensitive SVM as well as the proposed cost-sensitive CRF can boost the performance significantly when compared to SVM.