Mohammed Nimir

Ruqayya Awan, Mohammed Nimir, Shan E Ahmed Raza et al.

The accurate diagnosis and molecular profiling of colorectal cancers are critical for planning the best treatment options for patients. Microsatellite instability (MSI) or mismatch repair (MMR) status plays a vital role in appropriate treatment selection, has prognostic implications and is used to investigate the possibility of patients having underlying genetic disorders (Lynch syndrome). NICE recommends that all CRC patients should be offered MMR/MSI testing. Immunohistochemistry is commonly used to assess MMR status with subsequent molecular testing performed as required. This incurs significant extra costs and requires additional resources. The introduction of automated methods that can predict MSI or MMR status from a target image could substantially reduce the cost associated with MMR testing. Unlike previous studies on MSI prediction involving training a CNN using coarse labels (MSI vs Microsatellite Stable (MSS)), we have utilised fine-grain MMR labels for training purposes. In this paper, we present our work on predicting MSI status in a two-stage process using a single target slide either stained with CK8/18 or H&E. First, we trained a multi-headed convolutional neural network model where each head was responsible for predicting one of the MMR protein expressions. To this end, we performed the registration of MMR stained slides to the target slide as a pre-processing step. In the second stage, statistical features computed from the MMR prediction maps were used for the final MSI prediction. Our results demonstrated that MSI classification can be improved by incorporating fine-grained MMR labels in comparison to the previous approaches in which only coarse labels were utilised.

Simon Graham, Mostafa Jahanifar, Ayesha Azam et al.

The development of deep segmentation models for computational pathology (CPath) can help foster the investigation of interpretable morphological biomarkers. Yet, there is a major bottleneck in the success of such approaches because supervised deep learning models require an abundance of accurately labelled data. This issue is exacerbated in the field of CPath because the generation of detailed annotations usually demands the input of a pathologist to be able to distinguish between different tissue constructs and nuclei. Manually labelling nuclei may not be a feasible approach for collecting large-scale annotated datasets, especially when a single image region can contain thousands of different cells. However, solely relying on automatic generation of annotations will limit the accuracy and reliability of ground truth. Therefore, to help overcome the above challenges, we propose a multi-stage annotation pipeline to enable the collection of large-scale datasets for histology image analysis, with pathologist-in-the-loop refinement steps. Using this pipeline, we generate the largest known nuclear instance segmentation and classification dataset, containing nearly half a million labelled nuclei in H&E stained colon tissue. We have released the dataset and encourage the research community to utilise it to drive forward the development of downstream cell-based models in CPath.