4 interesting papers in cancer detection using advanced analytics and AI
Below are 4 interesting papers from Lee Hwee Kuan’s team over at A*STAR BII on cancer detection using advanced analytics/AI techniques.
1) Nuclear Pleomorphism in Renal Clear Cell Cancer
The characteristics of the nuclei are often observed by pathologists when they assess the progression and presence of cancer cells in tissue biopsies. Cancerous tissue typically contains cells with enlarged, irregularly-shaped (pleomorphic) and darkly-stained (hyperchromasia) nuclei with prominent nucleoli. However, at different stages of the disease, the nuclear structure and prominence of nucleoli can change. The Fuhrman grading system for clear cell Renal Cell Carcinoma (ccRCC) was developed around these observed changes in the nuclei. It provides rules to classify the different stages of disease progression. Early stage ccRCC tumors typically have small, round nuclei with inconspicuous nucleoli, while late stage tumors have enlarged and irregularly-shaped nuclei with prominent nucleoli. Following on from our work on nucleoli detection, we have developed new machine learning methodologies to perform automatic grading of ccRCC histopathological images. From the histopathological images, we extract features describing the properties of multiple nuclei concurrently. This enables us to train classifiers that can distinguish the level of pleomorphism of the nuclei in the tissue sample, resulting in a higher accuracy in the automated grading.
Accepted for publication as:
Daniel Aitor Holdbrook, Malay Singh, Yukti Choudhury, Emarene Mationg Kalaw, Valerie Koh, Hui Shan Tan, Ravindran Kanesvaran, Puay Hoon Tan, John Yuen Shyi Peng, Min-Han Tan, and Hwee Kuan Lee. Automated renal cancer grading using nuclear pleomorphic patterns. JCO Clinical Cancer Informatics. 2018.
2) Automated Image Based Tumor Risk Assessment System for Hepatocellular Carcinoma
The evaluation of both asymptomatic patients and those with symptoms of liver disease involves blood testing and imaging evaluation. We developed an automated image based tumor risk assessment system as part of a micro-array gene expression based prognostic stratification system for resectable hepatocellular carcinoma. Whole slide images of liver cancer tissue were divided into two groups namely “Low Risk” and “High Risk” by micro-array gene expression based prognostic stratification system. These slides were then immunohistochemically (IHC) stained for different biomarker proteins. We developed an automated image based system to analyse the biomarker protein content. Our system predicted a Support Vector Regression (SVR) based score for each IHC image after quantification and analysis of stain. Our system was able to predict a higher SVR score for “High risk” patients when compared to “Low Risk” patients.
Published as:
Oleg V. Grinchuk, Surya Pavan Yenamandra, Ramakrishnan Iyer, Malay Singh, Hwee Kuan Lee, Igor V. Kurochkin, Kiat Hon Lim, Pierce K. H. Chow, and Vladimir A. Kuznetsov. Tumor-adjacent tissue co-expression profile analysis reveals pro-oncogenic gene signature for prognosis of resectable hepatocellular carcinoma. Molecular Oncology. 2017
Link:
http://onlinelibrary.wiley.
3) Gland Segmentation in Prostate Histopathological Images
Glandular structural features are important for the tumor pathologist in the assessment of cancer malignancy of prostate tissue slides. The varying shape and size of glands combined with tedious manual task can result in inaccurate assessment. There are also discrepancies and low level agreement among pathologists especially in cases of Gleason pattern 3 and pattern 4 prostate adenocarcinoma. We have developed an intelligent software to improve accuracy and reduce labor of gland structure assessment on Haematoxylin and Eosin (H&E) stained prostate tissue slides. Our method can easily fit into the existing workflow of the pathologist. Prostate cancer glands with their varying shapes, structures, and size pose an extreme challenge for automated gland segmentation systems. Our method achieved an averaged Jaccard Index score of 0.54 (range is [0,1], higher value is better) while outperforming various existing softwares in the literature.
Published as:
Malay Singh, Emarene Mationg Kalaw, Danilo Medina Giron, Kian-Tai Chong, Chew Lim Tan, and Hwee Kuan Lee. Gland segmentation in prostate histopathological images. Journal of Medical Imaging. 4(2), 027501, 2017.
Link:
dx.doi.org/10.1117/1.JMI.4.2.
4) Automated Image Based Prominent Nucleoli Detection
The diagnosis and prognosis of cancers are major issue for a trained pathologist. Inter-observer variability and tediousness of tissue reading hamper the accuracy of assessment by the pathologist. The analysis of prominent nucleoli is one of the main methods of cancer assessment. We have developed an intelligent software to improve accuracy and reduce labor of tissue reading of prominent nucleoli assessment on H&E
stained slides. Our method can easily fit into the existing workflow of the pathologists work.
Published as:
a)
Choon Kong Yap, Emarene Mationg Kalaw, Malay Singh, Kian-Tai Chong, Danilo Medina Giron, Chao-Hui Huang, Li Cheng, Yan Nei Law, and Hwee Kuan Lee.
Automated Image Based Prominent Nucleoli Detection.
Journal of Pathology Informatics 6.1 (2015): 39. (JPI)
Link:
http://www.jpathinformatics.
and
b)
Malay Singh, Zeng Zeng, Emarene Mationg Kalaw, Danilo Medina Giron, Kian-Tai Chong, and Hwee Kuan Lee. A study of nuclei classification methods in histopathological images.
International Conference on Innovation in Medicine and Healthcare.
Link:
https://link.springer.com/
