NUMODRIL - Nuclear Morphology Optimized Deep Hybrid Learning

Research work link - https://www.biorxiv.org/content/10.1101/2020.11.23.393660v1

Nuclear Morphology Optimized Deep Hybrid Learning (NUMODRIL): Development of a novel architecture for accurate diagnosis and prognosis of Ovarian Cancer Duhita Sengupta1#, , 4#, SkNishan Ali2#, Aditya Bhattacharya2#, Joy Mustafi2#, Asima Mukhopadhyay3,4¶ & Kaushik Sengupta1*

1. Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, Kolkata;HomiBhaba National Institute, Mumbai, India
2. Artificial Intelligence and Machine Learning Division, MUST Research Trust, Flat 301, Block 4, Hyderabad 500046, Telangana, India
3. Kolkata Gynecological Oncology Trials & Translational Research Group, Chittaranjan National Cancer Institute, Kolkata, West Bengal 700026, India
4. Northern Gynaecological Oncology Centre, Queen Elizabeth Hospital, Gateshead, NE9 6SX United Kingdom

¶Formerly at Tata Medical Center, Kolkata, West Bengal 700156, India *To whom correspondence should be addressed: [email protected]

Nuclear morphometric features are potent determining factors for clinical diagnostic approaches adopted by pathologists to analyse the malignant potential of cancer cells. Considering the structural alteration of nucleus in cancer cells, various groups have developed machine learning techniques based on variation in nuclear morphometric information like nuclear shape, size, nucleus-cytoplasm ratio and various non-parametric methods like deep learning have also been tested for analysing immunohistochemistry images of tissue samples for diagnosing various cancers. Our aim is to study the morphometric distribution of nuclear lamin proteins as a specific parameter in ovarian cancer tissues. Besides being the principal mechanical component of the nucleus, lamins also present a platform for binding of proteins and chromatin thereby serving a wide range of nuclear functions like maintenance of genome stability, chromatin regulation. Altered expression of lamins in different subtypes of cancer is now evident from data across the world. It has already been elucidated that in ovarian cancer, extent of alteration in nuclear shape and morphology can determine degree of genetic changes and thus can be utilized to predict the outcome of low to high form of serous carcinoma. In this work, we have performed exhaustive imaging of ovarian cancer versus normal tissue and introduced a novel Deep Hybrid Learning approach on the basis of the distribution of lamin proteins. This would be of immense importance in accurate and fast diagnosis and prognosis of ovarian cancer and would perform across small/medium to large datasets with equal efficiency.

Keywords: lamins, ovarian cancer, tissue microarray, confocal imaging, nuclear morphometry, deep learning, deep hybrid learning

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