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Year : 2022  |  Volume : 2  |  Issue : 2  |  Page : 113-119

Prostate-specific Membrane Antigen Positron Emission Tomography (PSMA-PET) and Gleason grading system based Artificial Intelligence (AI) model in diagnosis and staging of prostate cancer

1 Department of Healthcare Solutions, Philips India Research, Manyata, Tech Park, Bengaluru, Karnataka, India
2 Department of Radiation Oncology, Healthcare Global Enterprises Ltd, Bengaluru, Karnataka, India

Correspondence Address:
Dr. Dinesh M Siddu
Healthcare Solutions-Research, Philips India Pvt. Ltd, Manyata Tech Park, Nagavara, Bengaluru - 560 045, Karnataka
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jpo.jpo_15_22

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Introduction: Prostate cancer is the second-most commonly occurring cancer and the fifth major cause of death among men worldwide. Early diagnosis and treatment planning are very crucial in reducing the mortality rate due to prostate cancer. Gleason grading is the most used prostate cancer prognosis tool by doctors for a long time. It is used to determine the aggressiveness of the cancer for planning treatment options. The process requires very trained pathologists to look at multiple biopsy samples under the microscope and assign a grade to the cancer based on its severity. Methods: In this work, we are adding Gleason grading capabilities to prostate-specific membrane antigen positron emission tomography/ computed tomography (PSMA PET-CT) scans for tumor habitats and classify them as aggressive or indolent type. Tagging habitats with Gleason grade to categorize them as aggressive or indolent type helps in biopsy planning to extract the right tissue samples and tagging helps to target aggressive tumors during radiation therapy. We have developed a machine learning-based model to automatically classify tumor habitat regions of interest from PSMA PET and CT imaging data into Gleason grade groups of indolent and aggressive. Results: The 68Ga-PSMA PET-CT scans are very effective in detecting the presence of different habitats within the tumor with distinct volumes, each with a specific combination of flow, cell density, necrosis, and edema. Habitat distribution through tumor heterogeneity analysis in patients with prostate cancers can be enabled to discriminate between cancers that progress quickly and those that are more indolent. Conclusion: We have developed an AI model to classify habitat tumors present in the gross tumor volume into indolent and aggressive types based on the ground truth generated using Gleason grade groups on pathology samples by Healthcare Global Cancer Hospital, Bangalore, India. Habitat analysis helps radiotherapists to target active tumor cells within gross tumor volume and helps in selecting the right tissue for performing biopsy. The currently developed model is performing with an overall accuracy of 90% on test data.

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