14-May-2021 | Market Research Store

In some of the cancers, the tumor origin is unrecognizable as the newly developed cancer therapeutics targets the primary tumor sites, thereby making the diagnosis of cancer of unknown primary (CUP) difficult and with a average overall survival of 2.7‑16 Months. For specific diagnosis, the patients are advised to perform detailed diagnostic study including biopsies, laboratory tests, and endoscopy procedures. Patients with metastatic cancer in the low-resource areas could now use an artificial intelligence (AI) system that uses histological data to find the origin of metastatic tumors, thereby developing differential diagnosis. The team from the Brigham and Women's Hospital has developed this diagnostic tool for using universally acquired data coupled with artificial intelligence for complicated case diagnosis.

Researchers developed Tumor Origin Assessment via Deep Learning (TOAD), a deep-learning-based algorithm, which can continuously identify the tumor whether primary or metastatic for helping detect its site of origin. Using the gigapixel pathology whole-slide images of cancers obtained from many cancer cases, the researchers trained the model and also used TOAD to analyze complex metastatic cancer to develop utility of the AI model on CUPs. TOAD was found to function with 96% accuracy. The dependency of the latest model on genomic data helped accurately predict origin of the tumors. This alternative option is the best option for low-resource settings.

The latest diagnostic tool helps speed up diagnosis and treatment by lowering the need for other tests and tissue sampling. The long and stressful tests and procedures required for cancer diagnosis makes origin of site difficult. The limited resource settings with no pathology expertise can use the latest model to predict a differential diagnosis. The AI-assisted cancer origin analysis is the new exciting research that can help improve and standardize the diagnosis process.