AI Article Synopsis
- Papillary thyroid carcinoma (PTC) is mainly treated through surgery, but identifying the aggressiveness of the tumor can be challenging during the procedure.
- Researchers used hyperspectral imaging and radiomic features to assess the aggressiveness of PTC in fresh surgical specimens from 44 patients.
- Their machine learning approach yielded an impressive AUC of 0.85, indicating that these advanced imaging techniques can enhance the real-time detection of cancer aggressiveness during surgery.
Article Abstract
Papillary thyroid carcinoma (PTC) is primarily treated by surgical resection. During surgery, surgeons often need intraoperative frozen analysis and pathologic consultation in order to detect PTC. In some cases pathologists cannot determine if the tumor is aggressive until the operation has been completed. In this work, we have taken tumor classification a step further by determining the tumor aggressiveness of fresh surgical specimens. We employed hyperspectral imaging (HSI) in combination with multiparametric radiomic features to complete this task. The study cohort includes 72 tissue specimens from 44 patients with pathology-confirmed PTC. A total of 67 features were extracted from this data. Using machine learning classification methods, we were able to achieve an AUC of 0.85. Our study shows that hyperspectral imaging and multiparametric radiomic features could aid in the pathological detection of tumor aggressiveness using fresh surgical spemens obtained during surgery.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9232190 | PMC |
| http://dx.doi.org/10.1117/12.2582147 | DOI Listing |
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