Head and neck cancer researchers demonstrate the power of a deep learning algorithm in a postoperative environment

Head and neck cancer researchers demonstrate the ability of a deep learning algorithm in a postoperative setting for assessing t

“This type of research is essential because it can help identify patients with high-risk head and neck cancer, and also help select suitable patients for treatment relief,” says Dr. Benjamin Kahn, who led the study. Credit: Artificial Intelligence in Medicine Program, Brigham and Women’s Hospital

Artificial intelligence can augment existing methods for predicting the risk of head and neck cancer spreading beyond the boundaries of neck lymph nodes, according to researchers at the ECOG-ACRIN Cancer Research Group (ECOG-ACRIN). A custom deep learning algorithm using standardized CT scan images and associated data contributed by patients who participated in the E3311 phase 2 trial shows promise, especially for patients with a new diagnosis of HPV associated with head and neck cancer. The E3311 validated dataset holds the potential to contribute to more accurate disease staging and risk prediction.

Benjamin Kahn, MD (Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School) led the ECOG-ACRIN study. He will present the findings during the annual meeting of the American Society of Radiation Oncology (ASTRO) in San Antonio, Texas.

“This type of research is essential because it can help identify patients with high-risk and aggressive diseases and also help select suitable patients to de-escalate treatment,” Dr. Kahn said.

Head and neck cancers and their standard treatments – surgery, radiotherapy or chemotherapy – carry significant morbidity. They affect a person’s appearance, speech, eating or breathing. Therefore, there is great interest in developing less intensive treatment strategies for patients. For example, the completed Phase III E3311 trial showed that low-dose radiation at 50 Gray (Gy) without chemotherapy after oral surgery resulted in very high survival and outstanding quality of life in patients at average risk of recurrence (Ferris R.L. J Clin Unk. December 2021).

Dr. Kan and colleagues developed and validated a network based on neural networks deep learning algorithm Based on diagnostic CT scans, pathology, and Clinical data. The source was the group of participants in the E3311 trial who were assessed as being at risk of recurrence by standard pathological and clinical measures.

Head and neck launch cancer “It presents a challenging clinical problem,” said Dr. Kahn. “In particular, our current efforts to quantify external extension by human interpretation of pre-treatment imaging have generally shown poor results.”

Among the factors that determine the stage of cancer are the size of the original tumor, the number of lymph nodes involved, and external expansion – when malignant cells spread beyond the boundaries of the lymph nodes in the neck to surrounding tissues. At E3311, patients were assessed as high risk if there was a 1 mm external extension (ENE). These patients were assigned to chemotherapy and high-dose radiation (66 Gy) after transoral surgery.

Dr. Kahn and colleagues obtained pre-treatment CT scans and corresponding surgical pathology reports from the E3311 high-risk group, as available. Of the 177 scans collected, 311 nodes were annotated: 71 (23%) with ENE and 39 (13%) with ≥1 mm ENE.

The tool showed a high performance in the ENE prediction, significantly outperforming reviews by head and neck radiology experts.

“The deep learning algorithm accurately classified 85% of nodes as having ENE compared to 70% by radiologists,” said Dr. Kahn. “In terms of specificity and sensitivity, the deep learning algorithm was 78% accurate versus 62% by radiologists.”

The team plans to evaluate the data set as part of future treatment trials for the head and neck cancer. The algorithm will be evaluated for its ability to improve current disease risk assessment and staging methods.

“Our ability to develop biomarkers from standard CT scans is an exciting new area of ​​clinical research and offers hope that we will be able to tailor better treatment to individual patients, including determining when surgery is best used and who should reduce the extent of treatment,” Senior author Barbara A. Bertens, MD.


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more information:
141 External Extension Screening with Deep Learning: Evaluation in the ECOG-ACRIN E3311, a randomized de-escalation trial of HPV-associated oropharyngeal carcinoma, plan.core-apps.com/myastroapp2 … c7071c5947c71a441519

Submitted by ECOG-ACRIN . Cancer Research Group

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