글로벌 연구동향
방사선종양학
- 2024년 06월호
[Cancers (Basel) .] Prediction of Overall Disease Burden in (y)pN1 Breast Cancer Using Knowledge-Based Machine Learning Model서울의대 / 전석주, 장범섭, 신경환*
- 출처
- Cancers (Basel) .
- 등재일
- 2024 Apr 13
- 저널이슈번호
- 16(8):1494. doi: 10.3390/cancers16081494.
- 내용
Abstract
Background: We aimed to construct an expert knowledge-based Bayesian network (BN) model for assessing the overall disease burden (ODB) in (y)pN1 breast cancer patients and compare ODB across arms of ongoing trials.Methods: Utilizing institutional data and expert surveys, we developed a BN model for (y)pN1 breast cancer. Expert-derived probabilities and disability weights for radiotherapy-related benefit (e.g., 7-year disease-free survival [DFS]) and toxicities were integrated into the model. ODB was defined as the sum of disability weights multiplied by probabilities. In silico predictions were conducted for Alliance A011202, PORT-N1, RAPCHEM, and RT-CHARM trials, comparing ODB, 7-year DFS, and side effects.
Results: In the Alliance A011202 trial, 7-year DFS was 80.1% in both arms. Axillary lymph node dissection led to higher clinical lymphedema and ODB compared to sentinel lymph node biopsy with full regional nodal irradiation (RNI). In the PORT-N1 trial, the control arm (whole-breast irradiation [WBI] with RNI or post-mastectomy radiotherapy [PMRT]) had an ODB of 0.254, while the experimental arm (WBI alone or no PMRT) had an ODB of 0.255. In the RAPCHEM trial, the radiotherapy field did not impact the 7-year DFS in ypN1 patients. However, there was a mild ODB increase with a larger irradiation field. In the RT-CHARM trial, we identified factors associated with the major complication rate, which ranged from 18.3% to 22.1%.
Conclusions: The expert knowledge-based BN model predicted ongoing trial outcomes, validating reported results and assumptions. In addition, the model demonstrated the ODB in different arms, with an emphasis on quality of life.
Affiliations
Seok-Joo Chun 1 2, Bum-Sup Jang 1 3, Hyeon Seok Choi 1, Ji Hyun Chang 1 3, Kyung Hwan Shin 1 3 4, Division For Breast Cancer Korean Radiation Oncology Group
1Department of Radiation Oncology, Seoul National University Hospital, Seoul 03080, Republic of Korea.
2Department of Radiation Oncology, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Republic of Korea.
3Department of Radiation Oncology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.
4Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul 03080, Republic of Korea.
- 키워드
- Bayesian network; breast cancer; de-escalation; disability weights; disease burden; in silico; radiotherapy.
- 연구소개
- 선행 연구에서 개발된 베이지얀 모델을 이용하여 현재 진행 중인 유방암 관련 임상시험의 결과를 예측하려고 하였습니다. 치료의 이득과 위험성을 고려한 ‘Overall Disease Burden (ODB)’을 각 임상시험의 군끼리 비교를 하였습니다. ‘Alliance A011202’, ‘PORT-N1’, ‘RAPCHEM’, 그리고 ‘RT-CHARM’ 연구에서의 ODB를 비교하였고 전반적으로 treatment de-escalation 군의 비열등성이 보였습니다.
- 덧글달기
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- 다음글 [Cancers (Basel) .] Treatment of Pelvic and Spinal Bone Metastases: Radiotherapy and Hyperthermia Alone vs. in Combination