PMID- 37384290
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230703
IS  - 2234-943X (Print)
IS  - 2234-943X (Electronic)
IS  - 2234-943X (Linking)
VI  - 13
DP  - 2023
TI  - A Bayesian optimization tunning integrated multi-stacking classifier framework 
      for the prediction of radiodermatitis from 4D-CT of patients underwent breast 
      cancer radiotherapy.
PG  - 1152020
LID - 10.3389/fonc.2023.1152020 [doi]
LID - 1152020
AB  - PURPOSE: In this study, we aimed to develop a novel Bayesian optimization based 
      multi-stacking deep learning platform for the prediction of radiation-induced 
      dermatitis (grade >/= two) (RD 2+) before radiotherapy, by using multi-region 
      dose-gradient-related radiomics features extracted from pre-treatment planning 
      four-dimensional computed tomography (4D-CT) images, as well as clinical and 
      dosimetric characteristics of breast cancer patients who underwent radiotherapy. 
      MATERIALS AND METHODS: The study retrospectively included 214 patients with 
      breast cancer who received radiotherapy after breast surgeries. Six regions of 
      interest (ROIs) were delineated based on three PTV dose -gradient-related and 
      three skin dose-gradient-related parameters (i.e., isodose). A total of 4309 
      radiomics features extracted from these six ROIs, as well as clinical and 
      dosimetric characteristics, were used to train and validate the prediction model 
      using nine mainstream deep machine learning algorithms and three stacking 
      classifiers (i.e., meta-learners). To achieve the best prediction performance, a 
      Bayesian optimization based multi-parameter tuning technology was adopted for the 
      AdaBoost, random forest (RF), decision tree (DT), gradient boosting (GB) and 
      extra tree (XTree) five machine learning models. The five parameter -tuned 
      learners and the other four learners (i.e., logistic regression (LR), K-nearest 
      neighbors (KNN), linear discriminant analysis (LDA), Bagging) whose parameters 
      cannot be tuned, all as the primary week learners, were fed into the subsequent 
      meta-learners for training and learning the final prediction model. RESULTS: The 
      final prediction model included 20 radiomics features and eight clinical and 
      dosimetric characteristics. At the primary learner level, on base of Bayesian 
      parameter tuning optimization, the RF, XGBoost, AdaBoost, GBDT, and LGBM models 
      with the best parameter combinations achieved AUC of 0.82, 0.82, 0.77, 0.80, and 
      0.80 prediction performance in the verification data set, respectively. In the 
      secondary meta-learner lever, compared with LR and MLP meta-learner, the best 
      predictor of symptomatic RD 2+ for stacked classifiers was the GB meta-learner 
      with an area under the curve (AUC) of 0.97 [95% CI: 0.91-1.0] and an AUC of 0.93 
      [95% CI: 0.87-0.97] in the training and validation datasets, respectively and the 
      10 top predictive characteristics were identified. CONCLUSION: A novel 
      multi-region dose-gradient-based Bayesian optimization tunning integrated 
      multi-stacking classifier framework can achieve a high-accuracy prediction of 
      symptomatic RD 2+ in breast cancer patients than any other single deep machine 
      learning algorithm.
CI  - Copyright (c) 2023 Wu, Miu, Wang and Li.
FAU - Wu, Kuan
AU  - Wu K
AD  - Department of Tumor Radiotherapy, The First People's Hospital of Fuyang Hangzhou, 
      Hangzhou, China.
FAU - Miu, Xiaoyan
AU  - Miu X
AD  - Department of Tumor Radiotherapy, The First People's Hospital of Fuyang Hangzhou, 
      Hangzhou, China.
FAU - Wang, Hui
AU  - Wang H
AD  - Department of Tumor Radiotherapy, Hangzhou Cancer Hospital, Hangzhou, China.
FAU - Li, Xiadong
AU  - Li X
AD  - Department of Tumor Radiotherapy, Hangzhou Cancer Hospital, Hangzhou, China.
AD  - Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of 
      Zhejiang Province, Affiliated Hangzhou First People's Hospital, Zhejiang 
      University School of Medicine, Hangzhou, China.
AD  - Department of Radiotherapy, Affiliated Hangzhou Cancer Hospital, Zhejiang 
      University School of Medicine, Hangzhou, China.
AD  - Laboratory of Medical Imaging and Translational Medicine, Hangzhou Cancer 
      Hospital Affiliated to Zhejiang University School of Medicine, Hangzhou, China.
LA  - eng
PT  - Journal Article
DEP - 20230613
PL  - Switzerland
TA  - Front Oncol
JT  - Frontiers in oncology
JID - 101568867
PMC - PMC10294237
OTO - NOTNLM
OT  - breast cancer
OT  - radiation therapy
OT  - radiation-induced dermatitis
OT  - radiomics
OT  - stackingclassifier
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest
EDAT- 2023/06/29 13:42
MHDA- 2023/06/29 13:43
PMCR- 2023/01/01
CRDT- 2023/06/29 12:04
PHST- 2023/01/27 00:00 [received]
PHST- 2023/05/22 00:00 [accepted]
PHST- 2023/06/29 13:43 [medline]
PHST- 2023/06/29 13:42 [pubmed]
PHST- 2023/06/29 12:04 [entrez]
PHST- 2023/01/01 00:00 [pmc-release]
AID - 10.3389/fonc.2023.1152020 [doi]
PST - epublish
SO  - Front Oncol. 2023 Jun 13;13:1152020. doi: 10.3389/fonc.2023.1152020. eCollection 
      2023.