New field‑in‑field with two reference points method for whole breast radiotherapy: Dosimetric analysis and radiation‑induced skin toxicities assessment

Tekiki, Nouha; Kuroda, Masahiro; Ishizaka, Hinata; Khasawneh, Abdullah; Barham, Majd; Hamada, Kentaro; Konishi, Katsuaki; Sugimoto, Kohei; Katsui, Kuniaki; Sugiyama, Soichi; Watanabe, Kenta; Yoshio, Kotaro; Katayama, Norihisa; Ogata, Toru; Ihara, Hidetoshi; Kanazawa, Susumu; Asaumi, Junichi

doi:10.3892/mco.2021.2355

Cited by 3 publications

(9 citation statements)

References 19 publications

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“…FIF-2RP is a recently developed FIF radiation technique, particularly suitable for patients with relatively small breast volumes. In such patients, high-dose areas remain in the irradiation field around the reference point in conventional FIF-1RP, whereas the development of the FIF-2RP technique in our institution has improved dosimetric parameters, which led to a reduction in high-dose ranges and subsequent skin toxicities [ 18 ]. Tekiki et al reported that the FIF-2RP method decreased the high-dose range V105% of the target to 0% while maintaining a homogeneous dose distribution across the breast tissue.…”

Section: Discussionmentioning

confidence: 99%

“…This decrease in the high-dose range was in conjunction with a decrease in the occurrence and grade of skin adverse events. Therefore, the FIF-2RP could be advised as an optimal method in clinical practice for patients with early-stage breast cancer [ 18 ]. In this study, the predictive power of the DNN using FIF-2RP patients alone was analyzed on an external test dataset, indicating that this DNN model might be suitable for predicting MHD with the FIF-2RP technique.…”

Section: Discussionmentioning

confidence: 99%

“…All patients were irradiated for whole breast with 200 cGy per fraction, 25 fractions, for a total of 5000 cGy, after partial breast resection. Patients were treated in our hospital using either the conventional field-in-field (FIF) with one reference point (FIF-1RP) method or a new FIF with two reference points (FIF-2RP) method [ 18 ]. Eighty-eight patients were irradiated with an additional 1000–1600 cGy boost on the tumor bed.…”

Section: Methodsmentioning

confidence: 99%

“…In this study, Anaconda Python version 3.9 and Python libraries (Python Software Foundation, Wilmington, Delaware, USA) were utilized for building, developing, and experimenting with our ML models. We used an imbalanced dataset that consisted of 207 breast cancer patients, with 165 patients of low MHD (<300 cGy) and 42 patients of high MHD (≥300 cGy) [ 6 , 13 , 18 ]. The average MHD was 251 cGy.…”

Section: Methodsmentioning

confidence: 99%

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Mean Heart Dose Prediction Using Parameters of Single-Slice Computed Tomography and Body Mass Index: Machine Learning Approach for Radiotherapy of Left-Sided Breast Cancer of Asian Patients

et al. 2023

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Deep inspiration breath-hold (DIBH) is an excellent technique to reduce the incidental radiation received by the heart during radiotherapy in patients with breast cancer. However, DIBH is costly and time-consuming for patients and radiotherapy staff. In Asian countries, the use of DIBH is restricted due to the limited number of patients with a high mean heart dose (MHD) and the shortage of radiotherapy personnel and equipment compared to that in the USA. This study aimed to develop, evaluate, and compare the performance of ten machine learning algorithms for predicting MHD using a patient’s body mass index and single-slice CT parameters to identify patients who may not require DIBH. Machine learning models were built and tested using a dataset containing 207 patients with left-sided breast cancer who were treated with field-in-field radiotherapy with free breathing. The average MHD was 251 cGy. Stratified repeated four-fold cross-validation was used to build models using 165 training data. The models were compared internally using their average performance metrics: F2 score, AUC, recall, accuracy, Cohen’s kappa, and Matthews correlation coefficient. The final performance evaluation for each model was further externally analyzed using 42 unseen test data. The performance of each model was evaluated as a binary classifier by setting the cut-off value of MHD ≥ 300 cGy. The deep neural network (DNN) achieved the highest F2 score (78.9%). Most models successfully classified all patients with high MHD as true positive. This study indicates that the ten models, especially the DNN, might have the potential to identify patients who may not require DIBH.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Mean Heart Dose Prediction Using Parameters of Single-Slice Computed Tomography and Body Mass Index: Machine Learning Approach for Radiotherapy of Left-Sided Breast Cancer of Asian Patients

et al. 2023

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“…The small number of samples in our study noted did not influence the fact that the severity of radiodermatitis is limited to grade 1 or 2. Several institutions have reported that the severity of acute radiation dermatitis could be reduced when IMRT or FIF techniques were used due to minimized unwanted radiation dose inhomogeneity in the breasts [44][45][46]. In addition, Cordoba et al showed that the smaller body mass index (BMI) and breast size, the lower the radiation-induced skin damage was evaluated as grade 1 or 2 [47].…”

Section: Plos Onementioning

confidence: 99%

Quantitative radiomics approach to assess acute radiation dermatitis in breast cancer patients

Park,

Kim

et al. 2023

PLoS ONE

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Purpose We applied a radiomics approach to skin surface images to objectively assess acute radiation dermatitis in patients undergoing radiotherapy for breast cancer. Methods A prospective cohort study of 20 patients was conducted. Skin surface images in normal, polarized, and ultraviolet (UV) modes were acquired using a skin analysis device before starting radiotherapy (‘Before RT’), approximately 7 days after the first treatment (’RT D7’), on ‘RT D14’, and approximately 10 days after the radiotherapy ended (‘After RT D10’). Eighteen types of radiomic feature ratios were calculated based on the values acquired ‘Before RT’. We measured skin doses in ipsilateral breasts using optically stimulated luminescent dosimeters on the first day of radiotherapy. Clinical evaluation of acute radiation dermatitis was performed using the Radiation Therapy Oncology Group scoring criteria on ‘RT D14’ and ‘After RT D10’. Several statistical analysis methods were used in this study to test the performance of radiomic features as indicators of radiodermatitis evaluation. Results As the skin was damaged by radiation, the energy for normal mode and sum variance for polarized and UV modes decreased significantly for ipsilateral breasts, whereas contralateral breasts exhibited a smaller decrease with statistical significance. The radiomic feature ratios at ‘RT D7’ had strong correlations to skin doses and those at ‘RT D14’ and ‘after RT D10’ with statistical significance. Conclusions The energy for normal mode and sum variance for polarized and UV modes demonstrated the potential to evaluate and predict acute radiation, which assists in its appropriate management.

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Evaluation of the accuracy of heart dose prediction by machine learning for selecting patients not requiring deep inspiration breath‑hold radiotherapy after breast cancer surgery

Kamizaki,

Kuroda,

Al‑Hammad

et al. 2023

Exp Ther Med

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Increased heart dose during postoperative radiotherapy (RT) for left-sided breast cancer (BC) can cause cardiac injury, which can decrease patient survival. The deep inspiration breath-hold technique (DIBH) is becoming increasingly common for reducing the mean heart dose (MHD) in patients with left-sided BC. However, treatment planning and DIBH for RT are laborious, time-consuming and costly for patients and RT staff. In addition, the proportion of patients with left BC with low MHD is considerably higher among Asian women, mainly due to their smaller breast volume compared with that in Western countries. The present study aimed to determine the optimal machine learning (ML) model for predicting the MHD after RT to pre-select patients with low MHD who will not require DIBH prior to RT planning. In total, 562 patients with BC who received postoperative RT were randomly divided into the trainval (n=449) and external (n=113) test datasets for ML using Python (version 3.8). Imbalanced data were corrected using synthetic minority oversampling with Gaussian noise. Specifically, right-left, tumor site, chest wall thickness, irradiation method, body mass index and separation were the six explanatory variables used for ML, with four supervised ML algorithms used. Using the optimal value of hyperparameter tuning with root mean squared error (RMSE) as an indicator for the internal test data, the model yielding the best F2 score evaluation was selected for final validation using the external test data. The predictive ability of MHD for true MHD after RT was the highest among all algorithms for the deep neural network, with a RMSE of 77.4, F2 score of 0.80 and area under the curve-receiver operating characteristic of 0.88, for a cut-off value of 300 cGy.

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New field‑in‑field with two reference points method for whole breast radiotherapy: Dosimetric analysis and radiation‑induced skin toxicities assessment

Cited by 3 publications

References 19 publications

Mean Heart Dose Prediction Using Parameters of Single-Slice Computed Tomography and Body Mass Index: Machine Learning Approach for Radiotherapy of Left-Sided Breast Cancer of Asian Patients

Mean Heart Dose Prediction Using Parameters of Single-Slice Computed Tomography and Body Mass Index: Machine Learning Approach for Radiotherapy of Left-Sided Breast Cancer of Asian Patients

Quantitative radiomics approach to assess acute radiation dermatitis in breast cancer patients

Evaluation of the accuracy of heart dose prediction by machine learning for selecting patients not requiring deep inspiration breath‑hold radiotherapy after breast cancer surgery

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