Helical tomotherapy experience in breast cancer adjuvant radiotherapy and acute toxicity results

İnan, Gonca Altınışık; Aral, İpek Pınar; Arslan, Aliye; Celik, Tuba Ozkader; Ozturk, Huseyin Furkan; Arslan, Süheyla Aytaç; Tezcan, Yılmaz

doi:10.5603/rpor.a2022.0121

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…Among all the vital constituents of breast cancer treatment, adjuvant radiotherapy (RT) plays a paramount role in substantially enhancing overall survival rate and effectively reducing risk of localized cancer recurrence [ 1 , 2 ]. Recent developments in RT techniques have shown that Tomotherapy can be employed to achieve optimal target coverage through meticulous dose painting, which results in improving tumor control probability and reducing normal tissue complications in comparison with traditional techniques [ 3 – 5 ]. Nonetheless, considering patient’s quality of life and undesirable treatment interruptions, acute skin toxicity (AST) remains a notable concern in breast cancer RT [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

A dosiomics model for prediction of radiation-induced acute skin toxicity in breast cancer patients: machine learning-based study for a closed bore linac

Saadatmand,

Mahdavi,

Nikoofar

et al. 2024

Eur J Med Res

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Background Radiation induced acute skin toxicity (AST) is considered as a common side effect of breast radiation therapy. The goal of this study was to design dosiomics-based machine learning (ML) models for prediction of AST, to enable creating optimized treatment plans for high-risk individuals. Methods Dosiomics features extracted using Pyradiomics tool (v3.0.1), along with treatment plan-derived dose volume histograms (DVHs), and patient-specific treatment-related (PTR) data of breast cancer patients were used for modeling. Clinical scoring was done using the Common Terminology Criteria for Adverse Events (CTCAE) V4.0 criteria for skin-specific symptoms. The 52 breast cancer patients were grouped into AST 2 + (CTCAE ≥ 2) and AST 2 − (CTCAE < 2) toxicity grades to facilitate AST modeling. They were randomly divided into training (70%) and testing (30%) cohorts. Multiple prediction models were assessed through multivariate analysis, incorporating different combinations of feature groups (dosiomics, DVH, and PTR) individually and collectively. In total, seven unique combinations, along with seven classification algorithms, were considered after feature selection. The performance of each model was evaluated on the test group using the area under the receiver operating characteristic curve (AUC) and f1-score. Accuracy, precision, and recall of each model were also studied. Statistical analysis involved features differences between AST 2 − and AST 2 + groups and cutoff value calculations. Results Results showed that 44% of the patients developed AST 2 + after Tomotherapy. The dosiomics (DOS) model, developed using dosiomics features, exhibited a noteworthy improvement in AUC (up to 0.78), when spatial information is preserved in the dose distribution, compared to DVH features (up to 0.71). Furthermore, a baseline ML model created using only PTR features for comparison with DOS models showed the significance of dosiomics in early AST prediction. By employing the Extra Tree (ET) classifiers, the DOS + DVH + PTR model achieved a statistically significant improved performance in terms of AUC (0.83; 95% CI 0.71–0.90), accuracy (0.70), precision (0.74) and sensitivity (0.72) compared to other models. Conclusions This study confirmed the benefit of dosiomics-based ML in the prediction of AST. However, the combination of dosiomics, DVH, and PTR yields significant improvement in AST prediction. The results of this study provide the opportunity for timely interventions to prevent the occurrence of radiation induced AST.

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Section: Introductionmentioning

confidence: 99%

A dosiomics model for prediction of radiation-induced acute skin toxicity in breast cancer patients: machine learning-based study for a closed bore linac

Saadatmand,

Mahdavi,

Nikoofar

et al. 2024

Eur J Med Res

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Dosimetric evaluation of different planning strategies for hypofractionated whole-breast irradiation technique

Tang,

Liu,

Yuan

et al. 2024

Phys. Med. Biol.

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Purpose: The dose hotspot areas in hypofractionated whole-breast irradiation greatly increase the risk of acute skin toxicity because of the anatomical peculiarities of the breast. In this study, we presented several novel planning strategies that integrate multiple sub-planning target volumes (sub-PTVs), field secondary placement, and RapidPlan models for right-sided hypofractionated whole-breast irradiation. Methods: A total of 35 cases of whole-breast irradiation with a dose of 42.5Gy for PTVs using tangential intensity-modulated radiotherapy(IMRT) were selected. Both PTVs were planned for simultaneous treatment using the original manual multiple sub-PTV plan (OMMP) and the original manual single-PTV plan (OMSP). The manual field secondary placement multiple sub-PTV plan (m-FSMP) with multiple objects on the original PTV and the manual field secondary placement single-objective plan (m-FSSP) were initially planned, which were distribution-based of V105(volume receiving 105% of the prescription dose). In addition, two RapidPlan-based plans were developed, including the RapidPlan-based multiple sub-PTVs plan (r-FSMP) and the RapidPlan-based single-PTV plan (r-FSSP). Dosimetric parameters of the plans were compared, and V105 was evaluated using multivariate analysis to determine how it was related to the volume of PTV and the interval of lateral beam angles (ILBA).Results: The lowest mean V105 (5.64±6.5%) of PTV was observed in m-FSMP compared to other manual plans. Upon validation, r-FSSP demonstrated superior dosimetric quality for OAR compared to the two other manual planning methods, except for V5(the volume of ipsilateral lung receiving 5 Gy) of the ipsilateral lung. While r-FSMP showed no significant difference (p=0.06) compared to r-FSSP, it achieved the lowest V105 value (4.3±4.5%), albeit with a slight increase in the dose to some OARs.Conclusions: m-FSMP and r-FSMP can substantially enhance the homogeneity index (HI) and reduce V105, thereby minimizing the risk of acute skin toxicities, even though there may be a slight dose compromise for certain OARs.

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Helical tomotherapy experience in breast cancer adjuvant radiotherapy and acute toxicity results

Cited by 2 publications

References 21 publications

A dosiomics model for prediction of radiation-induced acute skin toxicity in breast cancer patients: machine learning-based study for a closed bore linac

A dosiomics model for prediction of radiation-induced acute skin toxicity in breast cancer patients: machine learning-based study for a closed bore linac

Dosimetric evaluation of different planning strategies for hypofractionated whole-breast irradiation technique

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