Predictive Factors Increasing the Risk of Radiation Toxicity in Patients with Early Breast Cancer

Abdeltawab, Asmaa A.; Ali, Shahzad; Mostafa, Hanan; Hassan, M. A.

doi:10.31557/apjcp.2021.22.1.145

Cited by 5 publications

(8 citation statements)

References 30 publications

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“…The current study aimed to help define the prevalence of grade ≥2 ARD and RP in breast cancer patients treated with adjuvant RT. In our study, the prevalence of grade ≥2 ARD was 25.3%, which was well in the range of 3-76% reported in the literature (9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24). In those studies that state the prevalence of grade ≥2 ARD irrespective of the type of fractionation, ARD rates ranged between 17% and 63% (11,(20)(21)(22)(23)26).…”

Section: Discussionsupporting

confidence: 85%

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Radiation Dermatitis and Pneumonitis in Patients Irradiated for Breast Cancer

EGGERT,

YU,

RADES

2023

In Vivo

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Background/Aim: Adjuvant radiotherapy (RT) for breast cancer can be associated with acute dermatitis (ARD) and pneumonitis (RP). Prevalence and risk factors were characterized. Patients and Methods: This study included 489 breast cancer patients receiving adjuvant RT with conventional fractionation (CF) ± sequential or simultaneous integrated boost, or hypo-fractionation ± sequential boost. RTregimen and 15 characteristics were investigated for grade ≥2 ARD and RP. Results: Prevalence of grade ≥2 ARD and RP was 25.3% and 2.5%, respectively. On univariate analyses, ARD was significantly associated with CF and radiation boost (p<0.0001), age ≤60 years (p=0.008), and systemic treatment (p=0.002). On multivariate analysis, and age (p=0.009) were associated with ARD. Chronic inflammatory disease was significantly associated with RP on univariate (p=0.007) and multivariate (p=0.016) analyses. Conclusion: Risk factors for grade ≥2 ARD and RP were determined that may help identify patients who require closer monitoring during and after RT.Breast cancer is the most common cancer type in females in Western countries (1, 2). Many of these women with nonmetastatic disease receive breast conserving surgery or mastectomy followed by local or loco-regional irradiation (3,4). Adjuvant radiotherapy (RT) may be associated with adverse events including acute radiation dermatitis (ARD) and radiation pneumonitis (RP) (5,6). ARD was reported to have a negative impact on the patients' quality of life, and RP can even be fatal (7, 8). In the literature, the prevalence of clinically relevant grade ≥2 ARD ranges between 3% and 76%, with higher rates in patients treated with conventional fractionation (CF) than in patients receiving hypofractionation (HF) (9-26). ARD usually develops during the second to third week of radiotherapy, becomes more severe during the further RT-course and possibly the first post-RT week, and disappears a few weeks after completion of RT (5). In contrast, RP is considered a subacute effect of RT, which often develops only several weeks or months following RT (6). The prevalence of RP after RT of breast cancer ranges between 0.8% and 19. 6% (6, 10, 27-33). Considering the comparably wide ranges regarding the prevalence of ARD and RP, it becomes obvious that additional studies are required to more precisely define the prevalence of these two RT-related side effects in breast cancer patients. Our present study evaluated the prevalence in a cohort of breast cancer patients irradiated between 2016 and 2019. Moreover, it aimed to determine risk factors for ARD and RP, in order to contribute to early identification of patients at risk who would benefit from closer monitoring of adverse events during and after the RT-course. Patients and MethodsThe present retrospective study investigated grade ≥2 ARD and grade ≥2 RP in 489 female breast cancer patients treated with adjuvant radiotherapy between 2016 and 2019. The original study protocol and an amendment were approved by the ethics committee of the University of Lü...

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Section: Discussionsupporting

confidence: 85%

“…ARD is the most common toxicity occurring during the course of RT or shortly afterwards (5). In previous studies, the prevalence of grade ≥2 ARD ranged between 3% and 76% (9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26). Considering this wide range, additional studies are needed.…”

Section: Discussionmentioning

confidence: 99%

Radiation Dermatitis and Pneumonitis in Patients Irradiated for Breast Cancer

EGGERT,

YU,

RADES

2023

In Vivo

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“…Second, PTR features show heterogeneity in the sample. However, Regarding the heterogeneity as reported in the fraction schedule [ 57 ], chemotherapy [ 65 ], surgery type [ 18 ], plan boost [ 46 ], treatment area [ 66 ], and nodal irradiation [ 67 ] in similar studies, it appears that these features have a heterogeneous nature. Therefore, considering potential variations in a homogeneous sample size, we recommend a cautious interpretation of the results.…”

Section: Discussionmentioning

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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“…In addition, previous studies reported that elevated concentrations of serum biomarkers, including cancer antigen 15-3, carcinoembryonic antigen and cancer antigen 125, are associated with DM [ 34 , 59 ]. Future statistical and ML models could potentially guide routine CTC and serum biomarkers for specified high-risk groups and enable earlier detection of DM [ 60 , 61 ].…”

Section: Discussionmentioning

confidence: 99%

Predicting Patterns of Distant Metastasis in Breast Cancer Patients following Local Regional Therapy Using Machine Learning

Shiner,

Kiss,

Saednia

et al. 2023

Genes

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Up to 30% of breast cancer (BC) patients will develop distant metastases (DM), for which there is no cure. Here, statistical and machine learning (ML) models were developed to estimate the risk of site-specific DM following local-regional therapy. This retrospective study cohort included 175 patients diagnosed with invasive BC who later developed DM. Clinicopathological information was collected for analysis. Outcome variables were the first site of metastasis (brain, bone or visceral) and the time interval (months) to developing DM. Multivariate statistical analysis and ML-based multivariable gradient boosting machines identified factors associated with these outcomes. Machine learning models predicted the site of DM, demonstrating an area under the curve of 0.74, 0.75, and 0.73 for brain, bone and visceral sites, respectively. Overall, most patients (57%) developed bone metastases, with increased odds associated with estrogen receptor (ER) positivity. Human epidermal growth factor receptor-2 (HER2) positivity and non-anthracycline chemotherapy regimens were associated with a decreased risk of bone DM, while brain metastasis was associated with ER-negativity. Furthermore, non-anthracycline chemotherapy alone was a significant predictor of visceral metastasis. Here, clinicopathologic and treatment variables used in ML prediction models predict the first site of metastasis in BC. Further validation may guide focused patient-specific surveillance practices.

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Predictive Factors Increasing the Risk of Radiation Toxicity in Patients with Early Breast Cancer

Cited by 5 publications

References 30 publications

Radiation Dermatitis and Pneumonitis in Patients Irradiated for Breast Cancer

Radiation Dermatitis and Pneumonitis in Patients Irradiated for Breast Cancer

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

Predicting Patterns of Distant Metastasis in Breast Cancer Patients following Local Regional Therapy Using Machine Learning

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