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

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.

Radiation Dermatitis and Pneumonitis in Patients Irradiated for Breast Cancer

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. RT-regimen 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), Ki-67 ≥15% (p=0.012), and systemic treatment (p=0.002). On multivariate analysis, RT-regimen (p<0.0001) 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.

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

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.

Functionalized Hybrid Iron Oxide-Gold Nanoparticles Targeting Membrane Hsp70 Radiosensitize Triple-Negative Breast Cancer Cells by ROS-Mediated Apoptosis

Triple-negative breast cancer (TNBC) a highly aggressive tumor entity with an unfavorable prognosis, is treated by multimodal therapies, including ionizing radiation (IR). Radiation-resistant tumor cells, as well as induced normal tissue toxicity, contribute to the poor clinical outcome of the disease. In this study, we investigated the potential of novel hybrid iron oxide (Fe₃O₄)-gold (Au) nanoparticles (FeAuNPs) functionalized with the heat shock protein 70 (Hsp70) tumor-penetrating peptide (TPP) and coupled via a PEG4 linker (TPP-PEG4-FeAuNPs) to improve tumor targeting and uptake of NPs and to break radioresistance in TNBC cell lines 4T1 and MDA-MB-231. Hsp70 is overexpressed in the cytosol and abundantly presented on the cell membrane (mHsp70) of highly aggressive tumor cells, including TNBCs, but not on corresponding normal cells, thus providing a tumor-specific target. The Fe₃O₄ core of the NPs can serve as a contrast agent enabling magnetic resonance imaging (MRI) of the tumor, and the nanogold shell radiosensitizes tumor cells by the release of secondary electrons (Auger electrons) upon X-ray irradiation. We demonstrated that the accumulation of TPP-PEG4-FeAuNPs into mHsp70-positive TNBC cells was superior to that of non-conjugated FeAuNPs and FeAuNPs functionalized with a non-specific, scrambled peptide (NGL). After a 24 h co-incubation period of 4T1 and MDA-MB-231 cells with TPP-PEG4-FeAuNPs, but not with control hybrid NPs, ionizing irradiation (IR) causes a cell cycle arrest at G2/M and induces DNA double-strand breaks, thus triggering apoptotic cell death. Since the radiosensitizing effect was completely abolished in the presence of the ROS inhibitor N-acetyl-L-cysteine (NAC), we assume that the TPP-PEG4-FeAuNP-induced apoptosis is mediated via an increased production of ROS.

Risk Factors Related to Acute Radiation Dermatitis in Breast Cancer Patients After Radiotherapy: A Systematic Review and Meta-Analysis

Background

Acute radiation dermatitis (ARD) is the most common acute response after adjuvant radiotherapy in breast cancer patients and negatively affects patients’ quality of life. Some studies have reported several risk factors that can predict breast cancer patients who are at a high risk of ARD. This study aimed to identify patient- and treatment-related risk factors associated with ARD.

Methods

PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, and WanFang literature databases were searched for studies exploring the risk factors in breast cancer patients. The pooled effect sizes, relative risks (RRs), and 95% CIs were calculated using the random-effects model. Potential heterogeneity and sensitivity analyses by study design, ARD evaluation scale, and regions were also performed.

Results

A total of 38 studies composed of 15,623 breast cancer patients were included in the analysis. Of the seven available patient-related risk factors, four factors were significantly associated with ARD: body mass index (BMI) ≥25 kg/m² (RR = 1.11, 95% CI = 1.06–1.16, I² = 57.1%), large breast volume (RR = 1.02, 95% CI = 1.01–1.03, I² = 93.2%), smoking habits (RR = 1.70, 95% CI = 1.24–2.34, I² = 50.7%), and diabetes (RR = 2.24, 95% CI = 1.53–3.27, I² = 0%). Of the seven treatment-related risk factors, we found that hypofractionated radiotherapy reduced the risk of ARD in patients with breast cancer compared with that in conventional fractionated radiotherapy (RR = 0.28, 95% CI = 0.19–0.43, I² = 84.5%). Sequential boost and bolus use was significantly associated with ARD (boost, RR = 1.91, 95% CI = 1.34–2.72, I² = 92.5%; bolus, RR = 1.94, 95% CI = 1.82–4.76, I² = 23.8%). However, chemotherapy regimen (RR = 1.17, 95% CI = 0.95–1.45, I² = 57.2%), hormone therapy (RR = 1.35, 95% CI = 0.94–1.93, I² = 77.1%), trastuzumab therapy (RR = 1.56, 95% CI = 0.18–1.76, I² = 91.9%), and nodal irradiation (RR = 1.57, 95% CI = 0.98–2.53, I² = 72.5%) were not correlated with ARD. Sensitivity analysis results showed that BMI was consistently associated with ARD, while smoking, breast volume, and boost administration were associated with ARD depending on study design, country of study, and toxicity evaluation scale used. Hypofractionation was consistently shown as protective. The differences between study design, toxicity evaluation scale, and regions might explain a little of the sources of heterogeneity.

Conclusion

The results of this systematic review and meta-analysis indicated that BMI ≥ 25 kg/m² was a significant predictor of ARD and that hypofractionation was consistently protective. Depending on country of study, study design, and toxicity scale used, breast volume, smoking habit, diabetes, and sequential boost and bolus use were also predictive of ARD.