Complication probability model for subcutaneous fibrosis based on published data of partial and whole breast irradiation

Contouring variation affects estimates of normal tissue complication probability for breast fibrosis after radiotherapy

BACKGROUND

Normal tissue complication probability (NTCP) models can be useful to estimate the risk of fibrosis after breast-conserving surgery (BCS) and radiotherapy (RT) to the breast. However, they are subject to uncertainties. We present the impact of contouring variation on the prediction of fibrosis.

MATERIALS AND METHODS

280 breast cancer patients treated BCS-RT were included. Nine Clinical Target Volume (CTV) contours were created for each patient: i) CTV_crop (reference), cropped 5 mm from the skin and ii) CTV_skin, uncropped and including the skin, iii) segmenting the 95% isodose (Iso95%) and iv) 3 different auto-contouring atlases generating uncropped and cropped contours (Atlas_skin/Atlas_crop). To illustrate the impact of contour variation on NTCP estimates, we applied two equations predicting fibrosis grade ≥ 2 at 5 years, based on Lyman-Kutcher-Burman (LKB) and Relative Seriality (RS) models, respectively, to each contour. Differences were evaluated using repeated-measures ANOVA. For completeness, the association between observed fibrosis events and NTCP estimates was also evaluated using logistic regression.

RESULTS

There were minimal differences between contours when the same contouring approach was followed (cropped and uncropped). CTV_skin and Atlas_skin contours had lower NTCP estimates (-3.92%, IQR 4.00, p < 0.05) compared to CTV_crop. No significant difference was observed for Atlas_crop and Iso95% contours compared to CTV_crop. For the whole cohort, NTCP estimates varied between 5.3% and 49.5% (LKB) or 2.2% and 49.6% (RS) depending on the choice of contours. NTCP estimates for individual patients varied by up to a factor of 4. Estimates from "skin" contours showed higher agreement with observed events.

CONCLUSION

Contour variations can lead to significantly different NTCP estimates for breast fibrosis, highlighting the importance of standardising breast contours before developing and/or applying NTCP models.

Reducing the Risk of Secondary Lung Cancer in Treatment Planning of Accelerated Partial Breast Irradiation

Purpose: Adjuvant accelerated partial breast irradiation (APBI) results in low local recurrence risks. However, the survival benefit of adjuvant radiotherapy APBI for low-risk breast cancer might partially be offset by the risk of radiation-induced lung cancer. Reducing the lung dose mitigates this risk, but this could result in higher doses to the ipsilateral breast. Different external beam APBI techniques are equally conformal and homogenous, but the intermediate to low dose distribution differs. Thus, the risk of toxicity is different. The purpose of this study is to quantify the trade-off between secondary lung cancer risk and breast dose in treatment planning and to compare an optimal coplanar and non-coplanar technique.

Methods: A total of 440 APBI treatment plans were generated using automated treatment planning for a coplanar VMAT beam-setup and a non-coplanar robotic stereotactic radiotherapy beam-setup. This enabled an unbiased comparison of two times 11 Pareto-optimal plans for 20 patients, gradually shifting priority from maximum lung sparing to maximum ipsilateral breast sparing. The excess absolute risks of developing lung cancer and breast fibrosis were calculated using the Schneider model for lung cancer and the Avanzo model for breast fibrosis.

Results: Prioritizing lung sparing reduced the mean lung dose from 2.2 Gy to as low as 0.3 Gy for the non-coplanar technique and from 1.9 Gy to 0.4 Gy for the coplanar technique, corresponding to a 7- and 4-fold median reduction of secondary lung cancer risk, respectively, compared to prioritizing breast sparing. The increase in breast dose resulted in a negligible 0.4% increase in fibrosis risk. The use of non-coplanar beams resulted in lower secondary cancer and fibrosis risks (p < 0.001). Lung sparing also reduced the mean heart dose for both techniques.

Conclusions: The risk of secondary lung cancer of external beam APBI can be dramatically reduced by prioritizing lung sparing during treatment planning. The associated increase in breast dose did not lead to a relevant increase in fibrosis risk. The use of non-coplanar beams systematically resulted in the lowest risks of secondary lung cancer and fibrosis. Prioritizing lung sparing during treatment planning could increase the overall survival of early-stage breast cancer patients by reducing mortality due to secondary lung cancer and cardiovascular toxicity.

Electron Density and Biologically Effective Dose (BED) Radiomics-Based Machine Learning Models to Predict Late Radiation-Induced Subcutaneous Fibrosis

Purpose: to predict the occurrence of late subcutaneous radiation induced fibrosis (RIF) after partial breast irradiation (PBI) for breast carcinoma by using machine learning (ML) models and radiomic features from 3D Biologically Effective Dose (3D-BED) and Relative Electron Density (3D-RED). Methods: 165 patients underwent external PBI following a hypo-fractionation protocol consisting of 40 Gy/10 fractions, 35 Gy/7 fractions, and 28 Gy/4 fractions, for 73, 60, and 32 patients, respectively. Physicians evaluated toxicity at regular intervals by the Common Terminology Adverse Events (CTAE) version 4.0. RIF was assessed every 3 months after the completion of radiation course and scored prospectively. RIF was experienced by 41 (24.8%) patients after average 5 years of follow up. The Hounsfield Units (HU) of the CT-images were converted into relative electron density (3D-RED) and Dose maps into Biologically Effective Dose (3D-BED), respectively. Shape, first-order and textural features of 3D-RED and 3D-BED were calculated in the planning target volume (PTV) and breast. Clinical and demographic variables were also considered (954 features in total). Imbalance of the dataset was addressed by data augmentation using ADASYN technique. A subset of non-redundant features that best predict the data was identified by sequential feature selection. Support Vector Machines (SVM), ensemble machine learning (EML) using various aggregation algorithms and Naive Bayes (NB) classifiers were trained on patient dataset to predict RIF occurrence. Models were assessed using sensitivity and specificity of the ML classifiers and the area under the receiver operator characteristic curve (AUC) of the score functions in repeated 5-fold cross validation on the augmented dataset. Results: The SVM model with seven features was preferred for RIF prediction and scored sensitivity 0.83 (95% CI 0.80-0.86), specificity 0.75 (95% CI 0.71-0.77) and AUC of the score function 0.86 (0.85-0.88) on cross-validation. The selected features included cluster shade and Run Length Non-uniformity of breast 3D-BED, kurtosis and cluster shade from PTV 3D-RED, and 10th percentile of PTV 3D-BED. Conclusion: Textures extracted from 3D-BED and 3D-RED in the breast and PTV can predict late RIF and may help better select patient candidates to exclusive PBI.

NTCP Models for Severe Radiation Induced Dermatitis After IMRT or Proton Therapy for Thoracic Cancer Patients

Radiation therapy (RT) of thoracic cancers may cause severe radiation dermatitis (RD), which impacts on the quality of a patient's life. Aim of this study was to analyze the incidence of acute RD and develop normal tissue complication probability (NTCP) models for severe RD in thoracic cancer patients treated with Intensity-Modulated RT (IMRT) or Passive Scattering Proton Therapy (PSPT). We analyzed 166 Non-Small-Cell Lung Cancer (NSCLC) patients prospectively treated at a single institution with IMRT (103 patients) or PSPT (63 patients). All patients were treated to a prescribed dose of 60 to 74 Gy in conventional daily fractionation with concurrent chemotherapy. RD was scored according to CTCAE v3 scoring system. For each patient, the epidermis structure (skin) was automatically defined by an in house developed segmentation algorithm. The absolute dose-surface histogram (DSH) of the skin were extracted and normalized using the Body Surface Area (BSA) index as scaling factor. Patient and treatment-related characteristics were analyzed. The Lyman-Kutcher-Burman (LKB) NTCP model recast for DSH and the multivariable logistic model were adopted. Models were internally validated by Leave-One-Out method. Model performance was evaluated by the area under the receiver operator characteristic curve, and calibration plot parameters. Fifteen of 166 (9%) patients developed severe dermatitis (grade 3). RT technique did not impact RD incidence. Total gross tumor volume (GTV) size was the only non dosimetric variable significantly correlated with severe RD (p = 0.027). Multivariable logistic modeling resulted in a single variable model including S_20Gy, the relative skin surface receiving more than 20 Gy (OR = 31.4). The cut off for S_20Gy was 1.1% of the BSA. LKB model parameters were TD₅₀ = 9.5 Gy, m = 0.24, n = 0.62. Both NTCP models showed comparably high prediction and calibration performances. Despite skin toxicity has long been considered a potential limiting factor in the clinical use of PSPT, no significant differences in RD incidence was found between RT modalities. Once externally validated, the availability of NTCP models for prediction of severe RD may advance treatment planning optimization.

A new formalism of Dose Surface Histograms for robust modeling of skin toxicity in radiation therapy

PURPOSE

To present a new formalism for a robust computation of Dose-Surface Histograms (DSHs) to be exploited in the analysis of surface effects in radiation induced toxicity phenomena.

METHODS

A new formal recipe for the DSH extraction is described. It is based on the computation of the Dose-Volume Histogram (DVH) on a 3D structure in the limit of vanishing thickness to approach the two-dimensional organ manifold. The theory is customized for the application to skin description.

RESULTS

The derived formalism resulted in a redefinition of the generalized equivalent uniform dose (gEUD) and, accordingly, in an extension of the scope of the classical Lyman-Kutcher-Burman (LKB) Normal Tissue Complication Probability (NTCP) to a DSH-based toxicity modeling.

CONCLUSIONS

Our approach properly fits the intrinsic 3D nature of the DSH computation issue, and guarantees the rotational invariance and the robustness of the results. The proposed formalism can be easily implemented in treatment planning systems for dose optimization and potentially paves the way to a consistent analysis of radiation-induced morbidity endpoints related to surface effects in hollow organs.

Ten daily fractions for partial breast irradiation. Long-term results of a prospective phase II trial

Partial breast irradiation (PBI) is an effective adjuvant treatment after breast conservative surgery for selected early-stage breast cancer patients. However, the best fractionation scheme is not well defined. Hereby, we report the 5-year clinical outcome and toxicity of a phase II prospective study of a novel regimen to deliver PBI, which consists in 40 Gy delivered in 10 daily fractions. Patients with early-stage (pT1-pT2, pN0-pN1a, M0) invasive breast cancer were enrolled after conservative surgery. The minimum age at diagnosis was 60 years old. PBI was delivered with 3D-conformal radiotherapy technique with a total dose of 40 Gy, fractionated in 10 daily fractions (4 Gy/fraction). Eighty patients were enrolled. The median follow-up was 67 months. Five-year local control (LC), disease-free survival (DFS), and overall survival (OS) were 95%, 91%, and 96%, respectively. Grade I and II subcutaneous fibrosis were documented in 23% and 5% of cases. No grade III late toxicity was observed. PBI delivered in 40 Gy in 10 daily fractions provided good clinical results and was a valid radiotherapy option for early-stage breast cancer patients.

Development of a normal tissue complication probability model for late unfavourable aesthetic outcome after breast-conserving therapy

PURPOSE/OBJECTIVES

To develop a normal tissue complication probability (NTCP) model for late unfavourable aesthetic outcome (AO) after breast-conserving therapy.

MATERIAL AND METHODS

The BCCT.core software evaluated the AO using standardized photographs of patients treated between 2009 and 2014. Dose maps in 2 Gy equivalents were calculated assuming α/β = 3.6 Gy. Uni- and multivariable logistic regression analysis was performed to study the predictive value of clinicopathological and dosimetric variables for unfavourable AO. The Lyman Kutcher Burman (LKB) model was fit to the data with dose modifying factors (dmf). Model performance was assessed with the area under the curve (AUC) of the receiver operating characteristic curve and bootstrap sampling.

RESULTS

Forty-four of the 121 analysed patients (36%) developed unfavourable AO. In the optimal multivariable logistic regression model, a larger breast volume receiving ≥55 Gy (V55), a seroma and an axillary lymph node dissection (ALND) were independently associated with an unfavourable AO, AUC = 0.75 (95%CI 0.64;0.85). Beta-estimates were -2.68 for β₀, 0.057 for V55, 1.55 for seroma and 1.20 for ALND. The optimal LKB model parameters were EUD_3.6(50) = 63.3 Gy, n = 1.00, m = 0.23, dmf(seroma) = 0.83 and dmf(ALND) = 0.84, AUC = 0.74 (95%CI 0.61;0.83).

CONCLUSIONS

An NTCP model for late unfavourable AO after breast-conserving therapy was developed including seroma, axillary lymphadenectomy and V55.

Seven fractions to deliver partial breast irradiation: the toxicity is Low

Purpose

To assess toxicity and clinical outcome, in breast cancer patients treated with external beam partial breast irradiation (PBI) consisting of 35 Gy in 7 daily fractions (5 Gy/fraction).

Materials and Methods

Patients affected by early-stage breast cancer were enrolled in this phase II trial. Patients had to be 60 years old or over and treated with breast conservative surgery for early stage invasive carcinoma.

Results

Seventy-three patients were analyzed. Median follow-up was 40 months. The proposed schedule was well tolerated. No Grade 3 toxicity was documented. Late toxicity was assessable for all the treated patients. Two patients (2.7%) developed Grade 2 pain 6 months after PBI. Four patients (5%) developed asymptomatic fat necrosis. Grade 2 fibrosis was observed in 5 patients (6.7%). No correlation was found between early and late toxicity and the type of adjuvant systemic therapy (no therapy vs. hormonal therapy vs. chemotherapy). No statistical correlation between dosimetric parameters and toxicity was found. Patients who developed Grade 2 radiation fibrosis had not higher radiation volumes to the untreated normal breast than those without fibrosis. Cosmesis was judged good/excellent in the majority of the cases (93%). One patient relapsed locally, and one developed distant metastases, corresponding to a 5-year local control and distant metastases-free survival of 98% and 96.7%, respectively.

Conclusions

35 Gy in 7 daily fractions is an effective and well-tolerated regimen to deliver PBI.

Dose-surface analysis for prediction of severe acute radio-induced skin toxicity in breast cancer patients

BACKGROUND

Severe acute radiation-induced skin toxicity (RIST) after breast irradiation is a side effect impacting the quality of life in breast cancer (BC) patients. The aim of the present study was to develop normal tissue complication probability (NTCP) models of severe acute RIST in BC patients.

PATIENTS AND METHODS

We evaluated 140 consecutive BC patients undergoing conventional three-dimensional conformal radiotherapy (3D-CRT) after breast conserving surgery in a prospective study assessing acute RIST. The acute RIST was classified according to the RTOG scoring system. Dose-surface histograms (DSHs) of the body structure in the breast region were extracted as representative of skin irradiation. Patient, disease, and treatment-related characteristics were analyzed along with DSHs. NTCP modeling by Lyman-Kutcher-Burman (LKB) and by multivariate logistic regression using bootstrap resampling techniques was performed. Models were evaluated by Spearman's Rs coefficient and ROC area.

RESULTS

By the end of radiotherapy, 139 (99%) patients developed any degree of acute RIST. G3 RIST was found in 11 of 140 (8%) patients. Mild-moderate (G1-G2) RIST was still present at 40 days after treatment in six (4%) patients. Using DSHs for LKB modeling of acute RIST severity (RTOG G3 vs. G0-2), parameter estimates were TD50=39 Gy, n=0.38 and m=0.14 [Rs = 0.25, area under the curve (AUC) = 0.77, p = 0.003]. On multivariate analysis, the most predictive model of acute RIST severity was a two-variable model including the skin receiving ≥30 Gy (S30) and psoriasis [Rs = 0.32, AUC = 0.84, p < 0.001].

CONCLUSIONS

Using body DSH as representative of skin dose, the LKB n parameter was consistent with a surface effect for the skin. A good prediction performance was obtained using a data-driven multivariate model including S30 and a pre-existing skin disease (psoriasis) as a clinical factor.

Hypofractionation of partial breast irradiation using radiobiological models

PURPOSE

To reduce the fraction number in Partial Breast Irradiation (PBI) with initial prescription of 40 Gy in 10 fractions using radiobiological models with specific focus on risk of moderate/severe radiation-induced fibrosis (RIF) and report clinical results.

METHODS AND MATERIALS

68 patients (patient group A) were treated with 40 Gy in 10 fractions delivered by field-in-field, forward-planned IMRT. Isotoxic regimens with decreasing number of fractions were calculated using Biological Effective Dose (BED) to the breast. Risk for RIF in hypofractionated treatment was predicted by calculating NTCP from DVHs of group A rescaled to fractions and dose of novel regimens. Moderate/severe RIF was prospectively scored during follow-up. Various NTCP models, with and without incomplete repair correction, were assessed from difference to observed incidence of RIF. In order to verify the value for α/β of 3 Gy assumed for breast, we fitted α/β to observed incidences of moderate/severe RIF.

RESULTS

Treatments with 35 Gy/7f and 28 Gy/4f were selected for the fraction reduction protocol. 75 patients (group B) were treated in 35 Gy/7f. Incidence of moderate/severe RIF was 5.9% in group A, 5.3% in group B. The NTCP model with correction for incomplete repair had lowest difference from observed RIF. The α/β obtained from fitting was 2.8 (95%CIs 1.1-10.7) Gy.

CONCLUSIONS

The hypofractionated regimen was well tolerated. The model for NTCP corrected for incomplete repair was the most accurate and an assumed α/β value of 3 Gy is consistent with our patient data. The hypofractionation protocol is continuing with patients treated with 28 Gy/4f.

Normal tissue complication probability (NTCP) parameters for breast fibrosis: pooled results from two randomised trials

INTRODUCTION

The dose-volume effect of radiation therapy on breast tissue is poorly understood. We estimate NTCP parameters for breast fibrosis after external beam radiotherapy.

MATERIALS AND METHODS

We pooled individual patient data of 5856 patients from 2 trials including whole breast irradiation followed with or without a boost. A two-compartment dose volume histogram model was used with boost volume as the first compartment and the remaining breast volume as second compartment. Results from START-pilot trial (n=1410) were used to test the predicted models.

RESULTS

26.8% patients in the Cambridge trial (5 years) and 20.7% patients in the EORTC trial (10 years) developed moderate-severe breast fibrosis. The best fit NTCP parameters were BEUD3(50)=136.4 Gy, γ50=0.9 and n=0.011 for the Niemierko model and BEUD3(50)=132 Gy, m=0.35 and n=0.012 for the Lyman Kutcher Burman model. The observed rates of fibrosis in the START-pilot trial agreed well with the predicted rates.

CONCLUSIONS

This large multi-centre pooled study suggests that the effect of volume parameter is small and the maximum RT dose is the most important parameter to influence breast fibrosis. A small value of volume parameter 'n' does not fit with the hypothesis that breast tissue is a parallel organ. However, this may reflect limitations in our current scoring system of fibrosis.