Effects of lung tissue characterization in radiotherapy of breast cancer under deep inspiration breath hold when using Monte Carlo dosimetry

PurposeTo investigate the sensitivity of Monte Carlo (MC) calculated lung dose distributions to lung tissue characterization in external beam radiotherapy of breast cancer under Deep Inspiration Breath Hold (DIBH).MethodsEGSnrc based MC software was employed. Mean lung densities for one hundred patients were analysed. CT number frequency and clinical dose distributions were calculated for 15 patients with mean lung density below 0.14 g/cm³. Lung volume with a pre-defined CT numbers was also considered. Lung tissue was characterized by applying different CT calibrations in the low-density region and air-lung tissue thresholds. Dose impact was estimated by Dose Volume Histogram (DVH) parameters.ResultsMean lung densities below 0.14 g/cm³ were found in 10% of the patients. CT numbers below −960 HU dominated the CT frequency distributions with a high rate of CT numbers at −990 HU. Mass density conversion approach influenced the DVH shape. V_4Gy and V_8Gy varied by 7% and 5% for the selected patients and by 9% and 3.5% for the pre-defined lung volume. V_16Gy and V_20Gy, were within 2.5%. Regions above 20 Gy were affected. Variations in air- lung tissue differentiation resulted in DVH parameters within 1%. Threshold at −990 HU was confirmed by the CT number frequency distributions.ConclusionsLung dose distributions were more sensitive to variations in the CT calibration curve below lung (inhale) density than to air-lung tissue differentiation. Low dose regions were mostly affected. The dosimetry effects were found to be potentially important to 10% of the patients treated under DIBH.     

Hypofractionated radiotherapy for early breast cancer: Review of phase III studies

Breast-conserving surgery including whole breast irradiation has long been a recommended procedure for early breast cancer. However, conventionally fractionated radiotherapy requires a lengthy hospitalisation or prolonged commuting to a hospital for radiotherapy. In recent years, hypofractionated radiotherapy has increasingly been used. This method involves higher fraction doses (above 2 Gy) as compared to conventional radiotherapy, so the total dose can be delivered in fewer fractions and in a shorter overall treatment time. This review aims at presenting most important outcomes of four randomised studies comparing conventional and hypofractionated radiotherapy schemes including a total of 7000 patients. These studies have not shown apparent differences in treatment efficacy, incidence of late post-radiotherapy complications or cosmetic effects during a 5–10 year follow-up, but longer observation is warranted to fully evaluate the safety of this method. Currently, major societies consider modestly hypofractionated radiotherapy schemes as a routine management in selected groups of patients undergoing breast-conserving surgery. However, this method should be used cautiously in patients with lymph node metastases, big breasts, receiving chemotherapy or trastuzumab, or those under 50 years of age.     

A six-gene-based signature for breast cancer radiotherapy sensitivity estimation

Breast cancer (BRCA) represents the most common malignancy among women worldwide with high mortality. Radiotherapy is a prevalent therapeutic for BRCA that with heterogeneous effectiveness among patients. Here, we proposed to develop a gene expression-based signature for BRCA radiotherapy sensitivity estimation. Gene expression profiles of BRCA samples from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) were obtained and used as training and independent testing dataset, respectively. Differential expression genes (DEGs) in BRCA samples compared with their paracancerous samples in the training set were identified by using the edgeR Bioconductor package. Univariate Cox regression analysis and LASSO Cox regression method were applied to screen optimal genes for constructing a radiotherapy sensitivity estimation signature. Nomogram combining independent prognostic factors was used to predict 1-, 3-, and 5-year OS of radiation-treated BRCA patients. Relative proportions of tumor infiltrating immune cells (TIICs) calculated by CIBERSORT and mRNA levels of key immune checkpoint receptors was adopted to explore the relation between the signature and tumor immune response. As a result, 603 DEGs were obtained in BRCA tumor samples, six of which were retained and used to construct the radiotherapy sensitivity prediction model. The signature was proved to be robust in both training and testing sets. In addition, the signature was closely related to the immune microenvironment of BRCA in the context of TIICs and immune checkpoint receptors’ mRNA levels. In conclusion, the present study obtained a radiotherapy sensitivity estimation signature for BRCA, which should shed new light in clinical and experimental research.     

Deep inspiration breath hold in post-operative radiotherapy for right breast cancer: a retrospective analysis

BackgroundThe aim of our study is to determine whether deep inspiration breath hold (DIBH) is effective for reducing exposure of the heart, left coronary artery (LAD) and both lungs in right breast radiotherapy.Materials and methodsWe have analyzed 10 consecutive patients with right-sided breast cancer (BC), simulated during free breathing (FB) and in DIBH modality. For all patients we contoured breast PTV and organs at risk (right and left lungs, heart, LAD) on both CT scans (FB and DIBH). Finally, 5 patients were treated with IMRT and 5 with VMAT techniques.ResultsAll patients were able to end the treatments in DIBH modalities regardless of the longer treatment time in comparison to FB. The maximum and mean dose to the heart are lower in the DIBH modality. The mean values of the heart mean dose were 1.76 Gy in DIBH and 2.19 Gy in FB. The mean heart maximum dose in DIBH and FB were, respectively, 9.3 Gy and 11 Gy. Likewise, the maximum dose to the LAD is lower in DIBH; 2.57 Gy versus 3.56 Gy in FB. Noteworthy, 3 patients with hepatomegaly treated with the DIBH technique showed a higher ipsilateral lung dose than FB, but a decrease of liver dose.ConclusionWe report that the use of DIBH for right-sided BC allows the dose to the heart, LAD and to the liver to be reduced in case of hepatomegaly. This technique is well tolerated by patients, when adequately trained, and could be considered effective even in right sided BC.     

Mathematical modelling of radiotherapy strategies for early breast cancer

Targeted intraoperative radiotherapy (Targit) is a new concept of partial breast irradiation where single fraction radiotherapy is delivered directly to the tumour bed. Apart from logistic advantages, this strategy minimizes the risk of missing the tumour bed and avoids delay between surgery and radiotherapy. It is presently being compared with the standard fractionated external beam radiotherapy (EBRT) in randomized trials. In this paper we present a mathematical model for the growth and invasion of a solid tumour into a domain of tissue (in this case breast tissue), and then a model for surgery and radiation treatment of this tumour. We use the established linear-quadratic (LQ) model to compute the survival probabilities for both tumour cells and irradiated breast tissue and then simulate the effects of conventional EBRT and Targit. True local recurrence of the tumour could arise either from stray tumour cells, or the tumour bed that harbours morphologically normal cells having a predisposition to genetic changes, such as a loss of heterozygosity (LOH) in genes that are crucial for tumourigenesis, e.g. tumour suppressor genes (TSGs). Our mathematical model predicts that the single high dose of radiotherapy delivered by Targit would result in eliminating all these sources of recurrence, whereas the fractionated EBRT would eliminate stray tumour cells, but allow (by virtue of its very schedule) the cells with LOH in TSGs or cell-cycle checkpoint genes to pass on low-dose radiation-induced DNA damage and consequently mutations that may favour the development of a new tumour. The mathematical model presented here is an initial attempt to model a biologically complex phenomenon that has until now received little attention in the literature and provides a 'proof of principle' that it is possible to produce clinically testable hypotheses on the effects of different approaches of radiotherapy for breast cancer.     

Feasibility of image quality improvement for high-speed CBCT imaging using deep convolutional neural network for image-guided radiotherapy in prostate cancer

PurposeHigh-speed cone-beam computed tomography (CBCT) scan for image-guided radiotherapy (IGRT) can reduce both the scan time and the exposure dose. However, it causes noise and artifacts in the reconstructed images due to the lower number of acquired projection data. The purpose of this study is to improve the image quality of high-speed CBCT using a deep convolutional neural network (DCNN).MethodsCBCT images of 36 prostate cancer patients were selected. The CBCT images acquired at normal scan speed were defined as CBCT_100%. Simulated high-speed CBCT images acquired at twofold and fourfold scan speed were created, which were defined as CBCT_50% and CBCT_25%, respectively. The image quality of the CBCT_50% was treated as the requirement for IGRT in this study because previous studies reported that its image is sufficient with respect to IGRT. The DCNN model was trained to learn direct mapping from CBCT_25% to the corresponding CBCT_100%. The performance of the DCNN model was evaluated using the sixfold cross-validation method. CBCT images generated by DCNN (CBCT_25%+DCNN) were evaluated for voxel value accuracy and image quality.ResultsThe DCNN model can process CBCT_25% of a new patient within 0.06 s/slice. The CBCT_25%+DCNN was comparable to the CBCT_50% in terms of both voxel value accuracy and image quality.ConclusionsWe developed a DCNN model to remove noise and artifacts from high-speed CBCT. We emphasize that it is possible to reduce exposure to one quarter and to increase the CBCT scan speed by a factor of four.     

Fully automatic and robust segmentation of the clinical target volume for radiotherapy of breast cancer using big data and deep learning

PurposeTo train and evaluate a very deep dilated residual network (DD-ResNet) for fast and consistent auto-segmentation of the clinical target volume (CTV) for breast cancer (BC) radiotherapy with big data.MethodsDD-ResNet was an end-to-end model enabling fast training and testing. We used big data comprising 800 patients who underwent breast-conserving therapy for evaluation. The CTV were validated by experienced radiation oncologists. We performed a fivefold cross-validation to test the performance of the model. The segmentation accuracy was quantified by the Dice similarity coefficient (DSC) and the Hausdorff distance (HD). The performance of the proposed model was evaluated against two different deep learning models: deep dilated convolutional neural network (DDCNN) and deep deconvolutional neural network (DDNN).ResultsMean DSC values of DD-ResNet (0.91 and 0.91) were higher than the other two networks (DDCNN: 0.85 and 0.85; DDNN: 0.88 and 0.87) for both right-sided and left-sided BC. It also has smaller mean HD values of 10.5 mm and 10.7 mm compared with DDCNN (15.1 mm and 15.6 mm) and DDNN (13.5 mm and 14.1 mm). Mean segmentation time was 4 s, 21 s and 15 s per patient with DDCNN, DDNN and DD-ResNet, respectively. The DD-ResNet was also superior with regard to results in the literature.ConclusionsThe proposed method could segment the CTV accurately with acceptable time consumption. It was invariant to the body size and shape of patients and could improve the consistency of target delineation and streamline radiotherapy workflows.     

Surface guided 3DCRT in deep-inspiration breath-hold for left sided breast cancer radiotherapy: implementation and first clinical experience in Iran

BackgroundThe aim of the study is to evaluate the overall accuracy of the surface-guided radiotherapy (SGRT) workflow through a comprehensive commissioning and quality assurance procedures and assess the potential benefits of deep-inspiration breath-hold (DIBH) radiotherapy as a cardiac and lung dose reduction approach for left-sided breast cancer irradiation.Materials and methodsAccuracy and reproducibility of the optical surface scanner used for DIBH treatment were evaluated using different phantoms. Patient positioning accuracy and reproducibility of DIBH treatment were evaluated. Twenty patients were studied for treatment plan quality in target dose coverage and healthy organ sparing for the two different treatment techniques.ResultsReproducibility tests for the surface scanner showed good stability within 1 mm in all directions. The maximum position variation between applied shifts on the couch and the scanner measured offsets is 1 mm in all directions. The clinical study of 200 fractions showed good agreement between the surface scanner and portal imaging with the isocenter position deviation of less than 3 mm in each lateral, longitudinal, and vertical direction. The standard deviation of the DIBH level showed a value of < 2 mm during all evaluated DIBHs. Compared to the free breathing (FB) technique, DIBH showed significant reduction of 48% for heart mean dose, 43% for heart V25, and 20% for ipsilateral lung V20.ConclusionSurface-guided radiotherapy can be regarded as an accurate tool for patient positioning and monitoring in breast radiotherapy. DIBH treatment are considered to be effective techniques in heart and ipsilateral lung dose reductions for left breast radiotherapy.     

Advanced dose calculation algorithms in lung cancer radiotherapy: Implications for SBRT and locally advanced disease in deep inspiration breath hold

PurposeEvaluating performance of modern dose calculation algorithms in SBRT and locally advanced lung cancer radiotherapy in free breathing (FB) and deep inspiration breath hold (DIBH).MethodsFor 17 patients with early stage and 17 with locally advanced lung cancer, a plan in FB and in DIBH were generated with Anisotropic Analytical Algorithm (AAA). Plans for early stage were 3D-conformal SBRT, 45 Gy in 3 fractions, prescribed to 95% isodose covering 95% of PTV and aiming for 140% dose centrally in the tumour. Locally advanced plans were volumetric modulated arc therapy, 66 Gy in 33 fractions, prescribed to mean PTV dose. Calculation grid size was 1 mm for SBRT and 2.5 mm for locally advanced plans. All plans were recalculated with AcurosXB with same MU as in AAA, for comparison on target coverage and dose to risk organs.ResultsLung volume increased in DIBH, resulting in decreased lung density (6% for early and 13% for locally-advanced group).In SBRT, AAA overestimated mean and near-minimum PTV dose (p-values < 0.01) compared to AcurosXB, with largest impact in DIBH (differences of up to 11 Gy). These clinically relevant differences may be a combination of small targets and large dose gradients within the PTV.In locally advanced group, AAA overestimated mean GTV, CTV and PTV doses by median less than 0.8 Gy and near-minimum doses by median 0.4–2.7 Gy.No clinically meaningful difference was observed for lung and heart dose metrics between the algorithms, for both FB and DIBH.ConclusionsAAA overestimated target coverage compared to AcurosXB, especially in DIBH for SBRT.     

Normal tissue sparing potential of scanned proton beams with and without respiratory gating for the treatment of internal mammary nodes in breast cancer radiotherapy

Proton therapy has shown potential for reducing doses to normal tissues in breast cancer radiotherapy. However data on the impact of protons when including internal mammary nodes (IMN) in the target for breast radiotherapy is comparatively scarce. This study aimed to evaluate normal tissue doses when including the IMN in regional RT with scanned proton beams, with and without respiratory gating. The study cohort was composed of ten left-sided breast patients CT-scanned during enhanced inspiration gating (EIG) and free-breathing (FB). Proton plans were designed for the target including or excluding the IMN. Targets and organs-at-risk were delineated according to RTOG guidelines. Comparison was performed between dosimetric parameters characterizing target coverage and OAR radiation burden. Statistical significance of differences was tested using a paired, two-tailed Student’s t-test. Inclusion of the IMN in the target volume led to a small increase of the cardiopulmonary burden. The largest differences were seen for the ipsilateral lung where the mean dose increased from 6.1 to 6.6 Gy (RBE) (P < 0.0001) in FB plans and from 6.9 to 7.4 Gy (RBE) (P = 0.003) in EIG plans. Target coverage parameters were very little affected by the inclusion of IMN into the treatment target. Radiotherapy with scanned proton beams has the potential of maintaining low cardiovascular burden when including the IMN into the target, irrespective of whether respiratory gating is used or not.