Radiomics and deep learning in lung cancer.

Lung malignancies have been extensively characterized through radiomics and deep learning. By providing a three-dimensional characterization of the lesion, models based on radiomic features from computed tomography (CT) and positron-emission tomography (PET) have been developed to detect nodules, distinguish malignant from benign lesions, characterize their histology, stage, and genotype. Deep learning models have been applied to automatically segment organs at risk in lung cancer radiotherapy, stratify patients according to the risk for local and distant recurrence, and identify patients candidate for molecular targeted therapy and immunotherapy. Moreover, radiomics has also been applied successfully to predict side effects such as radiation- and immunotherapy-induced pneumonitis and differentiate lung injury from recurrence. Radiomics could also untap the potential for further use of the cone beam CT acquired for treatment image guidance, four-dimensional CT, and dose-volume data from radiotherapy treatment plans. Radiomics is expected to increasingly affect the clinical practice of treatment of lung tumors, optimizing the end-to-end diagnosis-treatment-follow-up chain. The main goal of this article is to provide an update on the current status of lung cancer radiomics.

AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics.

INTRODUCTION: The quantitative imaging features (radiomics) that can be obtained from the different modalities of current-generation hybrid imaging can give complementary information with regard to the tumour environment, as they measure different morphologic and functional imaging properties. These multi-parametric image descriptors can be combined with artificial intelligence applications into predictive models. It is now the time for hybrid PET/CT and PET/MRI to take the advantage offered by radiomics to assess the added clinical benefit of using multi-parametric models for the personalized diagnosis and prognosis of different disease phenotypes. OBJECTIVE: The aim of the paper is to provide an overview of current challenges and available solutions to translate radiomics into hybrid PET-CT and PET-MRI imaging for a smart and truly multi-parametric decision model.

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.

Fast time-of-flight camera based surface registration for radiotherapy patient positioning.

PURPOSE: This work introduces a rigid registration framework for patient positioning in radiotherapy, based on real-time surface acquisition by a time-of-flight (ToF) camera. Dynamic properties of the system are also investigated for future gating/tracking strategies. METHODS: A novel preregistration algorithm, based on translation and rotation-invariant features representing surface structures, was developed. Using these features, corresponding three-dimensional points were computed in order to determine initial registration parameters. These parameters became a robust input to an accelerated version of the iterative closest point (ICP) algorithm for the fine-tuning of the registration result. Distance calibration and Kalman filtering were used to compensate for ToF-camera dependent noise. Additionally, the advantage of using the feature based preregistration over an "ICP only" strategy was evaluated, as well as the robustness of the rigid-transformation-based method to deformation. RESULTS: The proposed surface registration method was validated using phantom data. A mean target registration error (TRE) for translations and rotations of 1.62 ± 1.08 mm and 0.07° ± 0.05°, respectively, was achieved. There was a temporal delay of about 65 ms in the registration output, which can be seen as negligible considering the dynamics of biological systems. Feature based preregistration allowed for accurate and robust registrations even at very large initial displacements. Deformations affected the accuracy of the results, necessitating particular care in cases of deformed surfaces. CONCLUSIONS: The proposed solution is able to solve surface registration problems with an accuracy suitable for radiotherapy cases where external surfaces offer primary or complementary information to patient positioning. The system shows promising dynamic properties for its use in gating/tracking applications. The overall system is competitive with commonly-used surface registration technologies. Its main benefit is the usage of a cost-effective off-the-shelf technology for surface acquisition. Further strategies to improve the registration accuracy are under development.

Deep learning based time-to-event analysis with PET, CT and joint PET/CT for head and neck cancer prognosis.

OBJECTIVES: Recent studies have shown that deep learning based on pre-treatment positron emission tomography (PET) or computed tomography (CT) is promising for distant metastasis (DM) and overall survival (OS) prognosis in head and neck cancer (HNC). However, lesion segmentation is typically required, resulting in a predictive power susceptible to variations in primary and lymph node gross tumor volume (GTV) segmentation. This study aimed at achieving prognosis without GTV segmentation, and extending single modality prognosis to joint PET/CT to allow investigating the predictive performance of combined- compared to single-modality inputs. METHODS: We employed a 3D-Resnet combined with a time-to-event outcome model to incorporate censoring information. We focused on the prognosis of DM and OS for HNC patients. For each clinical endpoint, five models with PET and/or CT images as input were compared: PET-GTV, PET-only, CT-GTV, CT-only, and PET/CT-GTV models, where -GTV indicates that the corresponding images were masked using the GTV contour. Publicly available delineated CT and PET scans from 4 different Canadian hospitals (293) and the MAASTRO clinic (74) were used for training by 3-fold cross-validation (CV). For independent testing, we used 110 patients from a collaborating institution. The predictive performance was evaluated via Harrell's Concordance Index (HCI) and Kaplan-Meier curves. RESULTS: In a 5-year time-to-event analysis, all models could produce CV HCIs with median values around 0.8 for DM and 0.7 for OS. The best performance was obtained with the PET-only model, achieving a median testing HCI of 0.82 for DM and 0.69 for OS. Compared with the PET/CT-GTV model, the PET-only still had advantages of up to 0.07 in terms of testing HCI. The Kaplan-Meier curves and corresponding log-rank test results also demonstrated significant stratification capability of our models for the testing cohort. CONCLUSION: Deep learning-based DM and OS time-to-event models showed predictive capability and could provide indications for personalized RT. The best predictive performance achieved by the PET-only model suggested GTV segmentation might be less relevant for PET-based prognosis.

Combining computed tomography and biologically effective dose in radiomics and deep learning improves prediction of tumor response to robotic lung stereotactic body radiation therapy.

PURPOSE: The aim of this study is to improve the performance of machine learning (ML) models in predicting response of non-small cell lung cancer (NSCLC) to stereotactic body radiation therapy (SBRT) by integrating image features from pre-treatment computed tomography (CT) with features from the biologically effective dose (BED) distribution. MATERIALS AND METHODS: Image features, consisting of crafted radiomic features or machine-learned features extracted using a convolutional neural network, were calculated from pre-treatment CT data and from dose distributions converted into BED for 80 NSCLC lesions over 76 patients treated with robotic guided SBRT. ML models using different combinations of features were trained to predict complete or partial response according to response criteria in solid tumors, including radiomics CT (RadCT ), radiomics CT and BED (RadCT,BED ), deep learning (DL) CT (DLCT ), and DL CT and BED (DLCT,BED ). Training of ML included feature selection by neighborhood component analysis followed by ensemble ML using robust boosting. A model was considered as acceptable when the sum of average sensitivity and specificity on test data in repeated cross validations was at least 1.5. RESULTS: Complete or partial response occurred in 58 out of 80 lesions. The best models to predict the tumor response were those using BED variables, achieving significantly better area under curve (AUC) and accuracy than those using only features from CT, including a RadCT,BED model using three radiomic features from BED, which scored an accuracy of 0.799 (95% confidence intervals (0.75-0.85)) and AUC of 0.773 (0.688-0.846), and a DLCT,BED model also using three variables with an accuracy of 0.798 (0.649-0.829) and AUC of 0.812 (0.755-0.867). CONCLUSION: According to our results, the inclusion of BED features improves the response prediction of ML models for lung cancer patients undergoing SBRT, regardless of the use of radiomic or DL features.

Distant metastasis time to event analysis with CNNs in independent head and neck cancer cohorts.

Deep learning models based on medical images play an increasingly important role for cancer outcome prediction. The standard approach involves usage of convolutional neural networks (CNNs) to automatically extract relevant features from the patient's image and perform a binary classification of the occurrence of a given clinical endpoint. In this work, a 2D-CNN and a 3D-CNN for the binary classification of distant metastasis (DM) occurrence in head and neck cancer patients were extended to perform time-to-event analysis. The newly built CNNs incorporate censoring information and output DM-free probability curves as a function of time for every patient. In total, 1037 patients were used to build and assess the performance of the time-to-event model. Training and validation was based on 294 patients also used in a previous benchmark classification study while for testing 743 patients from three independent cohorts were used. The best network could reproduce the good results from 3-fold cross validation [Harrell's concordance indices (HCIs) of 0.78, 0.74 and 0.80] in two out of three testing cohorts (HCIs of 0.88, 0.67 and 0.77). Additionally, the capability of the models for patient stratification into high and low-risk groups was investigated, the CNNs being able to significantly stratify all three testing cohorts. Results suggest that image-based deep learning models show good reliability for DM time-to-event analysis and could be used for treatment personalisation.

Correlation of a hypoxia based tumor control model with observed local control rates in nasopharyngeal carcinoma treated with chemoradiotherapy.

PURPOSE: To extend the application of current radiation therapy (RT) based tumor control probability (TCP) models of nasopharyngeal carcinoma (NPC) to include the effects of hypoxia and chemoradiotherapy (CRT). METHODS: A TCP model is described based on the linear-quadratic model modified to account for repopulation, chemotherapy, heterogeneity of dose to the tumor, and hypoxia. Sensitivity analysis was performed to determine which parameters exert the greatest influence on the uncertainty of modeled TCP. On the basis of the sensitivity analysis, the values of specific radiobiological parameters were set to nominal values reported in the literature for NPC or head and neck tumors. The remaining radiobiological parameters were determined by fitting TCP to clinical local control data from published randomized studies using both RT and CRT. Validation of the model was performed by comparison of estimated TCP and average overall local control rate (LCR) for 45 patients treated at the institution with conventional linear-accelerator-based or helical tomotherapy based intensity-modulated RT and neoadjuvant chemotherapy. RESULTS: Sensitivity analysis demonstrates that the model is most sensitive to the radiosensitivity term alpha and the dose per fraction. The estimated values of alpha and OER from data fitting were 0.396 Gy(-1) and 1.417. The model estimate of TCP (average 90.9%, range 26.9%-99.2%) showed good correlation with the LCR (86.7%). CONCLUSIONS: The model implemented in this work provides clinicians with a useful tool to predict the success rate of treatment, optimize treatment plans, and compare the effects of multimodality therapy.

Machine and deep learning methods for radiomics.

Radiomics is an emerging area in quantitative image analysis that aims to relate large-scale extracted imaging information to clinical and biological endpoints. The development of quantitative imaging methods along with machine learning has enabled the opportunity to move data science research towards translation for more personalized cancer treatments. Accumulating evidence has indeed demonstrated that noninvasive advanced imaging analytics, that is, radiomics, can reveal key components of tumor phenotype for multiple three-dimensional lesions at multiple time points over and beyond the course of treatment. These developments in the use of CT, PET, US, and MR imaging could augment patient stratification and prognostication buttressing emerging targeted therapeutic approaches. In recent years, deep learning architectures have demonstrated their tremendous potential for image segmentation, reconstruction, recognition, and classification. Many powerful open-source and commercial platforms are currently available to embark in new research areas of radiomics. Quantitative imaging research, however, is complex and key statistical principles should be followed to realize its full potential. The field of radiomics, in particular, requires a renewed focus on optimal study design/reporting practices and standardization of image acquisition, feature calculation, and rigorous statistical analysis for the field to move forward. In this article, the role of machine and deep learning as a major computational vehicle for advanced model building of radiomics-based signatures or classifiers, and diverse clinical applications, working principles, research opportunities, and available computational platforms for radiomics will be reviewed with examples drawn primarily from oncology. We also address issues related to common applications in medical physics, such as standardization, feature extraction, model building, and validation.

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.

Electron radiotherapy (IOERT) for applications outside of the breast: Dosimetry and influence of tissue inhomogeneities.

PURPOSE: The purpose of study is to investigate the dosimetry of electron intraoperative radiotherapy (IOERT) of the Intraop Mobetron 2000 mobile LINAC in treatments outside of the breast. After commissioning and external validation of dosimetry, we report in vivo results of measurements for treatments outside the breast in a large patient cohort, and investigate if the presence of inhomogeneities can affect in vivo measurements. METHODS AND MATERIALS: Applicator factors and profile curves were measured with a stereotactic diode. The applicators factors of the 6 cm flat and beveled applicators were also confirmed with radiochromic films, parallel-plate ion chamber and by an external audit performed with ThermoLuminescent Dosimeters (TLDs). The influence of bone on dose was investigated by using radiochromic films attached to an insert equivalent to cortical bone, immersed in the water phantom. In vivo dosimetry was performed on 126 patients treated with IOERT using metal oxide-silicon semiconductor field effect transistors (MOSFETs) placed on the tumor bed. RESULTS: Relatively small differences were found among different detectors for measurements of applicator factors. In the external audit, the agreement with the TLD was mostly within ±0.2%. The largest increase of dose due to the presence of cortical bone insert was +6.0% with energy 12 MeV and 3 cm applicator. On average, in vivo dose was significantly (+3.1%) larger than prescribed dose. CONCLUSION: IOERT in applications outside the breast results in low discrepancies between in vivo and prescribed doses, which can be also explained with the presence of tissue inhomogeneity.

Prediction of skin dose in low-kV intraoperative radiotherapy using machine learning models trained on results of in vivo dosimetry.

PURPOSE: The purpose of this study was to implement a machine learning model to predict skin dose from targeted intraoperative (TARGIT) treatment resulting in timely adoption of strategies to limit excessive skin dose. METHODS: A total of 283 patients affected by invasive breast carcinoma underwent TARGIT with a prescribed dose of 6 Gy at 1 cm, after lumpectomy. Radiochromic films were used to measure the dose to the skin for each patient. Univariate statistical analysis was performed to identify correlation of physical and patient variables with measured dose. After feature selection of predictors of in vivo skin dose, machine learning models stepwise linear regression (SLR), support vector regression (SVR), ensemble with bagging or boosting, and feed forward neural networks were trained on results of in vivo dosimetry to derive models to predict skin dose. Models were evaluated by tenfold cross validation and ranked according to root mean square error (RMSE) and adjusted correlation coefficient of true vs predicted values (adj-R2 ). RESULTS: The predictors correlated with in vivo dosimetry were the distance of skin from source, depth-dose in water at depth of the applicator in the breast, use of a replacement source, and irradiation time. The best performing model was SVR, which scored RMSE and adj-R2 , equal to 0.746 [95% confidence intervals (CI), 95% CI 0.737,0.756] and 0.481 (95% CI 0.468,0.494), respectively, on the tenfold cross validation. CONCLUSION: The model trained on results of in vivo dosimetry can be used to predict skin dose during setup of patient for TARGIT and this allows for timely adoption of strategies to prevent of excessive skin dose.

3T MRI evaluation of the accuracy of atlas-based subthalamic nucleus identification.

Modulation of the activity of the subthalamic nucleus (STN) using deep brain stimulation (DBS) in patients with advanced Parkinson's disease is the most common procedure performed today by functional neurosurgeons. The STN contours cannot be entirely identified on common 1.5 T images; in particular, the ventromedial border of the STN often blends with the substantia nigra. 3 T magnetic resonance imaging (MRI) provides better resolution and can improve the identification of the STN borders. In this work, we have directly identified the STN using 3 T MR imaging to validate the accuracy of a computer-aided atlas-based procedure for automatic STN identification. Coordinates of the STN were obtained from the Talairach and Tournoux atlas and transformed into the coordinates of the Montreal Neurological Institute (MNI) standard brain volume, creating a mask representation of the STN. 3 T volumetric T1 and T2 weighted (T1w and T2w, respectively) acquisitions were obtained for ten patients. The MNI standard brain volume was registered onto each patient MRI, using a new approach based on global affine, region-of-interest affine, and local nonrigid registrations. The estimated deformation field was then applied to the STN atlas-based mask, providing its location on the patient MRI. The entire procedure required on average about 20 min. Because STN is easily identifiable on 3 T T2w-MRIs, it was manually delineated; the coordinates of the center of mass of the manually and automatically identified structures were compared. Additionally, volumetric overlapping indices were calculated and the spatial relationship between the midcommissural point and the STN center of mass was investigated. All indices indicated, on average, good agreement between manually and automatically identified structures; displacement of the centers of mass of the manually and automatically identified structures was less than or equal to 2.35 mm, and more than 80% of the manually identified volume was covered by the automatic localization, on average. Bland-Altman analysis indicated that the automatic STN identification was within the limits of agreement with the manual localization on 3 T MRIs. Automatic atlas-based STN localization provides an accurate and user-friendly tool and can enhance target identification when 1.5 T scanners with limited capability to identify the STN boundaries are used.

Paramagnetic liposomes as innovative contrast agents for magnetic resonance (MR) molecular imaging applications.

This article illustrates some innovative applications of liposomes loaded with paramagnetic lanthanide-based complexes in MR molecular imaging field. When a relatively high amount of a Gd(III) chelate is encapsulated in the vesicle, the nanosystem can simultaneously affect both the longitudinal (R(1)) and the transverse (R(2)) relaxation rate of the bulk H2O H-atoms, and this finding can be exploited to design improved thermosensitive liposomes whose MRI response is not longer dependent on the concentration of the probe. The observation that the liposome compartmentalization of a paramagnetic Ln(III) complex induce a significant R(2) enhancement, primarily caused by magnetic susceptibility effects, prompted us to test the potential of such agents in cell-targeting MR experiments. The results obtained indicated that these nanoprobes may have a great potential for the MR visualization of cellular targets (like the glutamine membrane transporters) overexpressing in tumor cells. Liposomes loaded with paramagnetic complexes acting as NMR shift reagents have been recently proposed as highly sensitive CEST MRI agents. The main peculiarity of CEST probes is to allow the MR visualization of different agents present in the same region of interest, and this article provides an illustrative example of the in vivo potential of liposome-based CEST agents.

BOLD fMRI integration into radiosurgery treatment planning of cerebral vascular malformations.

Functional magnetic resonance imaging (fMRI) is used to distinguish areas of the brain responsible for different tasks and functions. It is possible, for example, by using fMRI images, to identify particular regions in the brain which can be considered as "functional organs at risk" (fOARs), i.e., regions which would cause significant patient morbidity if compromised. The aim of this study is to propose and validate a method to exploit functional information for the identification of fOARs in CyberKnife (Accuray, Inc., Sunnyvale, CA) radiosurgery treatment planning; in particular, given the high spatial accuracy offered by the CyberKnife system, local nonrigid registration is used to reach accurate image matching. Five patients affected by arteriovenous malformations (AVMs) and scheduled to undergo radiosurgery were scanned prior to treatment using computed tomography (CT), three-dimensional (3D) rotational angiography (3DRA), T2 weighted and blood oxygenation level dependent echo planar imaging MRI. Tasks were chosen on the basis of lesion location by considering those areas which could be potentially close to treatment targets. Functional data were superimposed on 3DRA and CT used for treatment planning. The procedure for the localization of fMRI areas was validated by direct cortical stimulation on 38 AVM and tumor patients undergoing conventional surgery. Treatment plans studied with and without considering fOARs were significantly different, in particular with respect to both maximum dose and dose volume histograms; consideration of the fOARs allowed quality indices of treatment plans to remain almost constant or to improve in four out of five cases compared to plans with no consideration of fOARs. In conclusion, the presented method provides an accurate tool for the integration of functional information into AVM radiosurgery, which might help to minimize undesirable side effects and to make radiosurgery less invasive.

Direct validation of atlas-based red nucleus identification for functional radiosurgery.

Treatment targets in functional neurosurgery usually consist of selected structures within the thalamus and basal ganglia, which can be stimulated in order to affect specific brain pathways. Chronic electrical stimulation of these structures is a widely used approach for selected patients with advanced movement disorders. An alternative therapeutic solution consists of producing a lesion in the target nucleus, for example by means of radiosurgery, a noninvasive procedure, and this prevents the use of intraoperative microelectrode recording as a method for accurate target definition. The need to have accurate noninvasive localization of the target motivated our previous work on atlas-based identification; the aim of this present work is to provide additional validation of this approach based on the identification of the red nuclei (RN), which are located near the subthalamic nucleus (STN). Coordinates of RN were obtained from the Talairach and Tournoux (TT) atlas and transformed into the coordinates of the Montreal Neurological Institute (MNI) atlas, creating a mask representation of RN. The MNI atlas volume was nonrigidly registered onto the patient magnetic resonance imaging (MRI). This deformation field was then applied to the RN mask, providing its location on the patient MRI. Because RN are easily identifiable on 1.5 T T2-MRI images, they were manually delineated; the coordinates of the centers of mass of the manually and automatically identified structures were compared. Additionally, volumetric overlapping indices were calculated. Ten patients were examined by this technique. All indices indicated a high level of agreement between manually and automatically identified structures. These results not only confirm the accuracy of the method but also allow fine tuning of the automatic identification method to be performed.

Beyond imaging: The promise of radiomics.

The domain of investigation of radiomics consists of large-scale radiological image analysis and association with biological or clinical endpoints. The purpose of the present study is to provide a recent update on the status of this rapidly emerging field by performing a systematic review of the literature on radiomics, with a primary focus on oncologic applications. The systematic literature search, performed in Pubmed using the keywords: "radiomics OR radiomic" provided 97 research papers. Based on the results of this search, we describe the methods used for building a model of prognostic value from quantitative analysis of patient images. Then, we provide an up-to-date overview of the results achieved in this field, and discuss the current challenges and future developments of radiomics for oncology.

Voxel-by-voxel correlation between radiologically radiation induced lung injury and dose after image-guided, intensity modulated radiotherapy for lung tumors.

PURPOSE: To correlate radiation dose to the risk of severe radiologically-evident radiation-induced lung injury (RRLI) using voxel-by-voxel analysis of the follow-up computed tomography (CT) of patients treated for lung cancer with hypofractionated helical Tomotherapy. METHODS AND MATERIALS: The follow-up CT scans from 32 lung cancer patients treated with various regimens (5, 8, and 25 fractions) were registered to pre-treatment CT using deformable image registration (DIR). The change in density was calculated for each voxel within the combined lungs minus the planning target volume (PTV). Parameters of a Probit formula were derived by fitting the occurrences of changes of density in voxels greater than 0.361gcm-3 to the radiation dose. The model's predictive capability was assessed using the area under receiver operating characteristic curve (AUC), the Kolmogorov-Smirnov test for goodness-of-fit, and the permutation test (Ptest). RESULTS: The best-fit parameters for prediction of RRLI 6months post RT were D50 of 73.0 (95% CI 59.2.4-85.3.7)Gy, and m of 0.41 (0.39-0.46) for hypofractionated (5 and 8 fractions) and D50 of 96.8 (76.9-123.9)Gy, and m of 0.36 (0.34-0.39) for 25 fractions RT. According to the goodness-of-fit test the null hypothesis of modeled and observed occurrence of RRLI coming from the same distribution could not be rejected. The AUC was 0.581 (0.575-0.583) for fractionated and 0.579 (0.577-0.581) for hypofractionated patients. The predictive models had AUC>upper 95% band of the Ptest. CONCLUSIONS: The correlation of voxel-by-voxel density increase with dose can be used as a support tool for differential diagnosis of tumor from benign changes in the follow-up of lung IMRT patients.

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.