Cone beam CT-based adaptive intensity modulated proton therapy assessment using automated planning for head-and-neck cancer.

BACKGROUND: To assess the feasibility of CBCT-based adaptive intensity modulated proton therapy (IMPT) using automated planning for treatment of head and neck (HN) cancers. METHODS: Twenty HN cancer patients who received radiotherapy and had pretreatment CBCTs were included in this study. Initial IMPT plans were created using automated planning software for all patients. Synthetic CTs (sCT) were then created by deforming the planning CT (pCT) to the pretreatment CBCTs. To assess dose calculation accuracy on sCTs, repeat CTs (rCTs) were deformed to the pretreatment CBCT obtained on the same day to create deformed rCT (rCTdef), serving as gold standard. The dose recalculated on sCT and on rCTdef were compared by using Gamma analysis. The accuracy of DIR generated contours was also assessed. To explore the potential benefits of adaptive IMPT, two sets of plans were created for each patient, a non-adapted IMPT plan and an adapted IMPT plan calculated on weekly sCT images. The weekly doses for non-adaptive and adaptive IMPT plans were accumulated on the pCT, and the accumulated dosimetric parameters of two sets were compared. RESULTS: Gamma analysis of the dose recalculated on sCT and rCTdef resulted in a passing rate of 97.9% ± 1.7% using 3 mm/3% criteria. With the physician-corrected contours on the sCT, the dose deviation range of using sCT to estimate mean dose for the most organ at risk (OARs) can be reduced to (- 2.37%, 2.19%) as compared to rCTdef, while for V95 of primary or secondary CTVs, the deviation can be controlled within (- 1.09%, 0.29%). Comparison of the accumulated doses from the adaptive planning against the non-adaptive plans reduced mean dose to constrictors (- 1.42 Gy ± 2.79 Gy) and larynx (- 2.58 Gy ± 3.09 Gy). The reductions result in statistically significant reductions in the normal tissue complication probability (NTCP) of larynx edema by 7.52% ± 13.59%. 4.5% of primary CTVs, 4.1% of secondary CTVs, and 26.8% tertiary CTVs didn't meet the V95 > 95% constraint on non-adapted IMPT plans. All adaptive plans were able to meet the coverage constraint. CONCLUSION: sCTs can be a useful tool for accurate proton dose calculation. Adaptive IMPT resulted in better CTV coverage, OAR sparing and lower NTCP for some OARs as compared with non-adaptive IMPT.

MRI-based delta-radiomic features for prediction of local control in liver lesions treated with stereotactic body radiation therapy.

Real-time magnetic resonance image guided stereotactic ablative radiotherapy (MRgSBRT) is used to treat abdominal tumors. Longitudinal data is generated from daily setup images. Our study aimed to identify delta radiomic texture features extracted from these images to predict for local control in patients with liver tumors treated with MRgSBRT. Retrospective analysis of an IRB-approved database identified patients treated with MRgSBRT for primary liver and secondary metastasis histologies. Daily low field strength (0.35 T) images were retrieved, and the gross tumor volume was identified on each image. Next, images' gray levels were equalized, and 39 s-order texture features were extracted. Delta-radiomics were calculated as the difference between feature values on the initial scan and after delivered biological effective doses (BED, α/ß = 10) of 20 Gy and 40 Gy. Then, features were ranked by the Gini Index during training of a random forest model. Finally, the area under the receiver operating characteristic curve (AUC) was estimated using a bootstrapped logistic regression with the top two features. We identified 22 patients for analysis. The median dose delivered was 50 Gy in 5 fractions. The top two features identified after delivery of BED 20 Gy were gray level co-occurrence matrix features energy and gray level size zone matrix based large zone emphasis. The model generated an AUC = 0.9011 (0.752-1.0) during bootstrapped logistic regression. The same two features were selected after delivery of a BED 40 Gy, with an AUC = 0.716 (0.600-0.786). Delta-radiomic features after a single fraction of SBRT predicted local control in this exploratory cohort. If confirmed in larger studies, these features may identify patients with radioresistant disease and provide an opportunity for physicians to alter management much sooner than standard restaging after 3 months. Expansion of the patient database is warranted for further analysis of delta-radiomic features.

Predictive Value of Delta-Radiomics Texture Features in 0.35 Tesla Magnetic Resonance Setup Images Acquired During Stereotactic Ablative Radiotherapy of Pancreatic Cancer.

Purpose: The purpose of this work is to explore delta-radiomics texture features for predicting response using setup images of pancreatic cancer patients treated with magnetic resonance image guided (MRI-guided) stereotactic ablative radiotherapy (SBRT). Methods: The total biological effective dose (BED) was calculated for 30 patients treated with MRI-guided SBRT that delivered physical doses of 30-60 Gy in three to five fractions. Texture features were then binned into groups based upon BED per fraction by dividing BED by the number of fractions. Delta-radiomics texture features were calculated after delivery of 20 Gy BED (BED20 features) and 40 Gy BED (BED40 features). A random forest (RF) model was constructed using BED20 and then BED40 features to predict binary outcome. During model training, the Gini Index, a measure of a variable's importance for accurate prediction, was calculated for all features, and the two features that ranked the highest were selected for internal validation. The two features selected from each bin were used in a bootstrapped logistic regression model to predict response and performance quantified using the area under the receiver operating characteristic curve (AUC). This process was an internal validation analysis. Results: After RF model training, the Gini Index was highest for gray-level co-occurrence matrix-based (GLCM) sum average, and neighborhood gray tone difference matrix-based (NGTDM) busyness for BED20 features and gray-level size zone matrix-based (GLSZM) large zones low gray-level emphasis and gray-level run length matrix-based (GLRLM) run percentage was selected from the BED40-based features. The mean AUC obtained using the two BED20 features was AUC = 0.845 with the 2.5 percentile and 97.5 percentile values ranging from 0.794 to 0.856. Internal validation of the BED40 delta-radiomics features resulted in a mean AUC = 0.567 with a 2.5 and 97.5 percentile range of 0.502-0.675. Conclusion: Early changes in treatment quantified with the BED20 delta-radiomics texture features in low field images acquired during MRI-guided SBRT demonstrated better performance in internal validation than features calculated later in treatment. Further analysis of delta-radiomics texture analysis in low field MRI is warranted.

Impact of quantization algorithm and number of gray level intensities on variability and repeatability of low field strength magnetic resonance image-based radiomics texture features.

PURPOSE: The purpose of this work was to investigate the impact of quantization preprocessing parameter selection on variability and repeatability of texture features derived from low field strength magnetic resonance (MR) images. METHODS: Texture features were extracted from low field strength images of a daily image QA phantom with four texture inserts. Feature variability over time was quantified using all combinations of three quantization algorithms and four different numbers of gray level intensities. In addition, texture features were extracted using the same combinations from the low field strength MR images of the gross tumor volume (GTV) and left kidney of patients with repeated set up scans. The impact of region of interest (ROI) preprocessing on repeatability was investigated with a test-retest study design. RESULTS: The phantom ROIs quantized to 64 Gy level intensities using the histogram equalization method resulted in the greatest number of features with the least variability. There was no clear method that resulted in the highest repeatability in the GTV or left kidney. However, eight texture features extracted from the GTV were repeatable regardless of ROI processing combination. CONCLUSION: Low field strength MR images can provide a stable basis for texture analysis with ROIs quantized to 64 Gy levels using histogram equalization, but there is no clear optimal combination for repeatability.

Assessment of online adaptive MR-guided stereotactic body radiotherapy of liver cancers.

PURPOSE/OBJECTIVE: Online Adaptive Radiotherapy (ART) with daily MR-imaging has the potential to improve dosimetric accuracy by accounting for inter-fractional anatomical changes. This study provides an assessment for the feasibility and potential benefits of online adaptive MRI-Guided Stereotactic Body Radiotherapy (SBRT) for treatment of liver cancer. MATERIALS/METHODS: Ten patients with liver cancer treated with MR-Guided SBRT were included. Prescription doses ranged between 27 and 50 Gy in 3-5 fx. All SBRT fractions employed daily MR-guided setup while utilizing cine-MR gating. Organs-at-risk (OARs) included duodenum, bowel, stomach, kidneys and spinal cord. Daily MRIs and contours were utilized to create each adapted plan. Adapted plans used the beam-parameters and optimization-objectives from the initial plan. Planning target volume (PTV) coverage and OAR constraints were used to compare non-adaptive and adaptive plans. RESULTS: PTV coverage for non-adapted treatment plans was below the prescribed coverage for 32/47 fractions (68%), with 11 fractions failing by more than 10%. All 47 adapted fractions met prescribed coverage. OAR constraint violations were also compared for several organs. The duodenum exceeded tolerance for 5/23 non-adapted and 0/23 for adapted fractions. The bowel exceeded tolerance for 5/34 non-adaptive and 1/34 adaptive fractions. The stomach exceeded tolerance for 4/19 non-adapted and 1/19 for adaptive fractions. Accumulated dose volume histograms were also generated for each patient. CONCLUSION: Online adaptive MR-Guided SBRT of liver cancer using daily re-optimization resulted in better target conformality, coverage and OAR sparing compared with non-adaptive SBRT. Daily adaptive planning may allow for PTV dose escalation without compromising OAR sparing.

Predictive value of 0.35 T magnetic resonance imaging radiomic features in stereotactic ablative body radiotherapy of pancreatic cancer: A pilot study.

PURPOSE: The aim of this study was to evaluate the potential and feasibility of radiomic features extracted from low field strength (0.35 T) magnetic resonance images (MRIs) in predicting treatment response for patients with pancreatic cancer undergoing stereotactic body radiotherapy (SBRT). METHODS: Twenty patients with unresected, non-metastatic pancreatic ductal adenocarcinoma (PDAC) were enrolled, all of whom received neoadjuvant chemotherapy followed by five-fraction MR-guided SBRT with a radiation dose range of 33-50 Gy. For each patient, five daily setup scans were acquired from a hybrid 0.35 T MRI/radiotherapy unit. Tumor heterogeneity quantified with radiomic features extracted from the gross tumor volume (GTV) was averaged over the course of treatment. Random forest (RF) and adaptive least absolute shrinkage and selection operator (LASSO) classification models were constructed to identify radiomics features predictive of treatment response. Predictive capability of the top-performing features was then evaluated using the receiver operating characteristic area under curve (AUC) obtained using leave-one-out cross-validation. RESULTS: Half of the 20 patients showed response to treatment, defined by tumor regression on histopathology or tumor response on follow-up dynamic contrast-enhanced computed tomography (CT). The most predictive features selected by the RF method were GLCM energy and GLSZM gray-level variance. The RF-based model achieved an AUC = 0.81 with a 95% confidence interval of [0.594 to 1] The LASSO algorithm selected GLCM energy as the only predictive feature, achieving an AUC = 0.81 with 95% confidence interval of [0.596 to 1]. CONCLUSION: The findings of this study suggest that radiomic features extracted during MR-guided SBRT may contain predictive information about response of PDAC patients to treatment. Using the images acquired during treatment of PDAC patients supports continued expansion of radiomic analysis based on low field strength MR images and may hold the potential for providing timely indications of response to treatment.

Assessment of specific versus combined purpose knowledge based models in prostate radiotherapy.

Knowledge-based planning (KBP) can be used to improve plan quality, planning speed, and reduce the inter-patient plan variability. KPB may also identify and reduce systematic variations in VMAT plans, something very important in multi-institutional clinical trials. Training of a KBP library is a complex and difficult process, and models must be validated prior to their clinical use. The purpose of this work is to assess the quality of the treatment plans generated using a specific versus combined purpose model KBP library for prostate cancer. Seven KBP model libraries were created from a set of patients treated on various Institutional Review Board (IRB) approved protocols. All KBP libraries were validated using an independent set of twenty patients (half treated Pr: Prostate alone half treated PLN: prostate plus pelvic lymph nodes). Two models were tested on the Pr patients only, four tested on PLN patients only, and one tested on all patients. All plans were normalized such that at least 95% of the prostate planning target volume received 100% of the planned dose. The plans based on different model libraries were compared to each other and the expert clinical plan. For Pr plans there were almost no statistically significant differences (P < 0.008) between the plans types except conformity index (CI) with library plans better than the expert. For PLN plans, all model libraries in generally showed femur doses and CI better than the expert plans (P < 0.003). This study demonstrated that no large differences were observed between specific versus combined KBP model libraries in dosimetry of prostate cancer patients. This would allow for a fewer specific plans to be needed to create a model library. Further studies are needed to evaluate benefits of combined purpose model libraries for planning of complex sites such as head and neck cancer.

Magnetic Resonance-guided External Beam Radiation and Brachytherapy for a Patient with Intact Cervical Cancer.

Radiation treatment verification has improved significantly over the past decades. The field has moved from film X-rays and skin marks to fiducial tracking and daily cone beam computed tomography (CBCT) for tumor localization. We now have the ability to perform daily on-board magnetic resonance imaging (MRI), which provides superior soft tissue contrast compared to computed tomography (CT). In the management of cervical cancer, the brachytherapy literature has demonstrated that MRI allows for better delineation of the high-risk clinical target volume (HR-CTV) and the use of MRI-guided brachytherapy has translated into improved treatment outcomes. Consensus contouring guidelines for intensity modulated radiation therapy (IMRT) for cervical cancer advise including the whole uterus in the target volume and adding large planning target volume (PTV) margins to account for inter-fractional uterine motion and target motion resulting from variable rectal and bladder filling. MRI-guided radiation therapy (MRgRT) systems enable the possibility to precisely delineate the target volume on a daily basis and to perform truly adaptive delivery. This advancement in technology provides the opportunity to explore how external beam treatment volumes could be safely reduced for better sparing of pelvic organs for the benefit of our patients with cervical cancer. We describe the MR-guided definitive external beam radiation therapy and brachytherapy for a 32-year-old woman with intact cervical cancer. We contoured the uterus, bladder, rectum, and gross tumor volume (GTV) on each of her 25 set-up MRIs. We demonstrate a steady reduction in the GTV and increased displacement of the uterus and GTV as the GTV decreased in size. The findings presented suggest that cervical cancer could greatly benefit from an adaptive MRgRT approach.

Feasibility of Adaptive MR-guided Stereotactic Body Radiotherapy (SBRT) of Lung Tumors.

Online adaptive radiotherapy (ART) with frequent imaging has the potential to improve dosimetric accuracy by accounting for anatomical and functional changes during the course of radiotherapy. Presented are three interesting cases that provide an assessment of online adaptive magnetic resonance-guided radiotherapy (MRgRT) for lung stereotactic body radiotherapy (SBRT). The study includes three lung SBRT cases, treated on an MRgRT system where MR images were acquired for planning and prior to each treatment fraction. Prescription dose ranged from 48 to 50 Gy in four to five fractions, normalized to where 95% of the planning target volume (PTV) was covered by 100% of the prescription dose. The process begins with the gross tumor volume (GTV), PTV, spinal cord, lungs, heart, and esophagus being delineated on the planning MRI. The treatment plan was then generated using a step-and-shoot intensity modulated radiotherapy (IMRT) technique, which utilized a Monte Carlo dose calculation. Next, the target and organs at risk (OAR) contours from the planning MRI were deformably propagated to the daily setup MRIs. These deformed contours were reviewed and modified by the physician. To determine the efficacy of ART, two different strategies were explored: 1) Calculating the plan created for the planning MR on each fraction setup MR dataset (Non-Adapt) and 2) creating a new optimized IMRT plan on the fraction setup MR dataset (FxAdapt). The treatment plans from both strategies were compared using the clinical dose-volume constraints. PTV coverage constraints were not met for 33% Non-Adapt fractions; all FxAdapt fractions met this constraint. Eighty-eight percent of all OAR constraints studied were better on FxAdapt plans, while 12% of OAR constraints were superior on Non-Adapt fractions. The OAR that garnered the largest benefit would be the uninvolved lung, with superior sparing in 92% of the FxAdapt studied. Similar, but less pronounced, benefits from adaptive planning were experienced for the spinal cord, chest wall, and esophagus. Online adaptive MR-guided lung SBRT can provide better target conformality and homogeneity and OAR sparing compared with non-adaptive SBRT in selected cases. Conversely, if the PTV isn't adjacent to multiple OARs, then the benefit from ART may be limited. Further studies, which incorporate a larger cohort of patients with uniform prescriptions, are needed to thoroughly evaluate the benefits of daily online ART during MRgRT.