The prediction of liver Dmean with 3-dimensional radiation treatment planning (3DRTP) is time consuming in the selection of proton beam therapy (PBT), and deep learning prediction generally requires ...large and tumor-specific databases. We developed a simple dose prediction tool (SDP) using deep learning and a novel contour-based data augmentation (CDA) approach and assessed its usability. We trained the SDP to predict the liver Dmean immediately. Five and two computed tomography (CT) data sets of actual patients with liver cancer were used for the training and validation. Data augmentation was performed by artificially embedding 199 contours of virtual clinical target volume (CTV) into CT images for each patient. The data sets of the CTVs and OARs are labeled with liver Dmean for six different treatment plans using two-dimensional calculations assuming all tissue densities as 1.0. The test of the validated model was performed using 10 unlabeled CT data sets of actual patients. Contouring only of the liver and CTV was required as input. The mean relative error (MRE), the mean percentage error (MPE) and regression coefficient between the planned and predicted Dmean was 0.1637, 6.6%, and 0.9455, respectively. The mean time required for the inference of liver Dmean of the six different treatment plans for a patient was 4.47±0.13 seconds. We conclude that the SDP is cost-effective and usable for gross estimation of liver Dmean in the clinic although the accuracy should be improved further if we need the accuracy of liver Dmean to be compatible with 3DRTP.
Background
It is important to have precise image guidance throughout proton therapy in order to take advantage of the therapy's physical selectivity.
Purpose
We evaluated the effectiveness of ...computed tomography (CT)‐image guidance in proton therapy for patients with hepatocellular carcinoma (HCC) by assessing daily proton dose distributions. The importance of daily CT image‐guided registration and daily proton dose monitoring for tumors and organs at risk (OARs) was investigated.
Methods
A retrospective analysis was conducted using 570 sets of daily CT (dCT) images throughout whole treatment fractions for 38 HCC patients who underwent passive scattering proton therapy with either a 66 cobalt gray equivalent (GyE)/10 fractions (n = 19) or 76 GyE/20 fractions (n = 19) protocol. The actual daily delivered dose distributions were estimated by forward calculation using the dCT sets, their corresponding treatment plans, and the recorded daily couch correction information. We then evaluated the daily changes of the dose indices D99%, V30GyE, and Dmax for the tumor volumes, non‐tumorous liver, and other OARs, that is, stomach, esophagus, duodenum, colon, respectively. Contours were created for all dCT sets. We validated the efficacy of the dCT‐based tumor registrations (hereafter, “tumor registration”) by comparing them with the bone registration and diaphragm registration as a simulation of the treatment based on the positioning using the conventional kV X‐ray imaging. The dose distributions and the indices of three registrations were obtained by simulation using the same dCT sets.
Results
In the 66 GyE/10 fractions, the daily D99% value in both the tumor and diaphragm registrations agreed with the planned value with 3%–6% (SD), and the V30GyE value for the liver agreed within ±3%; the indices in the bone registration showed greater deterioration. Nevertheless, tumor‐dose deterioration occurred in all registration methods for two cases due to daily changes of body shape and respiratory condition. In the 76 GyE/20 fractions, in particular for such a treatment that the dose constraints for the OARs have to be cared in the original planning, the daily D99% in the tumor registration was superior to that in the other registration (p < 0.001), indicating the effectiveness of the tumor registration. The dose constraints, set in the plan as the maximum dose for OARs (i.e., duodenum, stomach, colon, and esophagus) were maintained for 16 patients including seven treated with re‐planning. For three patients, the daily Dmax increased gradually or changed randomly, resulting in an inter‐fractional averaged Dmax higher than the constraints. The dose distribution would have been improved if re‐planning had been conducted. The results of these retrospective analyses indicate the importance of daily dose monitoring followed by adaptive re‐planning when needed.
Conclusions
The tumor registration in proton treatment for HCC was effective to maintain the daily dose to the tumor and the dose constraints of OARs, particularly in the treatment where the maintenance for the dose constraints needs to be considered throughout the treatment. Nevertheless daily proton dose monitoring with daily CT imaging is important for more reliable and safer treatment.
The aim of this study was to develop a normal tissue complication probability model using a machine learning approach (ML-based NTCP) to predict the risk of radiation-induced liver disease in ...hepatocellular carcinoma (HCC) patients.
The study population included 201 HCC patients treated with radiotherapy. The patients' medical records were retrospectively reviewed to obtain the clinical and radiotherapy data. Toxicity was defined by albumin-bilirubin (ALBI) grade increase. The normal liver dose-volume histogram was reduced to mean liver dose (MLD) based on the fraction size-adjusted equivalent uniform dose (2 Gy/fraction and α/β = 2). Three types of ML-based classification models were used, a penalized logistic regression (PLR), random forest (RF), and gradient-boosted tree (GBT) model. Model performance was compared using the area under the receiver operating characteristic curve (AUROC). Internal validation was performed by 5-fold cross validation and external validation was done in 44 new patients.
Liver toxicity occurred in 87 patients (43.1%). The best individual model was the GBT model using baseline liver function, liver volume, and MLD as inputs and the best overall model was an ensemble of the PLR and GBT models. An AUROC of 0.82 with a standard deviation of 0.06 was achieved for the internal validation. An AUROC of 0.78 with a standard deviation of 0.03 was achieved for the external validation. The behaviors of the best GBT model were also in good agreement with the domain knowledge on NTCP.
We propose the methodology to develop an ML-based NTCP model to estimate the risk of ALBI grade increase.
•Normal tissue complication probability (NTCP) model can guide treatment selection.•NTCP for radiation-induced liver toxicity (RILT) has been developed.•RILT risk varied among different Child–Pugh ...and viral hepatitis infection status.•Estimated NTCP model and ΔNTCP for RILT are specific to the patient subgroups.•ΔNTCP with uncertainty in individualized subgroups is helpful to select treatment.
To predict the probability of radiation-induced liver toxicity (RILT) and implement the normal tissue complication probability (NTCP) model-based approach considering confidence intervals (CIs) to select patients for new treatment techniques, such as proton beam therapy, based on a certain NTCP reduction (ΔNTCP) threshold for primary liver cancer patients.
Common Toxicity Criteria for Adverse Events (CTCAE) grade ≥2 RILT was scored. The Lyman NTCP models predicting the probability of CTCAE grade ≥2 RILT as a function of the fraction-size adjusted mean liver dose (MLD), using reference fraction size = 2 Gy/fraction and α/β ratio = 2 Gy, were fitted using the maximum likelihood method. At certain combinations of MLDs, ΔNTCP with a CI was evaluated by the delta method.
Of the 239 patients, the incidence of CTCAE grade ≥2 RILT was 55% (46% in the Child–Pugh (CP)-A vs. 81% in the CP-B/C, p < 0.001). Among 180 CP-A patients, 40% who had viral hepatitis infections experienced toxicity vs. 32% in the nonhepatitis subgroup. The MLD was 18 Gy in the toxicity group vs. 16.1 Gy in the nontoxicity group (p = 0.002). The estimated NTCP model parameters specific to the patient subgroups and the ΔNTCP with CI assuming a particular CP classification and viral hepatitis infection status were considerably different which possible changed treatment decision.
Patients with CP-A and viral hepatitis infection or CP-B/C cirrhosis had greater susceptibility to CTCAE grade ≥2 RILT. The estimated NTCP and ΔNTCP for individual patients along with a consideration of uncertainties improve the reliability of the NTCP model-based approach.
Pharyngeal cancer patients treated with intensity-modulated proton therapy (IMPT) using a model-based approach were retrospectively reviewed, and acute toxicities were analyzed. From June 2016 to ...March 2019, 15 pharyngeal (7 naso-, 5 oro- and 3 hypo-pharyngeal) cancer patients received IMPT with robust optimization. Simulation plans for IMPT and intensity-modulated X-ray therapy (IMXT) were generated before treatment. We also reviewed 127 pharyngeal cancer patients with IMXT in the same treatment period. In the simulation planning comparison, all of the normal-tissue complication probability values for dysphagia, dysgeusia, tube-feeding dependence and xerostomia were lower for IMPT than for IMXT in the 15 patients. After completing IMPT, 13 patients completed the evaluation, and 12 of these patients had a complete response. The proportions of patients who experienced grade 2 or worse acute toxicities in the IMPT and IMXT cohorts were 21.4 and 56.5% for dysphagia (P < 0.05), 46.7 and 76.3% for dysgeusia (P < 0.05), 73.3 and 62.8% for xerostomia (P = 0.43), 73.3 and 90.6% for mucositis (P = 0.08) and 66.7 and 76.4% for dermatitis (P = 0.42), respectively. Multivariate analysis revealed that IMPT was independently associated with a lower rate of grade 2 or worse dysphagia and dysgeusia. After propensity score matching, 12 pairs of IMPT and IMXT patients were selected. Dysphagia was also statistically lower in IMPT than in IMXT (P < 0.05). IMPT using a model-based approach may have clinical benefits for acute dysphagia.
Abstract
Physically precise external-beam radiotherapy (EBRT) technologies may not translate to the best outcome in individual patients. On the other hand, clinical considerations alone are often ...insufficient to guide the selection of a specific EBRT approach in patients. We examine the ways in which to compare different EBRT approaches based on physical, biological and clinical considerations, and how they can be enhanced with the addition of biophysical models and machine-learning strategies. The process of selecting an EBRT modality is expected to improve in tandem with knowledge-based treatment planning.
•The short-term GI-tract motion was assessed in eleven pancreatic cancer patients.•Dose uncertainties were also evaluated with SBRT of 40 Gy in 5 fractions.•The necessary margin was at least 8 mm to ...compensate for the organ motion.•The short-term motion could lead to unexpectedly high doses in parts of the GI-tract.•The results of this study have important implications for intra-fractional motion.
The aim of this study is to quantify the short-term motion of the gastrointestinal tract (GI-tract) and its impact on dosimetric parameters in stereotactic body radiation therapy (SBRT) for pancreatic cancer.
The analyzed patients were eleven pancreatic cancer patients treated with SBRT or proton beam therapy. To ensure a fair analysis, the simulation SBRT plan was generated on the planning CT in all patients with the dose prescription of 40 Gy in 5 fractions. The GI-tract motion (stomach, duodenum, small and large intestine) was evaluated using three CT images scanned at spontaneous expiration. After fiducial-based rigid image registration, the contours in each CT image were generated and transferred to the planning CT, then the organ motion was evaluated. Planning at risk volumes (PRV) of each GI-tract were generated by adding 5 mm margins, and the volume receiving at least 33 Gy (V33) < 0.5 cm3 was evaluated as the dose constraint.
The median interval between the first and last CT scans was 736 s (interquartile range, IQR:624–986). To compensate for the GI-tract motion based on the planning CT, the necessary median margin was 8.0 mm (IQR: 8.0–10.0) for the duodenum and 14.0 mm (12.0–16.0) for the small intestine. Compared to the planned V33 with the worst case, the median V33 in the PRV of the duodenum significantly increased from 0.20 cm3 (IQR: 0.02–0.26) to 0.33 cm3 (0.10–0.59) at Wilcoxon signed-rank test (p = 0.031).
The short-term motions of the GI-tract lead to high dose differences.
Purpose
To evaluate the dosimetric advantages of daily adaptive radiotherapy (DART) in intensity‐modulated proton therapy (IMPT) for high‐risk prostate cancer by comparing estimated doses of the ...conventional non‐adaptive radiotherapy (NART) that irradiates according to an original treatment plan through the entire treatment and the DART that uses an adaptive treatment plan generated by using daily CT images acquired before each treatment.
Methods
Twenty‐three patients with prostate cancer were included. A treatment plan with 63 Gy (relative biological effectiveness (RBE)) in 21 fractions was generated using treatment planning computed tomography (CT) images assuming that all patients had high‐risk prostate cancer for which the clinical target volume (CTV) needs to include prostate and the seminal vesicle (SV) in our treatment protocol. Twenty‐one adaptive treatment plans for each patient (total 483 data sets) were generated using daily CT images, and dose distributions were calculated. Using a 3 mm set‐up uncertainty in the robust optimization, the doses to the CTV, prostate, SV, rectum, and bladder were compared.
Results
Estimated accumulated doses of NART and DART in the 23 patients were 60.81 ± 3.47 Gy (RBE) and 63.24 ± 1.04 Gy (RBE) for CTV D99 (p < 0.01), 62.99 ± 1.28 Gy (RBE) and 63.43 ± 1.33 Gy (RBE) for the prostate D99 (p = 0.2529), and 59.07 ± 5.19 Gy (RBE) and 63.17 ± 1.04 Gy (RBE) for SV D99 (p < 0.001). No significant differences were observed between NART and DART in the estimated accumulated dose for the rectum and bladder.
Conclusion
Compared with the NART, DART was shown to be a useful approach that can maintain the dose coverage to the target without increasing the dose to the organs at risk (OAR) using the 3 mm set‐up uncertainty in the robust optimization in patients with high‐risk prostate cancer.
We developed a confidence interval-(CI) assessing model in multivariable normal tissue complication probability (NTCP) modeling for predicting radiation-induced liver disease (RILD) in primary liver ...cancer patients using clinical and dosimetric data. Both the mean NTCP and difference in the mean NTCP (ΔNTCP) between two treatment plans of different radiotherapy modalities were further evaluated and their CIs were assessed. Clinical data were retrospectively reviewed in 322 patients with hepatocellular carcinoma (n = 215) and intrahepatic cholangiocarcinoma (n = 107) treated with photon therapy. Dose-volume histograms of normal liver were reduced to mean liver dose (MLD) based on the fraction size-adjusted equivalent uniform dose. The most predictive variables were used to build the model based on multivariable logistic regression analysis with bootstrapping. Internal validation was performed using the cross-validation leave-one-out method. Both the mean NTCP and the mean ΔNTCP with 95% CIs were calculated from computationally generated multivariate random sets of NTCP model parameters using variance-covariance matrix information. RILD occurred in 108/322 patients (33.5%). The NTCP model with three clinical and one dosimetric parameter (tumor type, Child-Pugh class, hepatitis infection status and MLD) was most predictive, with an area under the receiver operative characteristics curve (AUC) of 0.79 (95% CI 0.74-0.84). In eight clinical subgroups based on the three clinical parameters, both the mean NTCP and the mean ΔNTCP with 95% CIs were able to be estimated computationally. The multivariable NTCP model with the assessment of 95% CIs has potential to improve the reliability of the NTCP model-based approach to select the appropriate radiotherapy modality for each patient.
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