Advances in dose-volume/outcome (or normal tissue complication probability, NTCP) modeling since the seminal Emami paper from 1991 are reviewed. There has been some progress with an increasing number ...of studies on large patient samples with three-dimensional dosimetry. Nevertheless, NTCP models are not ideal. Issues related to the grading of side effects, selection of appropriate statistical methods, testing of internal and external model validity, and quantification of predictive power and statistical uncertainty, all limit the usefulness of much of the published literature. Synthesis (meta-analysis) of data from multiple studies is often impossible because of suboptimal primary analysis, insufficient reporting and variations in the models and predictors analyzed. Clinical limitations to the current knowledge base include the need for more data on the effect of patient-related cofactors, interactions between dose distribution and cytotoxic or molecular targeted agents, and the effect of dose fractions and overall treatment time in relation to nonuniform dose distributions. Research priorities for the next 5-10 years are proposed.
Radiation-associated liver injury Pan, Charlie C; Kavanagh, Brian D; Dawson, Laura A ...
International journal of radiation oncology, biology, physics,
03/2010, Volume:
76, Issue:
3 Suppl
Journal Article
Peer reviewed
Open access
The liver is a critically important organ that has numerous functions including the production of bile, metabolism of ingested nutrients, elimination of many waste products, glycogen storage, and ...plasma protein synthesis. The liver is often incidentally irradiated during radiation therapy (RT) for tumors in the upper- abdomen, right lower lung, distal esophagus, or during whole abdomen or whole body RT. This article describes the endpoints, time-course, and dose-volume effect of radiation on the liver.
The Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) review summarizes the currently available three-dimensional dose/volume/outcome data to update and refine the normal tissue ...dose/volume tolerance guidelines provided by the classic Emami et al. paper published in 1991. A "clinician's view" on using the QUANTEC information in a responsible manner is presented along with a description of the most commonly used normal tissue complication probability (NTCP) models. A summary of organ-specific dose/volume/outcome data, based on the QUANTEC reviews, is included.
To quantitatively evaluate published experiences with hepatic stereotactic body radiation therapy (SBRT), to determine local control rates after treatment of primary and metastatic liver tumors and ...to examine whether outcomes are affected by SBRT dosing regimen.
We identified published articles that reported local control rates after SBRT for primary or metastatic liver tumors. Biologically effective doses (BEDs) were calculated for each dosing regimen using the linear-quadratic equation. We excluded series in which a wide range of BEDs was used. Individual lesion data for local control were extracted from actuarial survival curves, and data were aggregated to form a single dataset. Actuarial local control curves were generated using the Kaplan-Meier method after grouping lesions by disease type and BED (<100 Gy
vs >100 Gy
). Comparisons were made using log-rank testing.
Thirteen articles met all inclusion criteria and formed the dataset for this analysis. The 1-, 2-, and 3-year actuarial local control rates after SBRT for primary liver tumors (n = 431) were 93%, 89%, and 86%, respectively. Lower 1- (90%), 2- (79%), and 3-year (76%) actuarial local control rates were observed for liver metastases (n = 290, log-rank P = .011). Among patients treated with SBRT for primary liver tumors, there was no evidence that local control is influenced by BED within the range of schedules used. For liver metastases, on the other hand, outcomes were significantly better for lesions treated with BEDs exceeding 100 Gy
(3-year local control 93%) than for those treated with BEDs of ≤100 Gy
(3-year local control 65%, P < .001).
Stereotactic body radiation therapy for primary liver tumors provides high rates of durable local control, with no clear evidence for a dose-response relationship among commonly utilized schedules. Excellent local control rates are also seen after SBRT for liver metastases when BEDs of >100 Gy
are utilized.
Assess dosimetric correlates of long-term dysphagia after chemo-intensity-modulated radiotherapy (IMRT) of oropharyngeal cancer (OPC) sparing parts of the swallowing organs.
Prospective longitudinal ...study: weekly chemotherapy concurrent with IMRT for Stages III/IV OPC, aiming to reduce dysphagia by sparing noninvolved parts of swallowing-related organs: pharyngeal constrictors (PC), glottic and supraglottic larynx (GSL), and esophagus, as well as oral cavity and major salivary glands. Dysphagia outcomes included patient-reported Swallowing and Eating Domain scores, Observer-based (CTCAEv.2) dysphagia, and videofluoroscopy (VF), before and periodically after therapy through 2 years. Relationships between dosimetric factors and worsening (from baseline) of dysphagia through 2 years were assessed by linear mixed-effects model.
Seventy-three patients participated. Observer-based dysphagia was not modeled because at >6 months there were only four Grade ≥2 cases (one of whom was feeding-tube dependent). PC, GSL, and esophagus mean doses, as well as their partial volume doses (V(D)s), were each significantly correlated with all dysphagia outcomes. However, the V(D)s for each organ intercorrelated and also highly correlated with the mean doses, leaving only mean doses significant. Mean doses to each of the parts of the PCs (superior, middle, and inferior) were also significantly correlated with all dysphagia measures, with superior PCs demonstrating highest correlations. For VF-based strictures, most significant predictor was esophageal mean doses (48±17 Gy in patients with, vs 27±12 in patients without strictures, p = 0.004). Normal tissue complication probabilities (NTCPs) increased moderately with mean doses without any threshold. For increased VF-based aspirations or worsened VF summary scores, toxic doses (TDs)(50) and TD(25) were 63 Gy and 56 Gy for PC, and 56 Gy and 39 Gy for GSL, respectively. For both PC and GSL, patient-reported swallowing TDs were substantially higher than VF-based TDs.
Swallowing organs mean doses correlated significantly with long-term worsening of swallowing. Different methods assessing dysphagia resulted in different NTCPs, and none demonstrated a threshold.
Subtle differences in a patient's genetics and physiology may alter radiotherapy (RT) treatment responses, motivating the need for a more personalized treatment plan. Accordingly, we have developed a ...novel quantum deep reinforcement learning (qDRL) framework for clinical decision support that can estimate an individual patient's dose response mid-treatment and recommend an optimal dose adjustment. Our framework considers patients' specific information including biological, physical, genetic, clinical, and dosimetric factors. Recognizing that physicians must make decisions amidst uncertainty in RT treatment outcomes, we employed indeterministic quantum states to represent human decision making in a real-life scenario. We paired quantum decision states with a model-based deep q-learning algorithm to optimize the clinical decision-making process in RT. We trained our proposed qDRL framework on an institutional dataset of 67 stage III non-small cell lung cancer (NSCLC) patients treated on prospective adaptive protocols and independently validated our framework in an external multi-institutional dataset of 174 NSCLC patients. For a comprehensive evaluation, we compared three frameworks: DRL, qDRL trained in a Qiskit quantum computing simulator, and qDRL trained in an IBM quantum computer. Two metrics were considered to evaluate our framework: (1) similarity score, defined as the root mean square error between retrospective clinical decisions and the AI recommendations, and (2) self-evaluation scheme that compares retrospective clinical decisions and AI recommendations based on the improvement in the observed clinical outcomes. Our analysis shows that our framework, which takes into consideration individual patient dose response in its decision-making, can potentially improve clinical RT decision-making by at least about 10% compared to unaided clinical practice. Further validation of our novel quantitative approach in a prospective study will provide a necessary framework for improving the standard of care in personalized RT.
Purpose
To investigate deep reinforcement learning (DRL) based on historical treatment plans for developing automated radiation adaptation protocols for nonsmall cell lung cancer (NSCLC) patients ...that aim to maximize tumor local control at reduced rates of radiation pneumonitis grade 2 (RP2).
Methods
In a retrospective population of 114 NSCLC patients who received radiotherapy, a three‐component neural networks framework was developed for deep reinforcement learning (DRL) of dose fractionation adaptation. Large‐scale patient characteristics included clinical, genetic, and imaging radiomics features in addition to tumor and lung dosimetric variables. First, a generative adversarial network (GAN) was employed to learn patient population characteristics necessary for DRL training from a relatively limited sample size. Second, a radiotherapy artificial environment (RAE) was reconstructed by a deep neural network (DNN) utilizing both original and synthetic data (by GAN) to estimate the transition probabilities for adaptation of personalized radiotherapy patients’ treatment courses. Third, a deep Q‐network (DQN) was applied to the RAE for choosing the optimal dose in a response‐adapted treatment setting. This multicomponent reinforcement learning approach was benchmarked against real clinical decisions that were applied in an adaptive dose escalation clinical protocol. In which, 34 patients were treated based on avid PET signal in the tumor and constrained by a 17.2% normal tissue complication probability (NTCP) limit for RP2. The uncomplicated cure probability (P+) was used as a baseline reward function in the DRL.
Results
Taking our adaptive dose escalation protocol as a blueprint for the proposed DRL (GAN + RAE + DQN) architecture, we obtained an automated dose adaptation estimate for use at ∼2/3 of the way into the radiotherapy treatment course. By letting the DQN component freely control the estimated adaptive dose per fraction (ranging from 1–5 Gy), the DRL automatically favored dose escalation/de‐escalation between 1.5 and 3.8 Gy, a range similar to that used in the clinical protocol. The same DQN yielded two patterns of dose escalation for the 34 test patients, but with different reward variants. First, using the baseline P+ reward function, individual adaptive fraction doses of the DQN had similar tendencies to the clinical data with an RMSE = 0.76 Gy; but adaptations suggested by the DQN were generally lower in magnitude (less aggressive). Second, by adjusting the P+ reward function with higher emphasis on mitigating local failure, better matching of doses between the DQN and the clinical protocol was achieved with an RMSE = 0.5 Gy. Moreover, the decisions selected by the DQN seemed to have better concordance with patients eventual outcomes. In comparison, the traditional temporal difference (TD) algorithm for reinforcement learning yielded an RMSE = 3.3 Gy due to numerical instabilities and lack of sufficient learning.
Conclusion
We demonstrated that automated dose adaptation by DRL is a feasible and a promising approach for achieving similar results to those chosen by clinicians. The process may require customization of the reward function if individual cases were to be considered. However, development of this framework into a fully credible autonomous system for clinical decision support would require further validation on larger multi‐institutional datasets.
Published data suggest that the risk of moderately severe (>or=Grade 3) radiation-induced acute small-bowel toxicity can be predicted with a threshold model whereby for a given dose level, D, if the ...volume receiving that dose or greater (VD) exceeds a threshold quantity, the risk of toxicity escalates. Estimates of VD depend on the means of structure segmenting (e.g., V15 = 120 cc if individual bowel loops are outlined or V45 = 195 cc if entire peritoneal potential space of bowel is outlined). A similar predictive model of acute toxicity is not available for stomach. Late small-bowel/stomach toxicity is likely related to maximum dose and/or volume threshold parameters qualitatively similar to those related to acute toxicity risk. Concurrent chemotherapy has been associated with a higher risk of acute toxicity, and a history of abdominal surgery has been associated with a higher risk of late toxicity.
To investigate direct radiation dose-related and inflammation-mediated regional hepatic function losses after stereotactic body radiation therapy (SBRT) in patients with hepatocellular carcinoma ...(HCC) and poor liver function.
Twenty-four patients with HCC enrolled on an IRB-approved adaptive SBRT trial had liver dynamic gadoxetic acid-enhanced magnetic resonance imaging and blood sample collections before and 1 month after SBRT. Gadoxetic acid uptake rate (k1) maps were quantified for regional hepatic function and coregistered to both 2-Gy equivalent dose and physical dose distributions. Regional k1 loss patterns from before to after SBRT were analyzed for effects of dose and patient using a mixed-effects model and logistic function and were associated with pretherapy liver-function albumin-bilirubin scores. Plasma levels of tumor necrosis factor α receptor 1 (TNFR1), an inflammation marker, were correlated with mean k1 losses in the lowest dose regions by Spearman rank correlation.
The whole group had a k1 loss rate of 0.4%/Gy (2-Gy equivalent dose); however, there was a significant random effect of patient in the mixed-effect model (P < .05). Patients with poor and good liver functions lost 50% of k1 values at 12.5 and 57.2 Gy and 33% and 16% of k1 values at the lowest dose regions (<5 Gy), respectively. The k1 losses at the lowest dose regions of individual patients were significantly correlated with their TNFR1 levels after SBRT (P < .02).
The findings suggest that regional hepatic function losses after SBRT in patients with HCC include both direct radiation dose-dependent and inflammation-mediated effects, which could influence how to manage these patients to preserve their liver function after SBRT.
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