Investigate potential clinical benefits and linear energy transfer sparing utilizing proton arc therapy for hepatocellular carcinoma.

PURPOSE: To investigate the potential clinical benefits and dose-averaged Linear Energy Transfer (LETd) sparing, utilizing proton arc plan for hepatocellular carcinoma (HCC) patients in comparison with Intensity Modulated Proton Therapy (IMPT). METHODS: Ten HCC patients have been retrospectively selected. Two planning groups were created: Proton Arc plans using Monaco ver. 6 and the clinical IMPT plan. Both planning groups used the same robustness parameters. The prescription dose is 67.5 Gy (RBE) in 15 fractions of the Clinical Target Volume (CTV). Robustness evaluations were performed to ensure dose coverage. Normal Tissue Complicated Probability (NTCP) model was utilized to predict the possibility of Radiation-Induced Liver Disease (RILD) and evaluate the potential benefit of proton arc therapy. LETd calculation and evaluation were performed as well. RESULTS: Proton arc plan has shown better dosimetric improvements of most Organ-At-Risks (OARs). More specifically, the liver mean dose has been significantly reduced from 14.7 GyE to 10.62 GyE compared to the IMPT plan. The predicted possibility of RILD has also been significantly reduced for cases with a large and deep liver target where healthy liver tissue sparing is a challenge. Additionally, proton arc therapy could increase the average LETd in the target and reduce LETd in adjacent OARs. CONCLUSIONS: The potential clinical benefit of utilizing proton arc therapy HCC varies depending on the patient-specific geometry. With more freedom, proton arc therapy can offer a better dosimetric plan quality in the challenge cases, which might not be feasible using the current IMPT technique.

Brachial plexopathy following stereotactic body radiation therapy in apical lung malignancies: A dosimetric pooled analysis of individual patient data.

BACKGROUND AND OBJECTIVES: The aim of this study is to establish dosimetric constraints for the brachial plexus at risk of developing grade ≥ 2 brachial plexopathy in the context of stereotactic body radiation therapy (SBRT). PATIENTS AND METHODS: Individual patient data from 349 patients with 356 apical lung malignancies who underwent SBRT were extracted from 5 articles. The anatomical brachial plexus was delineated following the guidelines provided in the atlases developed by Hall, et al. and Kong, et al.. Patient characteristics, pertinent SBRT dosimetric parameters, and brachial plexopathy grades (according to CTCAE 4.0 or 5.0) were obtained. Normal tissue complication probability (NTCP) models were used to estimate the risk of developing grade ≥ 2 brachial plexopathy through maximum likelihood parameter fitting. RESULTS: The prescription dose/fractionation schedules for SBRT ranged from 27 to 60 Gy in 1 to 8 fractions. During a follow-up period spanning from 6 to 113 months, 22 patients (6.3 %) developed grade ≥2 brachial plexopathy (4.3 % grade 2, 2.0 % grade 3); the median time to symptoms onset after SBRT was 8 months (ranged, 3-54 months). NTCP models estimated a 10 % risk of grade ≥2 brachial plexopathy with an anatomic brachial plexus maximum dose (Dmax) of 20.7 Gy, 34.2 Gy, and 42.7 Gy in one, three, and five fractions, respectively. Similarly, the NTCP model estimates the risks of grade ≥2 brachial plexopathy as 10 % for BED Dmax at 192.3 Gy and EQD2 Dmax at 115.4 Gy with an α/ß ratio of 3, respectively. Symptom persisted after treatment in nearly half of patients diagnosed with grade ≥2 brachial plexopathy (11/22, 50 %). CONCLUSIONS: This study establishes dosimetric constraints ranging from 20.7 to 42.7 Gy across 1-5 fractions, aimed at mitigating the risk of developing grade ≥2 brachial plexopathy following SBRT. These findings provide valuable guidance for future ablative SBRT in apical lung malignancies.

Revolution of Medical Review: The Application of Meta-Analysis and Convolutional Neural Network-Natural Language Processing in Classifying the Literature for Head and Neck Cancer Radiotherapy.

This study explored the application of meta-analysis and convolutional neural network-natural language processing (CNN-NLP) technologies in classifying literature concerning radiotherapy for head and neck cancer. It aims to enhance both the efficiency and accuracy of literature reviews. By integrating statistical analysis with deep learning, this research successfully identified key studies related to the probability of normal tissue complications (NTCP) from a vast corpus of literature. This demonstrates the advantages of these technologies in recognizing professional terminology and extracting relevant information. The findings not only improve the quality of literature reviews but also offer new insights for future research on optimizing medical studies through AI technologies. Despite the challenges related to data quality and model generalization, this work provides clear directions for future research.

This study examines how advanced technologies like meta-analysis and machine learning, specifically through Convolutional Neural Networks and Natural Language Processing (CNN-NLP), can revolutionize the way medical researchers review literature on radiotherapy for head and neck cancer. Typically, reviewing vast amounts of medical studies is time-consuming and complex. This paper showcases a method that combines statistical analysis and AI to streamline the process, enhancing the accuracy and efficiency of identifying crucial research. By applying these technologies, the researchers were able to sift through thousands of articles rapidly, pinpointing the most relevant ones without the extensive manual effort usually required. This approach not only speeds up the review process but also improves the quality of the information extracted, making it easier for medical professionals to keep up with the latest findings and apply them effectively in clinical settings. The findings of this study are promising, demonstrating that integrating AI with traditional review methods can significantly aid in managing the ever-growing body of medical literature, potentially leading to better treatment strategies and outcomes for patients suffering from head and neck cancer. Despite some challenges like data quality and the need for extensive computational resources, the study provides a forward path for using AI to enhance medical research and practice.

Radiobiological analysis of VMAT treatment plan with flattened and flattening filter free photon beam: an EUD and TCP based comparative study.

Background: This study aimed to evaluate the dosimetric and radiobiological differences between 6MV flattened filter (FF) and flattening filter free (FFF) using volumetric modulated arc (VMAT) technique for head and neck (H&N) cancer patients. Materials and methods: Fifteen patients with H&N carcinoma were selected and treated with VMAT with FF (VMATFF) treatment plan. Retrospectively, additional VMAT treatment plans were developed using FFF beams (VMATFFF). Radiobiological parameters, such as equivalent uniform dose (EUD), tumor cure probability (TCP), and normal tissue complication probability (NTCP), were calculated using Niemierko's model for both VMATFF and VMATFFF. Correlation between dosimetric and radiobiological data were analyzed and compared. Results: The conformity index (CI) was 0.975 ± 0.014 (VMATFF) and 0.964 ± 0. 019 (VMATFFF) with p ≥ 0.05. Statistically, there was an insignificant difference in the planning target volume (PTV) results for TCP (%) values, with values of 81.20 ± 0.88% (VMATFF) and 81.01 ± 0.92 (%) (VMATFF). Similarly, there was an insignificant difference in the EUD (Gy) values, which were 71.53 ± 0.33 Gy (VMATFF) and 71.46 ± 0.34 Gy (VMATFFF). The NTCP values for the spinal cord, left parotid, and right parotid were 6.54 × 10-07%, 8.04%, and 7.69%, respectively, in the case of VMATFF. For VMATFFF, the corresponding NTCP values for the spinal cord, parotids left, and parotid right were 3.09 × 10-07%, 6.57%, and 6.73%, respectively. Conclusion: The EUD and Mean Dose to PTV were strongly correlated for VMATFFF. An increased mean dose to the PTV and greater TCP were reported for the VMATFF, which can enhance the delivery of the therapeutic dose to the target.

Artificial intelligence for response prediction and personalisation in radiation oncology.

Artificial intelligence (AI) systems may personalise radiotherapy by assessing complex and multifaceted patient data and predicting tumour and normal tissue responses to radiotherapy. Here we describe three distinct generations of AI systems, namely personalised radiotherapy based on pretreatment data, response-driven radiotherapy and dynamically optimised radiotherapy. Finally, we discuss the main challenges in clinical translation of AI systems for radiotherapy personalisation.

Estimating the risk of major adverse cardiac events following radiotherapy for left breast cancer using a modified generalized Lyman normal-tissue complication probability model.

BACKGROUND: We introduced an adapted Lyman normal-tissue complication probability (NTCP) model, incorporating clinical risk factors and censored time-to-event data, to estimate the risk of major adverse cardiac events (MACE) following left breast cancer radiotherapy (RT). MATERIALS AND METHODS: Clinical characteristics and MACE data of 1100 women with left-side breast cancer receiving postoperative RT from 2005 to 2017 were retrospectively collected. A modified generalized Lyman NTCP model based on the individual left ventricle (LV) equivalent uniform dose (EUD), accounting for clinical risk factors and censored data, was developed using maximum likelihood estimation. Subgroup analysis was performed for low-comorbidity and high-comorbidity groups. RESULTS: Over a median follow-up 7.8 years, 64 patients experienced MACE, with higher mean LV dose in affected individuals (4.1 Gy vs. 2.9 Gy). The full model accounting for clinical factors identified D50 = 43.3 Gy, m = 0.59, and n = 0.78 as the best-fit parameters. The threshold dose causing a 50 % probability of MACE was lower in the high-comorbidity group (D50 = 30 Gy) compared to the low-comorbidity group (D50 = 45 Gy). Predictions indicated that restricting LV EUD below 5 Gy yielded a 10-year relative MACE risk less than 1.3 and 1.5 for high-comorbidity and low-comorbidity groups, respectively. CONCLUSION: Patients with comorbidities are more susceptible to cardiac events following breast RT. The proposed modified generalized Lyman model considers nondosimetric risk factors and addresses incomplete follow-up for late complications, offering comprehensive and individualized MACE risk estimates post-RT.

Non-complete recovery of temporal lobe white matter diffusion metrics at one year Post-Radiotherapy: Implications for Radiation-Induced necrosis risk.

BACKGROUND: Temporal lobe (TL) white matter (WM) injuries are often seen early after radiotherapy (RT) in nasopharyngeal carcinoma patients (NPCs), which fail to fully recover in later stages, exhibiting a "non-complete recovery pattern". Herein, we explored the correlation between non-complete recovery WM injuries and TL necrosis (TLN), identifying dosimetric predictors for TLN-related high-risk WM injuries. METHODS: We longitudinally examined 161 NPCs and 19 healthy controls employing multi-shell diffusion MRI. Automated fiber-tract quantification quantified diffusion metrics within TL WM tract segments. ANOVA identified non-complete recovery WM tract segments one-year post-RT. Cox regression models discerned TLN risk factors utilizing non-complete recovery diffusion metrics. Normal tissue complication probability (NTCP) models and dose-response analysis further scrutinized RT-related toxicity to high-risk WM tract segments. RESULTS: Seven TL WM tract segments exhibited a "non-complete recovery pattern". Cox regression analysis identified mean diffusivity of the left uncinate fasciculus segment 1, neurite density index (NDI) of the left cingulum hippocampus segment 1, and NDI of the right inferior longitudinal fasciculus segment 1 as TLN risk predictors (hazard ratios [HRs] with confidence interval [CIs]: 1.45 [1.17-1.81], 1.07 [1.00-1.15], and 1.15 [1.03-1.30], respectively; all P-values < 0.05). In NTCP models, D10cc.L, D20cc.L and D10cc.R demonstrated superior performance, with TD50 of 37.22 Gy, 24.96 Gy and 37.28 Gy, respectively. CONCLUSIONS: Our findings underscore the significance of the "non-complete recovery pattern" in TL WM tract segment injuries during TLN development. Understanding TLN-related high-risk WM tract segments and their tolerance doses could facilitate early intervention in TLN and improve RT protocols.

Dosimetric comparison of IMPT vs VMAT for multiple lung lesions: an NTCP model-based decision-making strategy.

To compare the dosimetric differences in volumetric modulated arc therapy (VMAT) and intensity modulated proton therapy (IMPT) in stereotactic body radiation therapy (SBRT) of multiple lung lesions and determine a normal tissue complication probability (NTCP) model-based decision strategy that determines which treatment modality the patient will use. A total of 41 patients were retrospectively selected for this study. The number of patients with 1-6 lesions was 5, 16, 7, 6, 3, and 4, respectively. A prescription dose of 70 GyRBE in 10 fractions was given to each lesion. SBRT plans were generated using VMAT and IMPT. All the IMPT plans used robustness optimization with ± 3.5% range uncertainties and 5 mm setup uncertainties. Dosimetric metrics and the predicted NTCP value of radiation pneumonitis (RP), esophagitis, and pericarditis were analyzed to evaluate the potential clinical benefits between different planning groups. In addition, a threshold for the ratio of PTV to lungs (%) to determine whether a patient would benefit highly from IMPT was determined using receiver operating characteristic curves. All plans reached target coverage (V70GyRBE ≥ 95%). Compared with VMAT, IMPT resulted in a significantly lower dose of most thoracic normal tissues. For the 1-2, 3-4 and 5-6 lesion groups, the lung V5 was 29.90 ± 9.44%, 58.33 ± 13.35%, and 81.02 ± 5.91% for VMAT and 11.34 ± 3.11% (p < 0.001), 21.45 ± 3.80% (p < 0.001), and 32.48 ± 4.90% (p < 0.001) for IMPT, respectively. The lung V20 was 12.07 ± 4.94%, 25.57 ± 6.54%, and 43.99 ± 11.83% for VMAT and 6.76 ± 1.80% (p < 0.001), 13.14 ± 2.27% (p < 0.01), and 19.62 ± 3.48% (p < 0.01) for IMPT. The Dmean of the total lung was 7.65 ± 2.47 GyRBE, 14.78 ± 2.75 GyRBE, and 21.64 ± 4.07 GyRBE for VMAT and 3.69 ± 1.04 GyRBE (p < 0.001), 7.13 ± 1.41 GyRBE (p < 0.001), and 10.69 ± 1.81 GyRBE (p < 0.001) for IMPT. Additionally, in the VMAT group, the maximum NTCP value of radiation pneumonitis was 73.91%, whereas it was significantly lower in the IMPT group at 10.73%. The accuracy of our NTCP model-based decision model, which combines the number of lesions and PTV/Lungs (%), was 97.6%. The study demonstrated that the IMPT SBRT for multiple lung lesions had satisfactory dosimetry results, even when the number of lesions reached 6. The NTCP model-based decision strategy presented in our study could serve as an effective tool in clinical practice, aiding in the selection of the optimal treatment modality between VMAT and IMPT.

A marginal structural model for normal tissue complication probability.

The goal of radiation therapy for cancer is to deliver prescribed radiation dose to the tumor while minimizing dose to the surrounding healthy tissues. To evaluate treatment plans, the dose distribution to healthy organs is commonly summarized as dose-volume histograms (DVHs). Normal tissue complication probability (NTCP) modeling has centered around making patient-level risk predictions with features extracted from the DVHs, but few have considered adapting a causal framework to evaluate the safety of alternative treatment plans. We propose causal estimands for NTCP based on deterministic and stochastic interventions, as well as propose estimators based on marginal structural models that impose bivariable monotonicity between dose, volume, and toxicity risk. The properties of these estimators are studied through simulations, and their use is illustrated in the context of radiotherapy treatment of anal canal cancer patients.

Comparison of deep inspiration breath hold and free breathing intensity modulated proton therapy of locally advanced lung cancer.

Background and purpose: For locally advanced non-small cell lung cancer (LA-NSCLC), intensity-modulated proton therapy (IMPT) can reduce organ at risk (OAR) doses compared to intensity-modulated radiotherapy (IMRT). Deep inspiration breath hold (DIBH) reduces OAR doses compared to free breathing (FB) in IMRT. In IMPT, differences in dose distributions and robustness between DIBH and FB are unclear. In this study, we compare DIBH to FB in IMPT, and IMPT to IMRT. Materials and methods: Fortyone LA-NSCLC patients were prospectively included. 4D computed tomography images (4DCTs) and DIBH CTs were acquired for treatment planning and during weeks 1 and 3 of treatment. A new system for automated robust planning was developed and used to generate a FB and a DIBH IMPT plan for each patient. Plans were compared in terms of dose-volume parameters and normal tissue complication probabilities (NTCPs). Dose recalculations on repeat CTs were used to compare inter-fraction plan robustness. Results: In IMPT, DIBH reduced median lungs Dmean from 9.3 Gy(RBE) to 8.0 Gy(RBE) compared to FB, and radiation pneumonitis NTCP from 10.9 % to 9.4 % (p < 0.001). Inter-fraction plan robustness for DIBH and FB was similar. Median NTCPs for radiation pneumonitis and mortality were around 9 percentage points lower with IMPT than IMRT (p < 0.001). These differences were much larger than between FB and DIBH within each modality. Conclusion: DIBH IMPT resulted in reduced lung dose and radiation pneumonitis NTCP compared to FB IMPT. Inter-fraction robustness was comparable. OAR doses were far lower in IMPT than IMRT.

Normal tissue complication probability model of temporal lobe injury following re-irradiation of IMRT for local recurrent nasopharyngeal carcinoma.

Purpose: We tried to establish the normal tissue complication probability (NTCP) model of temporal lobe injury of recurrent nasopharyngeal carcinoma (NPC) patients after two courses of intensity modulated radiotherapy (IMRT) to provide more reliable dose-volume data reference to set the temporal lobe tolerance dose for recurrent NPC patients in the future. Methods and materials: Recurrent NPC patients were randomly divided into training data set and validation data set in a ratio of 2:1, All the temporal lobes (TLs) were re-contoured as R/L structures and named separately in the MIM system. The dose distribution of the initial IMRT plan was deformed into the second course planning CT via MIM software to get the deformed dose. Equivalent dose of TLs in 2Gy fractions was calculated via linear quadratic model, using an α/ß=3 for temporal lobes. NTCP model that correlated the irradiated volume of the temporal lobe and? the clinical variables were evaluated in a multivariate prediction model using AUC analysis. Results: From Jan. 2010 to Dec. 2020, 78 patients were enrolled into our study. Among which 26 (33.3%) developed TLI. The most important factors affecting TLI was the sum-dose d1.5cc of TL, while the possible clinical factors did not reach statistically significant differences in multivariate analysis. According to NTCP model, the TD5 and TD50 EQD2 dose of sum-dose d1.5cc were 65.26Gy (46.72-80.69Gy) and 125.25Gy (89.51-152.18Gy), respectively. For the accumulated EQD2 dose, the area under ROC shadow was 0.8702 (0.7577-0.9828) in model validation, p<0.001. Conclusion: In this study, a NTCP model of temporal lobe injury after a second course of IMRT for recurrent nasopharyngeal carcinoma was established. TD5 and TD50 doses of temporal lobe injury after re-RT were obtained according to the model, and the model was verified by validation set data.

Quantify the Effect of Air Gap Errors on Skin Dose for Breast Cancer Radiotherapy.

Purpose: Determining the impact of air gap errors on the skin dose in postoperative breast cancer radiotherapy under dynamic intensity-modulated radiation therapy (IMRT) techniques. Methods: This was a retrospective study that involved 55 patients who underwent postoperative radiotherapy following modified radical mastectomy. All plans employed tangential IMRT, with a prescription dose of 50 Gy, and bolus added solely to the chest wall. Simulated air gap depth errors of 2 mm, 3 mm, and 5 mm were introduced at depression or inframammary fold areas on the skin, resulting in the creation of air gaps named Air2, Air3, and Air5. Utilizing a multivariable GEE, the average dose (Dmean) of the local skin was determined to evaluate its relationship with air gap volume and the lateral beam's average angle (AALB). Additionally, an analysis was conducted on the impact of gaps on local skin. Results: When simulating an air gap depth error of 2 mm, the average Dmean in plan2 increased by 0.46 Gy compared to the initial plan (planO) (p < .001). For the 3-mm air gap, the average Dmean of plan3 was 0.51 Gy higher than that of planO (p < .001). When simulating the air gap as 5 mm, the average Dmean of plan5 significantly increased by 0.59 Gy compared to planO (p < .001). The TCP results showed a similar trend to those of Dmean. As the depth of air gap error increases, NTCP values also gradually rise. The linear regression of the multivariable GEE equation indicates that the volume of air gaps and the AALB are strong predictors of Dmean. Conclusion: With small irregular air gap errors simulated in 55 patients, the values of skin's Dmean, TCP, and NTCP increased. A multivariable linear GEE regression model may effectively explain the impact of air gap volume and AALB on the local skin.

Evaluation of an automated clinical decision system with deep learning dose prediction and NTCP model for prostate cancer proton therapy.

Objective.To evaluate the feasibility of using a deep learning dose prediction approach to identify patients who could benefit most from proton therapy based on the normal tissue complication probability (NTCP) model.Approach.Two 3D UNets were established to predict photon and proton doses. A dataset of 95 patients with localized prostate cancer was randomly partitioned into 55, 10, and 30 for training, validation, and testing, respectively. We selected NTCP models for late rectum bleeding and acute urinary urgency of grade 2 or higher to quantify the benefit of proton therapy. Propagated uncertainties of predicted ΔNTCPs resulting from the dose prediction errors were calculated. Patient selection accuracies for a single endpoint and a composite evaluation were assessed under different ΔNTCP thresholds.Main results.Our deep learning-based dose prediction technique can reduce the time spent on plan comparison from approximately 2 days to as little as 5 seconds. The expanded uncertainty of predicted ΔNTCPs for rectum and bladder endpoints propagated from the dose prediction error were 0.0042 and 0.0016, respectively, which is less than one-third of the acceptable tolerance. The averaged selection accuracies for rectum bleeding, urinary urgency, and composite evaluation were 90%, 93.5%, and 93.5%, respectively.Significance.Our study demonstrates that deep learning dose prediction and NTCP evaluation scheme could distinguish the NTCP differences between photon and proton treatment modalities. In addition, the dose prediction uncertainty does not significantly influence the decision accuracy of NTCP-based patient selection for proton therapy. Therefore, automated deep learning dose prediction and NTCP evaluation schemes can potentially be used to screen large patient populations and to avoid unnecessary delays in the start of prostate cancer radiotherapy in the future.

Dose-response modeling and treatment plan assessment with a python software toolkit.

This software assistant aims at calculating the dose-response relations of tumors and normal tissues, or clinically assessing already determined values by other researchers. It can also indicate the optimal dose prescription by optimizing the expected treatment outcome. The software is developed solely in python programming language, and it employs PSFL license for its Graphical User Interface (GUI), NUMPY, MATPLOTLIB, and SCIPY libraries. It comprises of two components. The first is the Dose-response relations derivation component, which takes as input the dose volume histograms (DVHs) of patients and their recorded responses regarding a given clinical endpoint to determine the parameters of different tumor control probability (TCP) or normal tissue complication probability (NTCP) models. The second is the Treatment Plan Assessment component, which uses the DVHs of a plan and the dose-response parameters values of the involved tumors and organs at risk (OARs) to calculate their expected responses. Additionally, the overall probabilities of benefit (PB), injury (PI) and complication-free tumor control (P+) are calculated. The software calculates rapidly the corresponding generalized equivalent uniform doses (gEUD) and biologically effective uniform doses (D‾‾) for the Lyman-Kutcher-Burman (LKB), parallel volume (PV) and relative seriality (RS) models respectively, determining the model parameters. In the Dose-Response Relations Derivation component, the software plots the dose-response curves of the irradiated organ with the relevant confidence internals along with the data of the patients with and without toxicity. It also calculates the odds ratio (OR) and the area under the curve (AUC) of different dose metrics or model parameter values against the individual patient outcomes to determine their discrimination capacity. It also performs a goodness-of-fit evaluation of any model parameter set. The user has the option of viewing plots like Scatter, 3D surfaces, and Bootstrap plots. In the Treatment Plan Assessment part, the software calculates the TCP and NTCP values of the involved tumors and OARs, respectively. Furthermore, it plots the dose-response curves of the TCPs, NTCPs, PB, PI, and P+ for a range of prescription doses for different treatment plans. The presented software is ideal for efficiently conducting studies of radiobiological modeling. Furthermore, it is ideal for performing treatment plan assessment, comparison, and optimization studies.

Comparative analysis of simultaneous integrated boost and sequential boost radiotherapy in node-positive cervical cancer: dosimetric and radiobiological considerations.

For locally advanced cervical cancer, the standard therapeutic approach involves concomitant chemoradiation therapy, supplemented by a brachytherapy boost. Moreover, an external beam radiotherapy (RT) boost should be considered for treating gross lymph node (LN) volumes. Two boost approaches exist with Volumetric Intensity Modulated Arc Therapy (VMAT): Sequential (SEQ) and Simultaneous Integrated Boost (SIB). This study undertakes a comprehensive dosimetric and radiobiological comparison between these two boost strategies. The study encompassed ten patients who underwent RT for cervical cancer with node-positive disease. Two sets of treatment plans were generated for each patient: SIB-VMAT and SEQ-VMAT. Dosimetric as well as radiobiological parameters including tumour control probability (TCP) and normal tissue complication probability (NTCP) were compared. Both techniques were analyzed for two different levels of LN involvement - only pelvic LNs and pelvic with para-aortic LNs. Statistical analysis was performed using SPSS software version 25.0. SIB-VMAT exhibited superior target coverage, yielding improved doses to the planning target volume (PTV) and gross tumour volume (GTV). Notably, SIB-VMAT plans displayed markedly superior dose conformity. While SEQ-VMAT displayed favorable organ sparing for femoral heads, SIB-VMAT appeared as the more efficient approach for mitigating bladder and bowel doses. TCP was significantly higher with SIB-VMAT, suggesting a higher likelihood of successful tumour control. Conversely, no statistically significant difference in NTCP was observed between the two techniques. This study's findings underscore the advantages of SIB-VMAT over SEQ-VMAT in terms of improved target coverage, dose conformity, and tumour control probability. In particular, SIB-VMAT demonstrated potential benefits for cases involving para-aortic nodes. It is concluded that SIB-VMAT should be the preferred approach in all cases of locally advanced cervical cancer.

Artificial neural network-assisted prediction of radiobiological indices in head and neck cancer.

Background and purpose: We proposed an artificial neural network model to predict radiobiological parameters for the head and neck squamous cell carcinoma patients treated with radiation therapy. The model uses the tumor specification, demographics, and radiation dose distribution to predict the tumor control probability and the normal tissue complications probability. These indices are crucial for the assessment and clinical management of cancer patients during treatment planning. Methods: Two publicly available datasets of 31 and 215 head and neck squamous cell carcinoma patients treated with conformal radiation therapy were selected. The demographics, tumor specifications, and radiation therapy treatment parameters were extracted from the datasets used as inputs for the training of perceptron. Radiobiological indices are calculated by open-source software using dosevolume histograms from radiation therapy treatment plans. Those indices were used as output in the training of a single-layer neural network. The distribution of data used for training, validation, and testing purposes was 70, 15, and 15%, respectively. Results: The best performance of the neural network was noted at epoch number 32 with the mean squared error of 0.0465. The accuracy of the prediction of radiobiological indices by the artificial neural network in training, validation, and test phases were determined to be 0.89, 0.87, and 0.82, respectively. We also found that the percentage volume of parotid inside the planning target volume is the significant parameter for the prediction of normal tissue complications probability. Conclusion: We believe that the model has significant potential to predict radiobiological indices and help clinicians in treatment plan evaluation and treatment management of head and neck squamous cell carcinoma patients.

The role of the soft palate dose regarding normal tissue toxicities in older adults with head and neck cancer undergoing definitive radiotherapy.

PURPOSE: The number of older adults with head and neck squamous cell carcinoma (HNSCC) is continuously increasing. Older HNSCC patients may be more vulnerable to radiotherapy-related toxicities, so that extrapolation of available normal tissue complication probability (NTCP) models to this population may not be appropriate. Hence, we aimed to investigate the correlation between organ at risk (OAR) doses and chronic toxicities in older patients with HNSCC undergoing definitive radiotherapy. METHODS: Patients treated with definitive radiotherapy, either alone or with concomitant systemic treatment, between 2009 and 2019 in a large tertiary cancer center were eligible for this analysis. OARs were contoured based on international consensus guidelines, and EQD2 doses using α/ß values of 3 Gy for late effects were calculated based on the radiation treatment plans. Treatment-related toxicities were graded according to Common Terminology Criteria for Adverse Events version 5.0. Logistic regression analyses were carried out, and NTCP models were developed and internally validated using the bootstrapping method. RESULTS: A total of 180 patients with a median age of 73 years fulfilled the inclusion criteria and were analyzed. Seventy-three patients developed chronic moderate xerostomia (grade 2), 34 moderate dysgeusia (grade 2), and 59 moderate-to-severe (grade 2-3) dysphagia after definitive radiotherapy. The soft palate dose was significantly associated with all analyzed toxicities (xerostomia: OR = 1.028, dysgeusia: OR = 1.022, dysphagia: OR = 1.027) in the multivariable regression. The superior pharyngeal constrictor muscle was also significantly related to chronic dysphagia (OR = 1.030). Consecutively developed and internally validated NTCP models were predictive for the analyzed toxicities (optimism-corrected AUCs after bootstrapping: AUCxerostomia=0.64, AUCdysgeusia=0.60, AUCdysphagia=0.64). CONCLUSIONS: Our data suggest that the dose to the soft palate is associated with chronic moderate xerostomia, moderate dysgeusia and moderate-to-severe dysphagia in older HNSCC patients undergoing definitive radiotherapy. If validated in external studies, efforts should be undertaken to reduce the soft palate dose in these patients.

Probability of normal tissue complications for hematologic and gastrointestinal toxicity in postoperative whole pelvic radiotherapy for gynecologic malignancies using intensity-modulated proton therapy with robust optimization.

This retrospective treatment-planning study was conducted to determine whether intensity-modulated proton therapy with robust optimization (ro-IMPT) reduces the risk of acute hematologic toxicity (H-T) and acute and late gastrointestinal toxicity (GI-T) in postoperative whole pelvic radiotherapy for gynecologic malignancies when compared with three-dimensional conformal radiation therapy (3D-CRT), intensity-modulated X-ray (IMXT) and single-field optimization proton beam (SFO-PBT) therapies. All plans were created for 13 gynecologic-malignancy patients. The prescribed dose was 45 GyE in 25 fractions for 95% planning target volume in 3D-CRT, IMXT and SFO-PBT plans and for 99% clinical target volume (CTV) in ro-IMPT plans. The normal tissue complication probability (NTCP) of each toxicity was used as an in silico surrogate marker. Median estimated NTCP values for acute H-T and acute and late GI-T were 0.20, 0.94 and 0.58 × 10-1 in 3D-CRT; 0.19, 0.65 and 0.24 × 10-1 in IMXT; 0.04, 0.74 and 0.19 × 10-1 in SFO-PBT; and 0.06, 0.66 and 0.15 × 10-1 in ro-IMPT, respectively. Compared with 3D-CRT and IMXT plans, the ro-IMPT plan demonstrated significant reduction in acute H-T and late GI-T. The risk of acute GI-T in ro-IMPT plan is equivalent with IMXT plan. The ro-IMPT plan demonstrated potential clinical benefits for reducing the risk of acute H-T and late GI-T in the treatment of gynecologic malignances by reducing the dose to the bone marrow and bowel bag while maintaining adequate dose coverage to the CTV. Our results indicated that ro-IMPT may reduce acute H-T and late GI-T risk with potentially improving outcomes for postoperative gynecologic-malignancy patients with concurrent chemotherapy.

Predicting radiation pneumonitis in lung cancer: a EUD-based machine learning approach for volumetric modulated arc therapy patients.

Purpose: This study aims to develop an optimal machine learning model that uses lung equivalent uniform dose (lung EUD to predict radiation pneumonitis (RP) occurrence in lung cancer patients treated with volumetric modulated arc therapy (VMAT). Methods: We analyzed a cohort of 77 patients diagnosed with locally advanced squamous cell lung cancer (LASCLC) receiving concurrent chemoradiotherapy with VMAT. Patients were categorized based on the onset of grade II or higher radiation pneumonitis (RP 2+). Dose volume histogram data, extracted from the treatment planning system, were used to compute the lung EUD values for both groups using a specialized numerical analysis code. We identified the parameter α, representing the most significant relative difference in lung EUD between the two groups. The predictive potential of variables for RP2+, including physical dose metrics, lung EUD, normal tissue complication probability (NTCP) from the Lyman-Kutcher-Burman (LKB) model, and lung EUD-calibrated NTCP for affected and whole lung, underwent both univariate and multivariate analyses. Relevant variables were then employed as inputs for machine learning models: multiple logistic regression (MLR), support vector machine (SVM), decision tree (DT), and K-nearest neighbor (KNN). Each model's performance was gauged using the area under the curve (AUC), determining the best-performing model. Results: The optimal α-value for lung EUD was 0.3, maximizing the relative lung EUD difference between the RP 2+ and non-RP 2+ groups. A strong correlation coefficient of 0.929 (P< 0.01) was observed between lung EUD (α = 0.3) and physical dose metrics. When examining predictive capabilities, lung EUD-based NTCP for the affected lung (AUC: 0.862) and whole lung (AUC: 0.815) surpassed LKB-based NTCP for the respective lungs. The decision tree (DT) model using lung EUD-based predictors emerged as the superior model, achieving an AUC of 0.98 in both training and validation datasets. Discussions: The likelihood of developing RP 2+ has shown a significant correlation with the advancements in RT technology. From traditional 3-D conformal RT, lung cancer treatment methodologies have transitioned to sophisticated techniques like static IMRT. Accurately deriving such a dose-effect relationship through NTCP modeling of RP incidence is statistically challenging due to the increased number of degrees-of-freedom. To the best of our knowledge, many studies have not clarified the rationale behind setting the α-value to 0.99 or 1, despite the closely aligned calculated lung EUD and lung mean dose MLD. Perfect independence among variables is rarely achievable in real-world scenarios. Four prominent machine learning algorithms were used to devise our prediction models. The inclusion of lung EUD-based factors substantially enhanced their predictive performance for RP 2+. Our results advocate for the decision tree model with lung EUD-based predictors as the optimal prediction tool for VMAT-treated lung cancer patients. Which could replace conventional dosimetric parameters, potentially simplifying complex neural network structures in prediction models.

Using meta-analysis and CNN-NLP to review and classify the medical literature for normal tissue complication probability in head and neck cancer.

PURPOSE: The study aims to enhance the efficiency and accuracy of literature reviews on normal tissue complication probability (NTCP) in head and neck cancer patients using radiation therapy. It employs meta-analysis (MA) and natural language processing (NLP). MATERIAL AND METHODS: The study consists of two parts. First, it employs MA to assess NTCP models for xerostomia, dysphagia, and mucositis after radiation therapy, using Python 3.10.5 for statistical analysis. Second, it integrates NLP with convolutional neural networks (CNN) to optimize literature search, reducing 3256 articles to 12. CNN settings include a batch size of 50, 50-200 epoch range and a 0.001 learning rate. RESULTS: The study's CNN-NLP model achieved a notable accuracy of 0.94 after 200 epochs with Adamax optimization. MA showed an AUC of 0.67 for early-effect xerostomia and 0.74 for late-effect, indicating moderate to high predictive accuracy but with high variability across studies. Initial CNN accuracy of 66.70% improved to 94.87% post-tuning by optimizer and hyperparameters. CONCLUSION: The study successfully merges MA and NLP, confirming high predictive accuracy for specific model-feature combinations. It introduces a time-based metric, words per minute (WPM), for efficiency and highlights the utility of MA and NLP in clinical research.