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.

Dosimetric Parameters Related to Acute Radiation Dermatitis of Patients with Nasopharyngeal Carcinoma Treated by Intensity-Modulated Proton Therapy.

Background: Growing patients with nasopharyngeal carcinoma (NPC) were treated with intensity-modulated proton therapy (IMPT). However, a high probability of severe acute radiation dermatitis (ARD) was observed. The objective of the study is to investigate the dosimetric parameters related to ARD for NPC patients treated with IMPT. Methods: Sixty-two patients with newly diagnosed NPC were analyzed. The ARD was recorded based on the criteria of Common Terminology Criteria for Adverse Events version 4.0. Logistic regression model was performed to identify the clinical and dosimetric parameters related to ARD. Receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC) were used to evaluate the performance of the models. Results: The maximum ARD grade was 1, 2, and 3 in 27 (43.5%), 26 (42.0%), and 9 (14.5%) of the patients, respectively. Statistically significant differences (p < 0.01) in average volume to skin 5 mm with the respective doses were observed in the range 54−62 Cobalt Gray Equivalent (CGE) for grade 2 and 3 versus grade 1 ARD. Smoking habit and N2-N3 status were identified as significant predictors to develop grade 2 and 3 ARD in clinical model, and V58CGE to skin 5 mm as an independent predictor in dosimetric model. After adding the variable of V58CGE to the metric incorporating two parameters of smoking habit and N status, the AUC value of the metric increases from 0.78 (0.66−0.90) to 0.82 (0.72−0.93). The most appropriate cut-off value of V58CGE to skin 5 mm as determined by ROC curve was 5.0 cm3, with a predicted probability of 54% to develop grade 2 and 3 ARD. Conclusion: The dosimetric parameter of V58CGE to skin 5 mm < 5.0 cm3 could be used as a constraint in treatment planning for NPC patients treated by IMPT.

Medical Mask Wearing During Treatment for Patients Undergoing Radiotherapy in COVID-19 Pandemic - An Experience of Protocol Setup and Dosimetric Evaluation for Particle/Proton Beam Therapy.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has caused extreme challenges for the healthcare system. Medical masks have been proven to effectively block disease transmission. Radiotherapeutic departments are at unique risk for disease exposure with the repeated daily treatment schedule. A protocol of mask wearing during daily treatment was established, and the effect of wearing medical masks on dosimetry during proton beam therapy (PBT) was validated. METHODS: A department protocol of medical mask wearing was initiated after the COVID-19 pandemic. Medical masks that were made under standardized specification and regulation were obtained for analyses. The physical and dosimetric characteristics of these medical masks were measured by different proton energies using commercialized measurement tools. RESULTS: Patients and staff were able to adopt the protocol on a weekly basis, and no adverse events were reported. The average physical thickness of a single piece of medical mask was 0.5 mm with a water equivalent thickness (WET) of 0.1 mm. CONCLUSION: Our study revealed that mask wearing for patients undergoing daily radiotherapy is feasible and can provide basic protection for patients and staff. The impact of mask wearing on dosimetry was only 0.1 mm in WET, which has no impact on clinical PBT treatment. A medical mask-wearing policy can be applied safely without dosimetric concerns and should be considered as a standard practice for PBT centers during the COVID-19 pandemic.

Radiation-Induced Secondary Cancer Risk Assessment in Patients With Lung Cancer After Stereotactic Body Radiotherapy Using the CyberKnife M6 System With Lung-Optimized Treatment.

BACKGROUND: To evaluate the lifetime secondary cancer risk (SCR) of stereotactic body radiotherapy (SBRT) using the CyberKnife (CK) M6 system with a lung-optimized treatment (LOT) module for lung cancer patients. METHODS: We retrospectively enrolled 11 lung cancer patients curatively treated with SBRT using the CK M6 robotic radiosurgery system. The planning treatment volume (PTV) and common organs at risk (OARs) for SCR analysis included the spinal cord, total lung, and healthy normal lung tissue (total lung volume - PTV). Schneider's full model was used to calculate SCR according to the concept of organ equivalent dose (OED). RESULTS: CK-LOT-SBRT delivers precisely targeted radiation doses to lung cancers and achieves good PTV coverage and conformal dose distribution, thus posing limited SCR to surrounding tissues. The three OARs had similar risk equivalent dose (RED) values among four different models. However, for the PTV, differences in RED values were observed among the models. The cumulative excess absolute risk (EAR) value for the normal lung, spinal cord, and PTV was 70.47 (per 10,000 person-years). Schneider's Lnt model seemed to overestimate the EAR/lifetime attributable risk (LAR). CONCLUSION: For lung cancer patients treated with CK-LOT optimized with the Monte Carlo algorithm, the SCR might be lower. Younger patients had a greater SCR, although the dose-response relationship seemed be non-linear for the investigated organs, especially with respect to the PTV. Despite the etiological association, the SCR after CK-LOT-SBRT for carcinoma and sarcoma, is low, but not equal to zero. Further research is required to understand and to show the lung SBRT SCR comparisons and differences across different modalities with motion management strategies.