Objective: Ultra-high dose rate FLASH radiotherapy (FLASH-RT) is emerging as a novel technique to improve the normal tissue tolerance by delivering ultra-high dose rate radiation several orders of magnitude higher than convention radiotherapy. It has been shown in preclinical studies to cause less injury to surrounding normal tissues during radiation treatment, while still maintaining local tumor control. The purpose of this protocol is to evaluate the safety of fractionated FLASH-RT in skin cancer.
Method: Patients with superficial skin tumors will be enrolled. The eligible patients will undergo electron FLASH-RT (24-40 Gy/3-5 fractions) to the tumor volume. The primary outcome is to evaluate the safety of FLASH-RT by collecting the acute (< 90 days) skin toxicity adverse events of radiation according to Common Terminology Criteria for Adverse Events (CTCAE) version 5.0. Secondary objectives include late (> 90 days) skin toxicity after FLASH-RT according to CTCAE version 5.0 and treatment response.
Discussion: If the results show that delivering FLASH-RT is safe and feasible for skin tumors, further investigation will be conduct to evaluate efficacy of FLASH-RT in a phase II trial.
Trial Registration number: ChiCTR2400080935. https://www.chictr.org.cn/showproj.html?proj=220336
Background: Computed Tomography (CT) imaging is essential for disease detection but carries a risk of cancer due to X-ray exposure. Typically, assessing this risk requires segmentation of the internal organ contours to predict organ doses, which hinders its clinical application. This study introduces a method that uses support vector regression (SVR) models trained on skin outline radiomic features to predict organ doses without organ segmentation, thus streamlining the process for clinical use.
Methods: CT scans of the head and abdomen were used to extract radiomic features of the skin outline. These features were used as inputs, with organ doses from Monte Carlo simulations as benchmarks to train the SVR models for predicting organ doses. The accuracy of the models was evaluated using the mean absolute percentage error (MAPE) and coefficient of determination (R2).
Results: The results showed a high precision in dose prediction for various organs, including the brain (MAPE: 1.5%, R2: 0.9), eyes (MAPE: 5%, R2: 0.84), lens (MAPE: 5%, R2: 0.82), bowel (MAPE: 6%, R2: 0.84), kidneys (MAPE: 7.5%, R2: 0.7), and liver (MAPE: 8%, R2: 0.67). Internal organ disturbances had a minimal impact on accuracy.
Conclusions: The SVR models efficiently predicted patient-specific organ doses from CT scans, offering a user-friendly tool for rapid segmentation-free dose prediction. This innovation can significantly enhance clinical efficiency and accessibility in predicting patient-specific organ doses using CT.
CyberKnife, an established noninvasive stereotactic radiotherapy technology, has been extensively utilized to treat various malignancies because of its high precision and conformal dose delivery. The success of CyberKnife treatment is crucially dependent on optimal fiducial marker placement. This study introduces a novel fiducial marker placement planning algorithm tailored for superficial tumors, which are located 20–50 mm beneath the epidermis. A retrospective analysis was performed on the data collected from three patients with thymus, breast, and submandibular gland tumors. This algorithm generated potential implantation sites by constructing and optimizing a B-spline surface around the tumor. Candidate points were filtered using multi-criteria constraints: (1) a minimum of 18-mm inter-marker distance, (2) angular separation >30°, and (3) nonoverlapping visibility in 45° oblique digital reconstructed radiographs. To enhance the computational efficiency, a kd-tree spatial indexing structure was integrated with graph theory, specifically the Bron–Kerbosch algorithm for maximal clique detection. The proposed method achieved a time complexity of O(mlogm + m2 + ), demonstrating a significant improvement over the brute-force O(n3) approach. The experimental results showed that our algorithm could efficiently plan fiducial marker placement, thereby simplifying the planning process and providing valuable technical support for CyberKnife treatments.
Objective: This study aimed to assess the dosimetric parameters and hematological toxicity (HT) associated with bone marrow-sparing (BMS) intensity-modulated radiation therapy (IMRT) in patients diagnosed with International Federation of Gynecology and Obstetrics (FIGO) stage IIIC cervical cancer undergoing extended-field radiation therapy (EFRT).
Methods: Patients with cervical cancer presenting with common iliac or para-aortic lymph node metastases require EFRT, which often results in grade 3 HT. Therefore, we retrospectively analyzed data of 84 patients with FIGO stage IIIC cervical cancer who underwent concurrent chemoradiotherapy (EFRT, brachytherapy, and weekly cisplatin 40 mg/m2) at Harbin Medical University Cancer Hospital, including 40 who received BMS-IMRT and 44 who received normal IMRT. Dose–volume histogram (DVH) parameters and estimated treatment times were compared. We also compared acute HT between the normal and BMS groups.
Results: Dosimetric analysis demonstrated that BMS-IMRT significantly reduced the mean volume of bone marrow receiving ≥10, ≥20, ≥30, and ≥40 Gy without affecting the target coverage of planning target volume and sparing the organs at risk. Within the BMS-IMRT group, 37.5% of the patients developed grade ≥3 HT, with an increase in HT (HT3+ = 61.4%) in patients receiving normal-IMRT (P = 0.029).
Conclusions: For patients with cervical cancer treated with EFRT, BMS-IMRT represents a feasible treatment approach that may mitigate HT and facilitate the uninterrupted administration of concurrent chemoradiotherapy.
Background: Accurate surface dose calculation is crucial in superficial low-energy electron beam radiotherapy owing to shallow treatment depths and the risk of skin toxicity. Traditional Monte Carlo (MC) simulations are precise but computationally expensive and time-consuming.
Methods: This study combined MC simulations with deep learning to improve both accuracy and speed. DOSXYZnrc was used to simulate low-energy electron beams for six body sites, generating computed tomography phantoms and corresponding dose distributions. A cascaded 3D U-Net (C3D) model was trained on these datasets to predict dose distributions rapidly.
Results: The C3D model demonstrated significant improvements over traditional 3D U-Net models, achieving a minimum Gamma pass rate of 92.09% and a minimum dose difference pass rate of 93.58%. The model completed dose predictions in just 0.42 seconds, making predictions approximately 140,000 times faster than MC simulations. In the evaluation of dose distributions across six anatomical regions, C3D consistently outperformed other deep learning models (3D U-Net, Deep Convolutional Neural Network, and HD U-Net) in both accuracy and robustness.
Conclusion: The integration of deep learning with MC simulations significantly enhances the efficiency of surface dose calculations in superficial electron beam radiotherapy. The C3D model provides rapid and accurate dose predictions, facilitating efficient treatment planning while maintaining high accuracy.
Positron emission tomography (PET) with gallium-68 prostate-specific membrane antigen (68Ga-PSMA) has emerged as a promising imaging modality for evaluating prostate cancer (PC). Quantification of tumor volume is crucial for staging, radiotherapy treatment planning, response assessment, and prognosis in PC patients. This review provides an overview of the current methods and challenges in the assessment of regional and total tumor volumes using 68Ga-PSMA PET. Traditional manual segmentation methods are time-consuming processes that are further challenged by inter-observer variability. Artificial intelligence (AI)-based segmentation techniques offer a promising solution to these challenges. AI algorithms, such as deep learning-based models, have shown remarkable performance in automating tumor segmentation tasks with high accuracy and efficiency. This review discusses the principles underlying AI-based segmentation algorithms, including convolutional neural networks, and their applications in PC imaging. Furthermore, the advantages of AI-based segmentation are highlighted, such as improved reproducibility, faster processing times, and potential for personalized medicine. Despite these advancements, AI-based segmentation faces significant challenges, including the need for standardized protocols, extensive validation studies, and seamless integration into clinical workflows. Addressing these limitations is essential for the widespread adoption of AI-based segmentation in 68Ga-PSMA PET for PC, ultimately advancing the field and improving patient care.
Purpose: This study evaluates the efficacy, toxicity, and survival impact of high-dose-rate endorectal brachytherapy (HDR-EBT) as neoadjuvant therapy for locally advanced rectal cancer.
Methods: A review of 16 studies from PubMed, Embase, and Web of Science (1990–2023) was conducted.
Results: Patients treated with HDR-EBT alone had a pathological complete response (pCR) rate of 23.7%–35.3% (mean: 24.3%), anal preservation rate of 12.2%–74.9% (mean: 41.8%), and 5-year progression-free survival rate of 64.6%–65.4% (mean: 65.3%). When combined with concurrent long-term radiotherapy and chemotherapy, pCR rates improved from 18.1%–55.0% (mean: 31.0%), with anal preservation rates of 39.6%–51.4% (mean: 45.3%). However, overall survival did not significantly improve.
Conclusion: Integrating advanced techniques such as intensity-modulated radiation therapy (IMRT) with HDR-EBT shows promise. This approach particularly benefits patients ineligible for surgery or those adopting a watch-and-wait strategy after complete clinical remission, thus highlighting the potential of HDR-EBT in this treatment landscape.
Oral Squamous Cell Carcinoma (OSCC) is the most prevalent form of oral cancer, constituting over 90% of reported cases. This malignancy commonly infiltrates bone, making bone invasion a significant clinical issue. OSCC may invade bone via either an infiltrative or erosive pattern, with the pattern of invasion closely correlating with the clinical behavior of the disease and potentially holding prognostic value. Typically, OSCC spreads to the mandibular bone through direct infiltration of the alveolar ridge or lingual cortical plate. Interestingly, only 6% of OSCC cases initially present with a primary tumor, necessitating comprehensive whole-body imaging and clinical examinations to exclude other primary tumors. This report details a rare case of long-standing OSCC of the retromolar pad, which led to infiltrative osteolysis of the mandible, culminating in the near-total disappearance of the bone in a 47-year-old male patient.