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BACKGROUND: Primary meningeal melanocytic tumors are ultra-rare entities with distinct histological and molecular features compared with other melanocytic or pigmented lesions, such as brain and leptomeningeal metastases from metastatic melanoma. METHODS: The European Network for Rare Cancers (EURACAN) Task Force on Ultra-Rare Brain Tumors (domain 10, subdomain 10) performed a literature review from January 1985 to December 2023 regarding the epidemiologic and clinical characteristics, histological and molecular features, radiological findings, and efficacy of local treatments (surgery and radiotherapy) and systemic treatments for these entities. RESULTS: Molecular analysis can detect specific mutations, including GNAQ, GNA11, SF3B1, EIF1AX, BAP1, that are typically found in circumscribed primary meningeal melanocytic tumors and not in other melanocytic lesions, whereas NRAS and BRAF mutations are typical for diffuse primary meningeal melanocytic tumors. The neuroimaging of the whole neuroaxis suggests a melanocytic nature of a lesion, depicts its circumscribed or diffuse nature, but cannot predict the tumor's aggressiveness. Gross-total resection is the first choice in the case of circumscribed meningeal melanocytoma and melanoma; conversely, meningeal biopsy may be reserved for patients with diffuse and multinodular leptomeningeal spread to achieve a definitive diagnosis. High-dose radiotherapy is rarely indicated in diffuse melanocytic tumors except as palliative treatment to alleviate symptoms. Last, a definitive advantage of a specific systemic treatment could not be concluded, as most of the data available derive from case reports or small cohorts. CONCLUSIONS: As primary meningeal melanocytic tumors are extremely rare, the correlations between the clinical characteristics, molecular profile, radiological findings at diagnosis and progression are weak, and poor evidence on the best therapeutic approach is available. There is a need to develop shared platforms and registries to capture more knowledge regarding these ultra-rare entities.
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OBJECTIVES: This work aims to explore the impact of multicenter data heterogeneity on deep learning brain metastases (BM) autosegmentation performance, and assess the efficacy of an incremental transfer learning technique, namely learning without forgetting (LWF), to improve model generalizability without sharing raw data. MATERIALS AND METHODS: A total of six BM datasets from University Hospital Erlangen (UKER), University Hospital Zurich (USZ), Stanford, UCSF, New York University (NYU), and BraTS Challenge 2023 were used. First, the performance of the DeepMedic network for BM autosegmentation was established for exclusive single-center training and mixed multicenter training, respectively. Subsequently privacy-preserving bilateral collaboration was evaluated, where a pretrained model is shared to another center for further training using transfer learning (TL) either with or without LWF. RESULTS: For single-center training, average F1 scores of BM detection range from 0.625 (NYU) to 0.876 (UKER) on respective single-center test data. Mixed multicenter training notably improves F1 scores at Stanford and NYU, with negligible improvement at other centers. When the UKER pretrained model is applied to USZ, LWF achieves a higher average F1 score (0.839) than naive TL (0.570) and single-center training (0.688) on combined UKER and USZ test data. Naive TL improves sensitivity and contouring accuracy, but compromises precision. Conversely, LWF demonstrates commendable sensitivity, precision and contouring accuracy. When applied to Stanford, similar performance was observed. CONCLUSION: Data heterogeneity (e.g., variations in metastases density, spatial distribution, and image spatial resolution across centers) results in varying performance in BM autosegmentation, posing challenges to model generalizability. LWF is a promising approach to peer-to-peer privacy-preserving model training.
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Brain Neoplasms , Deep Learning , Humans , Brain Neoplasms/secondary , Brain Neoplasms/radiotherapy , PrivacyABSTRACT
BACKGROUND AND PURPOSE: Current radiotherapy guidelines rely heavily on imaging-based monitoring. Liquid biopsy monitoring promises to complement imaging by providing frequent systemic information about the tumor. In particular, cell-free DNA (cfDNA) sequencing offers a tumor-agnostic approach, which lends itself to monitoring heterogeneous cohorts of cancer patients. METHODS: We collected plasma cfDNA from oligometastatic patients (OMD) and head-and-neck cancer patients (SCCHN) at six time points before, during, and after radiotherapy, and compared them to the plasma samples of healthy and polymetastatic volunteers. We performed low-pass (on average 7x) whole-genome sequencing on 93 plasma cfDNA samples and correlated copy number alterations and fragment length distributions to clinical and imaging findings. RESULTS: We observed copy number alterations in 4/7 polymetastatic cancer patients, 1/7 OMD and 1/7 SCCHN patients, these patients' imaging showed progression following radiotherapy. Using unsupervised learning, we identified cancer-specific fragment length features that showed a strong correlation with copy number-based tumor fraction estimates. In 4/4 HPV-positive SCCHN patient samples, we detected viral DNA that enabled the monitoring of very low tumor fraction samples. CONCLUSIONS: Our results indicate that an elevated tumor fraction is associated with tumor aggressiveness and systemic tumor spread. This information may be used to adapt treatment strategies. Further, we show that by detecting specific sequences such as viral DNA, the sensitivity of detecting cancer from cell-free DNA sequencing data can be greatly increased.
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Cell-Free Nucleic Acids , Head and Neck Neoplasms , Whole Genome Sequencing , Humans , Head and Neck Neoplasms/radiotherapy , Head and Neck Neoplasms/genetics , Head and Neck Neoplasms/pathology , Head and Neck Neoplasms/blood , Cell-Free Nucleic Acids/blood , Male , Female , Middle Aged , Aged , DNA Copy Number Variations , Radiotherapy Dosage , Adult , Squamous Cell Carcinoma of Head and Neck/radiotherapy , Squamous Cell Carcinoma of Head and Neck/genetics , Squamous Cell Carcinoma of Head and Neck/bloodABSTRACT
Melanoma, the deadliest form of skin cancer, has seen a steady increase in incidence rates worldwide, posing a significant challenge to dermatologists. Early detection is crucial for improving patient survival rates. However, performing total body screening (TBS), i.e., identifying suspicious lesions or ugly ducklings (UDs) by visual inspection, can be challenging and often requires sound expertise in pigmented lesions. To assist users of varying expertise levels, an artificial intelligence (AI) decision support tool was developed. Our solution identifies and characterizes UDs from real-world wide-field patient images. It employs a state-of-the-art object detection algorithm to locate and isolate all skin lesions present in a patient's total body images. These lesions are then sorted based on their level of suspiciousness using a self-supervised AI approach, tailored to the specific context of the patient under examination. A clinical validation study was conducted to evaluate the tool's performance. The results demonstrated an average sensitivity of 95% for the top-10 AI-identified UDs on skin lesions selected by the majority of experts in pigmented skin lesions. The study also found that the tool increased dermatologists' confidence when formulating a diagnosis, and the average majority agreement with the top-10 AI-identified UDs reached 100% when assisted by our tool. With the development of this AI-based decision support tool, we aim to address the shortage of specialists, enable faster consultation times for patients, and demonstrate the impact and usability of AI-assisted screening. Future developments will include expanding the dataset to include histologically confirmed melanoma and validating the tool for additional body regions.
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Early Detection of Cancer , Melanoma , Skin Neoplasms , Supervised Machine Learning , Humans , Skin Neoplasms/diagnosis , Melanoma/diagnosis , Early Detection of Cancer/methods , Artificial Intelligence , Algorithms , Male , Female , Skin/pathologyABSTRACT
Background and Purpose: The feasibility of acquiring diffusion-weighted imaging (DWI) images on an MR-Linac for quantitative response assessment during radiotherapy was explored. DWI data obtained with a Spin Echo Echo Planar Imaging sequence adapted for a 0.35 T MR-Linac were examined and compared with DWI data from a conventional 3 T scanner. Materials and Methods: Apparent diffusion coefficient (ADC) measurements and a distortion correction technique were investigated using DWI-calibrated phantoms and in the brains of seven volunteers. All DWI utilized two phase-encoding directions for distortion correction and off-resonance field estimation. ADC maps in the brain were analyzed for automatically segmented normal tissues. Results: Phantom ADC measurements on the MR-Linac were within a 3 % margin of those recorded by the 3 T scanner. The maximum distortion observed in the phantom was 2.0 mm prior to correction and 1.1 mm post-correction on the MR-Linac, compared to 6.0 mm before correction and 3.6 mm after correction at 3 T. In vivo, the average ADC values for gray and white matter exhibited variations of 14 % and 4 %, respectively, for different selections of b-values on the MR-Linac. Distortions in brain images before correction, estimated through the off-resonance field, reached 2.7 mm on the MR-Linac and 12 mm at 3 T. Conclusion: Accurate ADC measurements are achievable on a 0.35 T MR-Linac, both in phantom and in vivo. The selection of b-values significantly influences ADC values in vivo. DWI on the MR-Linac demonstrated lower distortion levels, with a maximum distortion reduced to 1.1 mm after correction.
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BACKGROUND: Surgical resection is the standard of care for patients with large or symptomatic brain metastases (BMs). Despite improved local control after adjuvant stereotactic radiotherapy, the risk of local failure (LF) persists. Therefore, we aimed to develop and externally validate a pre-therapeutic radiomics-based prediction tool to identify patients at high LF risk. METHODS: Data were collected from A Multicenter Analysis of Stereotactic Radiotherapy to the Resection Cavity of Brain Metastases (AURORA) retrospective study (training cohort: 253 patients from two centers; external test cohort: 99 patients from five centers). Radiomic features were extracted from the contrast-enhancing BM (T1-CE MRI sequence) and the surrounding edema (FLAIR sequence). Different combinations of radiomic and clinical features were compared. The final models were trained on the entire training cohort with the best parameter set previously determined by internal 5-fold cross-validation and tested on the external test set. RESULTS: The best performance in the external test was achieved by an elastic net regression model trained with a combination of radiomic and clinical features with a concordance index (CI) of 0.77, outperforming any clinical model (best CI: 0.70). The model effectively stratified patients by LF risk in a Kaplan-Meier analysis (p < 0.001) and demonstrated an incremental net clinical benefit. At 24 months, we found LF in 9% and 74% of the low and high-risk groups, respectively. CONCLUSIONS: A combination of clinical and radiomic features predicted freedom from LF better than any clinical feature set alone. Patients at high risk for LF may benefit from stricter follow-up routines or intensified therapy.
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Background and purpose: Magnetic resonance imaging (MRI) scans are highly sensitive to acquisition and reconstruction parameters which affect feature stability and model generalizability in radiomic research. This work aims to investigate the effect of image pre-processing and harmonization methods on the stability of brain MRI radiomic features and the prediction performance of radiomic models in patients with brain metastases (BMs). Materials and methods: Two T1 contrast enhanced brain MRI data-sets were used in this study. The first contained 25 BMs patients with scans at two different time points and was used for features stability analysis. The effect of gray level discretization (GLD), intensity normalization (Z-score, Nyul, WhiteStripe, and in house-developed method named N-Peaks), and ComBat harmonization on features stability was investigated and features with intraclass correlation coefficient >0.8 were considered as stable. The second data-set containing 64 BMs patients was used for a classification task to investigate the informativeness of stable features and the effects of harmonization methods on radiomic model performance. Results: Applying fixed bin number (FBN) GLD, resulted in higher number of stable features compare to fixed bin size (FBS) discretization (10 ± 5.5 % higher). `Harmonization in feature domain improved the stability for non-normalized and normalized images with Z-score and WhiteStripe methods. For the classification task, keeping the stable features resulted in good performance only for normalized images with N-Peaks along with FBS discretization. Conclusions: To develop a robust MRI based radiomic model we recommend using an intensity normalization method based on a reference tissue (e.g N-Peaks) and then using FBS discretization.
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INTRODUCTION: The international phase II single-arm LungTech trial 22113-08113 of the European Organization for Research and Treatment of Cancer assessed the safety and efficacy of stereotactic body radiotherapy (SBRT) in patients with centrally located early-stage NSCLC. METHODS: Patients with inoperable non-metastatic central NSCLC (T1-T3 N0 M0, ≤7cm) were included. After prospective central imaging review and radiation therapy quality assurance for any eligible patient, SBRT (8 × 7.5 Gy) was delivered. The primary endpoint was freedom from local progression probability three years after the start of SBRT. RESULTS: The trial was closed early due to poor accrual related to repeated safety-related pauses in recruitment. Between August 2015 and December 2017, 39 patients from six European countries were included and 31 were treated per protocol and analyzed. Patients were mainly male (58%) with a median age of 75 years. Baseline comorbidities were mainly respiratory (68%) and cardiac (48%). Median tumor size was 2.6 cm (range 1.2-5.5) and most cancers were T1 (51.6%) or T2a (38.7%) N0 M0 and of squamous cell origin (48.4%). Six patients (19.4%) had an ultracentral tumor location. The median follow-up was 3.6 years. The rates of 3-year freedom from local progression and overall survival were 81.5% (90% confidence interval [CI]: 62.7%-91.4%) and 61.1% (90% CI: 44.1%-74.4%), respectively. Cumulative incidence rates of local, regional, and distant progression at three years were 6.7% (90% CI: 1.6%-17.1%), 3.3% (90% CI: 0.4%-12.4%), and 29.8% (90% CI: 16.8%-44.1%), respectively. SBRT-related acute adverse events and late adverse events ≥ G3 were reported in 6.5% (n = 2, including one G5 pneumonitis in a patient with prior interstitial lung disease) and 19.4% (n = 6, including one lethal hemoptysis after a lung biopsy in a patient receiving anticoagulants), respectively. CONCLUSIONS: The LungTech trial suggests that SBRT with 8 × 7.5Gy for central lung tumors in inoperable patients is associated with acceptable local control rates. However, late severe adverse events may occur after completion of treatment. This SBRT regimen is a viable treatment option after a thorough risk-benefit discussion with patients. To minimize potentially fatal toxicity, careful management of dose constraints, and post-SBRT interventions is crucial.
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The purpose of this European Society for Radiotherapy and Oncology (ESTRO) project, endorsed by the European Association of Urology, is to explore expert opinion on the management of patients with oligometastatic and oligoprogressive renal cell carcinoma by means of stereotactic ablative radiotherapy (SABR) on extracranial metastases, with the aim of developing consensus recommendations for patient selection, treatment doses, and concurrent systemic therapy. A questionnaire on SABR in oligometastatic renal cell carcinoma was prepared by a core group and reviewed by a panel of ten prominent experts in the field. The Delphi consensus methodology was applied, sending three rounds of questionnaires to clinicians identified as key opinion leaders in the field. At the end of the third round, participants were able to find consensus on eight of the 37 questions. Specifically, panellists agreed to apply no restrictions regarding age (25 [100%) of 25) and primary renal cell carcinoma histology (23 [92%] of 25) for SABR candidates, on the upper threshold of three lesions to offer ablative treatment in patients with oligoprogression, and on the concomitant administration of immune checkpoint inhibitor. SABR was indicated as the treatment modality of choice for renal cell carcinoma bone oligometatasis (20 [80%] of 25) and for adrenal oligometastases 22 (88%). No consensus or major agreement was reached regarding the appropriate schedule, but the majority of the poll (54%-58%) retained the every-other-day schedule as the optimal choice for all the investigated sites. The current ESTRO Delphi consensus might provide useful direction for the application of SABR in oligometastatic renal cell carcinoma and highlight the key areas of ongoing debate, perhaps directing future research efforts to close knowledge gaps.
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Carcinoma, Renal Cell , Consensus , Delphi Technique , Kidney Neoplasms , Radiosurgery , Humans , Male , Carcinoma, Renal Cell/radiotherapy , Carcinoma, Renal Cell/secondary , Carcinoma, Renal Cell/pathology , Disease Progression , Europe , Kidney Neoplasms/pathology , Kidney Neoplasms/radiotherapy , Neoplasm Metastasis , Radiosurgery/standards , Urology/standardsABSTRACT
Background and Purpose: Standard imaging protocols can guarantee the spatial integrity of magnetic resonance (MR) images utilized in radiotherapy. However, the presence of metallic implants can significantly compromise this integrity. Our proposed method aims at characterizing the geometric distortions induced by both passive and active implants commonly encountered in planning images obtained from a low-field 0.35 T MR-linear accelerator (LINAC). Materials and Methods: We designed a spatial integrity phantom defining 1276 control points and covering a field of view of 20x20x20 cm3. This phantom was scanned in a water tank with and without different implants used in hip and shoulder arthroplasty procedures as well as with active cardiac stimulators. The images were acquired with the clinical planning sequence (balanced steady-state free-precession, resolution 1.5x1.5x1.5 mm3). Spatial integrity was assessed by the Euclidian distance between the control point detected on the image and their theoretical locations. A first plane free of artefact (FPFA) was defined to evaluate the spatial integrity beyond the larger banding artefact. Results: In the region extending up to 20 mm from the largest banding artefacts, the tested passive and active implants could cause distortions up to 2 mm and 3 mm, respectively. Beyond this region the spatial integrity was recovered and the image could be considered as unaffected by the implants. Conclusions: We characterized the impact of common implants on a low field MR-LINAC planning sequence. These measurements could support the creation of extra margin while contouring organs at risk and target volumes in the vicinity of implants.
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Background: Oligoprogression is defined as cancer progression of a limited number of metastases under active systemic therapy. The role of metastasis-directed therapy, using stereotactic body radiotherapy (SBRT), is controversial as is the continuation versus switch of systemic therapy. We report outcomes of oligoprogressive patients after SBRT, and compare those patients that continued or switched their current line of systemic therapy. Material/Methods: We included patients who developed up to 5 progressive extracranial metastases under systemic therapy for any solid organ malignancy and were treated with SBRT to all lesions at our institution between 01/2014 and 12/2019. Overall survival (OS) and progression-free survival (PFS) were analyzed using the Kaplan-Meier method, and the interval to the next systemic therapy line determined using cumulative incidence functions. Multivariable Cox regression models were used to analyze the influence of baseline and post-progression variables on OS, PFS and survival with the next systemic therapy after SBRT. Results: Among 135 patients with oligoprogressive disease of which the most common primary tumor was lung cancer (n = 46, 34.1 %), 96 continued their current line of systemic therapy after oligoprogression. Among 39 who switched systemic therapy, 28 (71.8 %) paused or discontinued, while 11 (28.2 %) immediately started another systemic treatment. After a median follow-up of 27.2 months, patients that switched and those who continued systemic therapy after oligoprogression had comparable median OS (32.1 vs. 38.2 months, p = 0.47) and PFS (4.3 vs. 3.4 months, p = 0.6). The intervals to the next systemic therapy line were comparable between both cohorts (p = 0.6). An ECOG performance status of 2 and immediately starting a new systemic therapy after oligoprogression were associated with a poorer survival without next systemic therapy, while the de-novo OMD state was associated with better survival without next systemic therapy compared to the induced state. Conclusion: Oncological outcomes of patients that continued or switched systemic therapy after SBRT for oligoprogression were comparable, potentially indicating that further lines of treatment may be safely delayed in selected cases.
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BACKGROUND AND PURPOSE: Studies investigating the application of Artificial Intelligence (AI) in the field of radiotherapy exhibit substantial variations in terms of quality. The goal of this study was to assess the amount of transparency and bias in scoring articles with a specific focus on AI based segmentation and treatment planning, using modified PROBAST and TRIPOD checklists, in order to provide recommendations for future guideline developers and reviewers. MATERIALS AND METHODS: The TRIPOD and PROBAST checklist items were discussed and modified using a Delphi process. After consensus was reached, 2 groups of 3 co-authors scored 2 articles to evaluate usability and further optimize the adapted checklists. Finally, 10 articles were scored by all co-authors. Fleiss' kappa was calculated to assess the reliability of agreement between observers. RESULTS: Three of the 37 TRIPOD items and 5 of the 32 PROBAST items were deemed irrelevant. General terminology in the items (e.g., multivariable prediction model, predictors) was modified to align with AI-specific terms. After the first scoring round, further improvements of the items were formulated, e.g., by preventing the use of sub-questions or subjective words and adding clarifications on how to score an item. Using the final consensus list to score the 10 articles, only 2 out of the 61 items resulted in a statistically significant kappa of 0.4 or more demonstrating substantial agreement. For 41 items no statistically significant kappa was obtained indicating that the level of agreement among multiple observers is due to chance alone. CONCLUSION: Our study showed low reliability scores with the adapted TRIPOD and PROBAST checklists. Although such checklists have shown great value during development and reporting, this raises concerns about the applicability of such checklists to objectively score scientific articles for AI applications. When developing or revising guidelines, it is essential to consider their applicability to score articles without introducing bias.
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Artificial Intelligence , Checklist , Delphi Technique , Radiotherapy Planning, Computer-Assisted , Humans , Radiotherapy Planning, Computer-Assisted/methods , Radiotherapy Planning, Computer-Assisted/standards , Practice Guidelines as Topic , Bias , Reproducibility of Results , Neoplasms/radiotherapyABSTRACT
Background and Purpose: To improve radiotherapy (RT) planning efficiency and plan quality, knowledge-based planning (KBP) and deep learning (DL) solutions have been developed. We aimed to make a direct comparison of these models for breast cancer planning using the same training, validation, and testing sets. Materials and Methods: Two KBP models were trained and validated with 90 RT plans for left-sided breast cancer with 15 fractions of 2.6 Gy. The versions either used the full dataset (non-clean model) or a cleaned dataset (clean model), thus eliminating geometric and dosimetric outliers. Results were compared with a DL U-net model (previously trained and validated with the same 90 RT plans) and manually produced RT plans, for the same independent dataset of 15 patients. Clinically relevant dose volume histogram parameters were evaluated according to established consensus criteria. Results: Both KBP models underestimated the mean heart and lung dose equally 0.4 Gy (0.3-1.1 Gy) and 1.4 Gy (1.1-2.8 Gy) compared to the clinical plans 0.8 Gy (0.5-1.8 Gy) and 1.7 Gy (1.3-3.2 Gy) while in the final calculations the mean lung dose was higher 1.9-2.0 Gy (1.5-3.5 Gy) for both KPB models. The U-Net model resulted in a mean planning target volume dose of 40.7 Gy (40.4-41.3 Gy), slightly higher than the clinical plans 40.5 Gy (40.1-41.0 Gy). Conclusions: Only small differences were observed between the estimated and final dose calculation and the clinical results for both KPB models and the DL model. With a good set of breast plans, the data cleaning module is not needed and both KPB and DL models lead to clinically acceptable results.
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Introduction and background: Metastatic disease has been proposed as a continuum, with no clear cut-off between oligometastatic and polymetastatic disease. This study aims to quantify tumor burden and patterns of spread in unselected metastatic cancer patients referred for PET-based staging, response assessment of restaging. Materials and methods: All oncological fluorodeoxyglucose (FDG-) and prostate-specific membrane antigen (PSMA-) positron emission tomography (PET) scans conducted at a single academic center in 2020 were analyzed. Imaging reports of all patients with metastatic disease were reviewed and assessed. Results: For this study, 7,000 PET scans were screened. One third of PET scans (n = 1,754; 33 %) from 1,155 unique patients showed presence of metastatic disease from solid malignancies, of which 601 (52 %) and 554 (48 %) were classified as oligometastatic (maximum 5 metastases) and polymetastatic (>5 metastases), respectively. Lung and pleural cancer, skin cancer, and breast cancer were the most common primary tumor histologies with 132 (23.8 %), 88 (15.9 %), and 72 (13.0 %) cases, respectively. Analysis of the number of distant metastases showed a strong bimodal distribution of the metastatic burden with 26 % of patients having one solitary metastasis and 43 % of patients harboring >10 metastases. Yet, despite 43 % of polymetastatic patients having >10 distant metastases, their pattern of distribution was restricted to one or two organs in about two thirds of patients, and there was no association between the number of distant metastases and the number of involved organs. Conclusion: The majority of metastatic cancer patients are characterized by either a solitary metastasis or a high tumor burden with >10 metastases, the latter was often associated with affecting a limited number of organs. These findings support both the spectrum theory of metastasis and the seed and soil hypothesis and can support in designing the next generation of clinical trials in the field of oligometastatic disease.
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Introduction and background: While recurrent glioblastoma patients are often treated with re-irradiation, there is limited data on the use of re-irradiation in the setting of bevacizumab (BEV), temozolomide (TMZ) re-challenge, or immune checkpoint inhibition (ICI). We describe target delineation in patients with prior anti-angiogenic therapy, assess safety and efficacy of re-irradiation, and evaluate patterns of recurrence. Materials and methods: Patients with a histologically confirmed diagnosis of glioblastoma treated at a single institution between 2013 and 2021 with re-irradiation were included. Tumor, treatment and clinical data were collected. Logistic and Cox regression analysis were used for statistical analysis. Results: One hundred and seventeen recurrent glioblastoma patients were identified, receiving 129 courses of re-irradiation. In 66 % (85/129) of cases, patients had prior BEV. In the 80 patients (62 %) with available re-irradiation plans, 20 (25 %) had all T2/FLAIR abnormality included in the gross tumor volume (GTV). Median overall survival (OS) for the cohort was 7.3 months, and median progression-free survival (PFS) was 3.6 months. Acute CTCAE grade ≥ 3 toxicity occurred in 8 % of cases. Concurrent use of TMZ or ICI was not associated with improved OS nor PFS. On multivariable analysis, higher KPS was significantly associated with longer OS (p < 0.01). On subgroup analysis, patients with prior BEV had significantly more marginal recurrences than those without (26 % vs. 13 %, p < 0.01). Conclusion: Re-irradiation can be safely employed in recurrent glioblastoma patients. Marginal recurrence was more frequent in patients with prior BEV, suggesting a need to consider more inclusive treatment volumes incorporating T2/FLAIR abnormality.
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â¢Stereotactic body radiation therapy (SBRT) for ultra-central lung tumors is associated with high toxicity rates.â¢To evaluate differences in radiosensitivity within the proximal bronchial tree (PBT), the PBT was sub-segmented into seven anatomical sections.â¢A risk-adapted SBRT regimen of EQD2_10 = 54.4 Gy in 8 or 10 fractions results in excellent local control and low rates of severe toxicity.â¢Data from a recent meta-analysis, the NORDIC Hilus trial and dosimetric data from this study were combined to create a NTCP model.â¢A dose threshold of EQD2_3 = 100 Gy to the PBT or any of its subsegments is expected to result in low rates of severe bronchial toxicity.
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Magnetic resonance image guided radiation therapy (MRIgRT) is a relatively new technology that has already shown outcomes benefits but that has not yet reached its clinical potential. The improved soft-tissue contrast provided with MR, coupled with the immediacy of image acquisition with respect to the treatment, enables expansion of on-table adaptive protocols, currently at a cost of increased treatment complexity, use of human resources, and longer treatment slot times, which translate to decreased throughput. Many approaches are being investigated to meet these challenges, including the development of artificial intelligence (AI) algorithms to accelerate and automate much of the workflow and improved technology that parallelizes workflow tasks, as well as improvements in image acquisition speed and quality. This article summarizes limitations of current available integrated MRIgRT systems and gives an outlook about scientific developments to further expand the use of MRIgRT.
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Artificial Intelligence , Radiotherapy, Image-Guided , Humans , Radiotherapy, Image-Guided/methods , Magnetic Resonance Imaging/methods , Algorithms , WorkflowABSTRACT
Radiotherapy in expiration breath-hold (EBH) has the potential to reduce treatment volumes of abdominal targets compared to an internal target volume concept in free-breathing. The reproducibility of EBH and required safety margins were investigated to quantify this volumetric benefit. Pre- and post-treatment diaphragm position difference and the positioning variability were determined on computed tomography. Systematic and random errors for EBH position reproducibility and positioning variability were calculated, resulting in margins of 7 to 12 mm depending on the prescription isodose and fractionation. A reduced volume was shown for EBH for lesions with superior-inferior breathing motion above 4 to 8 mm.
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Treatment for glioblastoma, an aggressive brain tumour usually relies on radiotherapy. This involves planning how to achieve the desired radiation dose distribution, which is known as treatment planning. Treatment planning is impacted by human errors, inter-expert variability in segmenting (or outlining) the tumor target and organs-at-risk, and differences in segmentation protocols. Erroneous segmentations translate to erroneous dose distributions, and hence sub-optimal clinical outcomes. Reviewing segmentations is time-intensive, significantly reduces the efficiency of radiation oncology teams, and hence restricts timely radiotherapy interventions to limit tumor growth. Moreover, to date, radiation oncologists review and correct segmentations without information on how potential corrections might affect radiation dose distributions, leading to an ineffective and suboptimal segmentation correction workflow. In this paper, we introduce an automated deep-learning based method: atomic surface transformations for radiotherapy quality assurance (ASTRA), that predicts the potential impact of local segmentation variations on radiotherapy dose predictions, thereby serving as an effective dose-aware sensitivity map of segmentation variations. On a dataset of 100 glioblastoma patients, we show how the proposed approach enables assessment and visualization of areas of organs-at-risk being most susceptible to dose changes, providing clinicians with a dose-informed mechanism to review and correct segmentations for radiation therapy planning. These initial results suggest strong potential for employing such methods within a broader automated quality assurance system in the radiotherapy planning workflow. Code to reproduce this is available at https://github.com/amithjkamath/astraClinical Relevance: ASTRA shows promise in indicating what regions of the OARs are more likely to impact the distribution of radiation dose.
Subject(s)
Brain Neoplasms , Glioblastoma , Radiation Oncology , Humans , Glioblastoma/radiotherapy , Radiotherapy Planning, Computer-Assisted/methods , Brain Neoplasms/diagnostic imaging , Brain Neoplasms/radiotherapy , Organs at RiskABSTRACT
BACKGROUND: Stereotactic arrhythmia radioablation (STAR) is delivered with a planning target volume (PTV) prescription dose of 25 Gy, mostly to the surrounding 75-85% isodose line. This means that the average and maximum dose received by the target is less than 35 Gy, which is the minimum threshold required to create a homogenous transmural fibrosis. Similar to catheter ablation, the primary objective of STAR should be transmural fibrosis to prevent heterogenous intracardiac conduction velocities and the occurrence of sustained ventricular arrhythmias (sVA) caused by reentry. We hypothesize that the current dose prescription used in STAR is inadequate for the long-term prevention of sVA and that a significant increase in dose is necessary to induce transmural scar formation. OBJECTIVE: A single arm, multi-center, phase II, dose escalation prospective clinical trial employing the i3 + 3 design is being conducted to examine the safety of a radiation dose-escalation strategy aimed at inducing transmural scar formation. The ultimate objective of this trial is to decrease the likelihood of sVA recurrence in patients at risk. METHODS: Patients with ischemic or non-ischemic cardiomyopathy and recurrent sVA, with an ICD and history of ≥ 1 catheter ablation for sVA will be included. This is a prospective, multicenter, one-arm, dose-escalation trial utilizing the i3 + 3 design, a modified 3 + 3 specifically created to overcome limitations in traditional dose-finding studies. A total of 15 patients will be recruited. The trial aims to escalate the ITV dose from 27.0 Gy to an ITV prescription dose-equivalent level of maximum 35.1 Gy by keeping the PTV prescription dose constant at 25 Gy while increasing the dose to the target (i.e. the VT substrate without PTV margin) by step-wise reduction of the prescribing isodose line (85% down to 65%). The primary outcome of this trial is safety measured by registered radiation associated adverse events (AE) up to 90 days after study intervention including radiation associated serious adverse events graded as at least 4 or 5 according to CTCAE v5, radiation pneumonitis or pericarditis requiring hospitalization and decrease in LVEF ≥ 10% as assessed by echocardiography or cardiac MRI at 90 days after STAR. The sample size was determined assuming an acceptable primary outcome event rate of 20%. Secondary outcomes include sVA burden at 6 months after STAR, time to first sVA recurrence, reduction in appropriate ICD therapies, the need for escalation of antiarrhythmic drugs, non-radiation associated safety and patient reported outcome measures such as SF-36 and EQ5D. DISCUSSION: DEFT-STAR is an innovative prospective phase II trial that aims to evaluate the optimal radiation dose for STAR in patients with therapy-refractory sVA. The trial has obtained IRB approval and focuses on determining the safe and effective radiation dose to be employed in the STAR procedure. TRIAL REGISTRATION: NCT05594368.