Coverage with evidence development program on stereotactic body radiotherapy in Belgium (2013-2019): a nationwide registry-based prospective study

Background

Although stereotactic body radiotherapy (SBRT) was progressively adopted in clinical practice in Belgium, a reimbursement request in 2011 was not granted because of remaining clinical and economic uncertainty. A coverage with evidence development (CED) program on SBRT started in 2013, with the aim to assess clinical and technical patterns-of-care in Belgium and monitor survival per indication, in view of supporting inclusion in the reimbursement system.

Methods

The Belgian National Institute for Health and Disability Insurance (NIHDI) initiated this prospective observational registry. Participating departments, using SBRT in clinical practice, signed the 'NIHDI convention'. Eligible patients had a primary tumour (PT) or oligometastatic disease (OMD). Patient, tumour, and treatment characteristics were collected through an online module of the Belgian Cancer Registry, prerequisite for financing. Five-year overall survival (5YOS) and 30- and 90-days mortality were primary outcomes, derived from vital status information.

Findings

Between 10/2013 and 12/2019, 20 of the 24 accredited radiotherapy departments participated, 6 were academic. Registered cases per department ranged from 21 to 867. Of 5675 registrations analysed, the majority had good performance status and limited number of lesions. Enrolment of PTs remained stable over time, OMDs almost doubled. Peripheral lung lesions dominated in PTs as in OMDs. Other metastases were (para)spinal, 'non-standard' and hepatic. Thirty- and 90-days mortalities remained below 0.5% [95% CI 0.3%-0.8%] respectively 2.1% [95% CI 1.6%-2.7%]. 5YOS varied by indication, primary prostate patients performing best (85%, 95% CI [76%, 96%]), those with liver metastases worst (19%, 95% CI [15%, 24%]). Better OS was observed in academic departments, department size did not significantly impact survival. OMD survival was better in 2018-19.

Interpretation

CED can be used to define patterns-of-care and real-life outcome of innovative radiotherapy. As the observed survival for different indications was in line with outcome in emerging literature, SBRT was included in the Belgian reimbursement system as of January 2020.

Funding

NIHDI financed participating departments per registered case.

MR signature matching (MRSIGMA) implementation for true real-time free-breathing volumetric imaging with sub-200 ms latency on an MR-Linac

PURPOSE

Develop a true real-time implementation of MR signature matching (MRSIGMA) for free-breathing 3D MRI with sub-200 ms latency on the Elekta Unity 1.5T MR-Linac.

METHODS

MRSIGMA was implemented on an external computer with a network connection to the MR-Linac. Stack-of-stars with partial kz sampling was used to accelerate data acquisition and ReconSocket was employed for simultaneous data transmission. Movienet network computed the 4D MRI motion dictionary and correlation analysis was used for signature matching. A programmable 4D MRI phantom was utilized to evaluate MRSIGMA with respect to a ground-truth translational motion reference. In vivo validation was performed on patients with pancreatic cancer, where 15 patients were employed to train Movienet and 7 patients to test the real-time implementation of MRSIGMA. Dice coefficients between real-time MRSIGMA and a retrospectively computed 4D reference were used to evaluate motion tracking performance.

RESULTS

Motion dictionary was computed in under 5 s. Signature acquisition and matching presented 173 ms latency on the phantom and 193 ms on patients. MRSIGMA presented a mean error of 1.3-1.6 mm for all phantom experiments, which was below the 2 mm acquisition resolution along the motion direction. The Dice coefficient over time between MRSIGMA and reference contours was 0.88 ± 0.02 (GTV), 0.87 ± 0.02(duodenum-stomach), and 0.78 ± 0.02(small bowel), demonstrating high motion tracking performance for both tumor and organs at risk.

CONCLUSION

The real-time implementation of MRSIGMA enabled true real-time free-breathing 3D MRI with sub-200 ms imaging latency on a clinical MR-Linac system, which can be used for treatment monitoring, adaptive radiotherapy and dose accumulation mapping in tumors affected by respiratory motion.

A single-center experience of the upright proton therapy for skull-base chordomas and chondrosarcomas: Updated results

Aim

To access efficacy and safety of the upright proton therapy for the skull-base chordomas and chondrosarcomas.

Materials and methods

The study encompasses single-center experience of proton therapy in chordomas (CA) and chondrosarcomas (CSA) of skull-base localization. We evaluate overall survival, local control and toxicity. Tumor response was assessed in accordance with RANO criteria. Treatment-related toxicity was evaluated with the help of CTCAE v 5.0 scale.

Results

Proton therapy in the upright position was utilized for 51pts (patients) with CA-CSA (40 pts with chordoma and 11pts with chondrosarcoma) at the A. Tsyb Medical Radiological Research Center in 2016-2023. Median tumor volume constituted 30 cm3 (IQR (interquartile range) 15-41 cm3). Median total dose was 70 GyRBE. Median number of fractions was 35. Overall survival (OS) at 1-, 2- and 3-year rates reached 98.0 %, 88.6 % and 82.7 %, respectively. Median follow-up time was 36 months. The 1-, 2- and 3-year local control (LC) rates constituted, respectively, 98 %, 78.6 % and 66.3 %. Prior surgery showed statistically significant association with better prognosis (p = 0.023). Brainstem-to-tumor dose coverage compromise became the major pattern of LC failure (p = 0.03). The late radiation toxicity reactions included temporal lobe necrosis grade 2 in 2 pts, xerostomia grade 1 in 1pt, radiation cataract grade 2 in 1pt and persistent headache grade 2 in 4 pts. Severe late toxicity reactions were observed in 2 cases (4 %): 1 myelitis grade 3 and brainstem damage grade 5 in 1pt.

Conclusion

Local control was achieved in the majority of patients receiving the scanning-beam upright proton therapy for skull-base CA-CSA. The LC rates after a surgery-radiotherapy combination treatment were higher compared with irradiation alone. Pattern of failure is mostly brainstem-tumor dose compromise. The high OS and LC rates were accompanied by low toxicity incidence. Even in complex case of the skull base CA-CSA upright proton therapy shows promising clinical outcomes.

The effect of time-delayed contrast-enhanced scanning in determining the gross tumor target volume of large-volume brain metastases

BACKGROUND AND PURPOSE

To assess the variation of large-volume brain metastases (BMs) boundaries and shapes using enhanced magnetic resonance (MR) scanning with different delay times and to provide a basis for determining the gross tumor target volume (GTV) for radiotherapy of BMs.

MATERIALS AND METHODS

We prospectively enrolled 155 patients initially diagnosed with BMs (561 lesions > 1 cm). Contrast-enhanced (CE) T1-weighted imaging scans were performed 1, 3, 5, 10, 18, and 20 min after gadolinium-based contrast agent injection and GTVs were determined as GTV-1min, GTV-3min, GTV-5min, GTV-10min, GTV-18min, and GTV-20min, respectively, which were subsequently fused in different phases. Fusion of the six GTVs was defined as GTV-total, which was set as the reference GTV. The volume, shape, and signal intensity of the GTVs and brain white matter (BWM) were compared at different delay times.

RESULTS

GTV-3min, GTV-5min, GTV-10min, GTV-18min, and GTV-20min volumes increased by 2.2 %, 3.8 %, 6.5 %, 9.5 %, and 10.6 %, respectively (P < 0.05) compared with GTV-1min. Compared with GTV-total, GTV-1min, GTV-3min, GTV-5min, GTV-10min, GTV-18min, and GTV-20min volumes reduced by 25.4 %, 22.1 %, 18.7 %, 15.0 %, 11.2 %, and 10.3 %, respectively (P < 0.05). Compared with GTV-total, 29 (51.8 %) fused GTVs had a volume reduction rate < 5 %, 45 (80.4 %) had a Dice similarity coefficient > 0.95, and all contained GTV-10min, GTV-18min or GTV-20min. The signal intensity ratio between the GTV and BWM peaked at 5 min (0.351 ± 0.24).

CONCLUSION

Enhanced MR scans with different delay times show significant differences in the boundaries and shapes of large-volume BMs, and time-delayed multi-phase CE scanning should be used in GTV determination, with time phases ≥ 10 min being mandatory.

Low-field MRI for use in neurological diseases

PURPOSE OF REVIEW

To review recent clinical uses of low-field magnetic resonance imaging (MRI) to guide incorporation into neurological practice.

RECENT FINDINGS

Use of low-field MRI has been demonstrated in applications including tumours, vascular pathologies, multiple sclerosis, brain injury, and paediatrics. Safety, workflow, and image quality have also been evaluated.

SUMMARY

Low-field MRI has the potential to increase access to critical brain imaging for patients who otherwise may not obtain imaging in a timely manner. This includes areas such as the intensive care unit and emergency room, where patients could be imaged at the point of care rather than be transported to the MRI scanner. Such systems are often more affordable than conventional systems, allowing them to be more easily deployed in resource constrained settings. A variety of systems are available on the market or in a research setting and are currently being used to determine clinical uses for these devices. The utility of such devices must be fully evaluated in clinical scenarios before adoption into standard practice can be achieved. This review summarizes recent clinical uses of low-field MR as well as safety, workflows, and image quality to aid practitioners in assessing this new technology.

A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy

BACKGROUND AND PURPOSE

Artificial Intelligence (AI) models in radiation therapy are being developed with increasing pace. Despite this, the radiation therapy community has not widely adopted these models in clinical practice. A cohesive guideline on how to develop, report and clinically validate AI algorithms might help bridge this gap.

METHODS AND MATERIALS

A Delphi process with all co-authors was followed to determine which topics should be addressed in this comprehensive guideline. Separate sections of the guideline, including Statements, were written by subgroups of the authors and discussed with the whole group at several meetings. Statements were formulated and scored as highly recommended or recommended.

RESULTS

The following topics were found most relevant: Decision making, image analysis, volume segmentation, treatment planning, patient specific quality assurance of treatment delivery, adaptive treatment, outcome prediction, training, validation and testing of AI model parameters, model availability for others to verify, model quality assurance/updates and upgrades, ethics. Key references were given together with an outlook on current hurdles and possibilities to overcome these. 19 Statements were formulated.

CONCLUSION

A cohesive guideline has been written which addresses main topics regarding AI in radiation therapy. It will help to guide development, as well as transparent and consistent reporting and validation of new AI tools and facilitate adoption.

Individualized trajectories in postradiotherapy neurocognitive functioning of patients with brain metastases

Background

The increasing incidence of brain metastases (BMs) and improved survival rates underscore the necessity to investigate the effects of treatments on individuals. The aim of this study was to evaluate the individual trajectories of subjective and objective cognitive performance after radiotherapy in patients with BMs.

Methods

The study population consisted of adult patients with BMs referred for radiotherapy. A semi-structured interview and comprehensive neurocognitive assessment (NCA) were used to assess both subjective and objective cognitive performance before, 3 months and ≥ 11 months after radiotherapy. Reliable change indices were used to identify individual, clinically meaningful changes.

Results

Thirty-six patients completed the 3-month follow-up, and 14 patients completed the ≥ 11-months follow-up. Depending on the domain, subjective cognitive decline was reported by 11-22% of patients. In total, 50% of patients reported subjective decline in at least one cognitive domain. Intracranial progression 3 months postradiotherapy was a risk-factor for self-reported deterioration (P = .031). Objective changes were observed across all domains, with a particular vulnerability for decline in memory at 3 months postradiotherapy. The majority of patients (81%) experienced both a deterioration as well as improvement (eg, mixed response) in objective cognitive functioning. Results were similar for the long-term follow-up (3 to ≥11 months). No risk factors for objective cognitive change 3 months postradiotherapy were identified.

Conclusions

Our study revealed that the majority of patients with BMs will show a mixed cognitive response following radiotherapy, reflecting the complex impact. This underscores the importance of patient-tailored NCAs 3 months postradiotherapy to guide optimal rehabilitation strategies.

Robustness evaluation of pencil beam scanning proton therapy treatment planning: A systematic review

Compared to conventional radiotherapy using X-rays, proton therapy, in principle, allows better conformity of the dose distribution to target volumes, at the cost of greater sensitivity to physical, anatomical, and positioning uncertainties. Robust planning, both in terms of plan optimization and evaluation, has gained high visibility in publications on the subject and is part of clinical practice in many centers. However, there is currently no consensus on the methods and parameters to be used for robust optimization or robustness evaluation. We propose to overcome this deficiency by following the modified Delphi consensus method. This method first requires a systematic review of the literature. We performed this review using the PubMed and Web Of Science databases, via two different experts. Potential conflicts were resolved by a third expert. We then explored the different methods before focusing on clinical studies that evaluate robustness on a significant number of patients. Many robustness assessment methods are proposed in the literature. Some are more successful than others and their implementation varies between centers. Moreover, they are not all statistically or mathematically equivalent. The most sophisticated and rigorous methods have seen more limited application due to the difficulty of their implementation and their lack of widespread availability.

Self-reported checklists and quality scoring tools in radiomics: a meta-research

OBJECTIVE

To evaluate the use of reporting checklists and quality scoring tools for self-reporting purposes in radiomics literature.

METHODS

Literature search was conducted in PubMed (date, April 23, 2023). The radiomics literature was sampled at random after a sample size calculation with a priori power analysis. A systematic assessment for self-reporting, including the use of documentation such as completed checklists or quality scoring tools, was conducted in original research papers. These eligible papers underwent independent evaluation by a panel of nine readers, with three readers assigned to each paper. Automatic annotation was used to assist in this process. Then, a detailed item-by-item confirmation analysis was carried out on papers with checklist documentation, with independent evaluation of two readers.

RESULTS

The sample size calculation yielded 117 papers. Most of the included papers were retrospective (94%; 110/117), single-center (68%; 80/117), based on their private data (89%; 104/117), and lacked external validation (79%; 93/117). Only seven papers (6%) had at least one self-reported document (Radiomics Quality Score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD), or Checklist for Artificial Intelligence in Medical Imaging (CLAIM)), with a statistically significant binomial test (p < 0.001). Median rate of confirmed items for all three documents was 81% (interquartile range, 6). For quality scoring tools, documented scores were higher than suggested scores, with a mean difference of - 7.2 (standard deviation, 6.8).

CONCLUSION

Radiomic publications often lack self-reported checklists or quality scoring tools. Even when such documents are provided, it is essential to be cautious, as the accuracy of the reported items or scores may be questionable.

CLINICAL RELEVANCE STATEMENT

Current state of radiomic literature reveals a notable absence of self-reporting with documentation and inaccurate reporting practices. This critical observation may serve as a catalyst for motivating the radiomics community to adopt and utilize such tools appropriately, thereby fostering rigor, transparency, and reproducibility of their research, moving the field forward.

KEY POINTS

• In radiomics literature, there has been a notable absence of self-reporting with documentation. • Even if such documents are provided, it is critical to exercise caution because the accuracy of the reported items or scores may be questionable. • Radiomics community needs to be motivated to adopt and appropriately utilize the reporting checklists and quality scoring tools.

Radiomics in esophagogastric junction cancer: A scoping review of current status and advances

PURPOSE

This scoping review aimed to understand the advances in radiomics in esophagogastric junction (EGJ) cancer and assess the current status of radiomics in EGJ cancer.

METHODS

We conducted systematic searches of PubMed, Embase, and Web of Science databases from January 18, 2012, to January 15, 2023, to identify radiomics articles related to EGJ cancer. Two researchers independently screened the literature, extracted data, and assessed the quality of the studies using the Radiomics Quality Score (RQS) and the METhodological RadiomICs Score (METRICS) tool, respectively.

RESULTS

A total of 120 articles were retrieved from the three databases, and after screening, only six papers met the inclusion criteria. These studies investigated the role of radiomics in differentiating adenocarcinoma from squamous carcinoma, diagnosing T-stage, evaluating HER2 overexpression, predicting response to neoadjuvant therapy, and prognosis in EGJ cancer. The median score percentage of RQS was 34.7% (range from 22.2% to 38.9%). The median score percentage of METRICS was 71.2% (range from 58.2% to 84.9%).

CONCLUSION

Although there is a considerable difference between the RQS and METRICS scores of the included literature, we believe that the research value of radiomics in EGJ cancer has been revealed. In the future, while actively exploring more diagnostic, prognostic, and biological correlation studies in EGJ cancer, greater emphasis should be placed on the standardization and clinical application of radiomics.

Electromagnetic Modulation of Cell Behavior: Unraveling the Positive Impacts in a Comprehensive Review

There are numerous effective procedures for cell signaling, in which humans directly transmit detectable signals to cells to govern their essential behaviors. From a biomedical perspective, the cellular response to the combined influence of electrical and magnetic fields holds significant promise in various domains, such as cancer treatment, targeted drug delivery, gene therapy, and wound healing. Among these modern cell signaling methods, electromagnetic fields (EMFs) play a pivotal role; however, there remains a paucity of knowledge concerning the effects of EMFs across all wavelengths. It's worth noting that most wavelengths are incompatible with human cells, and as such, this study excludes them from consideration. In this review, we aim to comprehensively explore the most effective and current EMFs, along with their therapeutic impacts on various cell types. Specifically, we delve into the influence of alternating electromagnetic fields (AEMFs) on diverse cell behaviors, encompassing proliferation, differentiation, biomineralization, cell death, and cell migration. Our findings underscore the substantial potential of these pivotal cellular behaviors in advancing the treatment of numerous diseases. Moreover, AEMFs wield a significant role in the realms of biomaterials and tissue engineering, given their capacity to decisively influence biomaterials, facilitate non-invasive procedures, ensure biocompatibility, and exhibit substantial efficacy. It is worth mentioning that AEMFs often serve as a last-resort treatment option for various diseases. Much about electromagnetic fields remains a mystery to the scientific community, and we have yet to unravel the precise mechanisms through which wavelengths control cellular fate. Consequently, our understanding and knowledge in this domain predominantly stem from repeated experiments yielding similar effects. In the ensuing sections of this article, we delve deeper into our extended experiments and research.

Deep Learning for Automatic Gross Tumor Volumes Contouring in Esophageal Cancer Based on Contrast-Enhanced Computed Tomography Images: A Multi-Institutional Study

PURPOSE

To develop and externally validate an automatic artificial intelligence (AI) tool for delineating gross tumor volume (GTV) in patients with esophageal squamous cell carcinoma (ESCC), which can assist in neo-adjuvant or radical radiation therapy treatment planning.

METHODS AND MATERIALS

In this multi-institutional study, contrast-enhanced CT images from 580 eligible ESCC patients were retrospectively collected. The GTV contours delineated by 2 experts via consensus were used as ground truth. A 3-dimensional deep learning model was developed for GTV contouring in the training cohort and internally and externally validated in 3 validation cohorts. The AI tool was compared against 12 board-certified experts in 25 patients randomly selected from the external validation cohort to evaluate its assistance in improving contouring performance and reducing variation. Contouring performance was measured using dice similarity coefficient (DSC) and average surface distance. Additionally, our previously established radiomics model for predicting pathologic complete response was used to compare AI-generated and ground truth contours, to assess the potential of the AI contouring tool in radiomics analysis.

RESULTS

The AI tool demonstrated good GTV contouring performance in multicenter validation cohorts, with median DSC values of 0.865, 0.876, and 0.866 and median average surface distance values of 0.939, 0.789, and 0.875 mm, respectively. Furthermore, the AI tool significantly improved contouring performance for half of 12 board-certified experts (DSC values, 0.794-0.835 vs 0.856-0.881, P = .003-0.048), reduced the intra- and interobserver variations by 37.4% and 55.2%, respectively, and saved contouring time by 77.6%. In the radiomics analysis, 88.7% of radiomic features from ground truth and AI-generated contours demonstrated stable reproducibility, and similar pathologic complete response prediction performance for these contours (P = .430) was observed.

CONCLUSIONS

Our AI contouring tool can improve GTV contouring performance and facilitate radiomics analysis in ESCC patients, which indicates its potential for GTV contouring during radiation therapy treatment planning and radiomics studies.

A Pilot Study of Simulation-Free Hippocampal-Avoidance Whole Brain Radiation Therapy Using Diagnostic MRI-Based and Online Adaptive Planning

PURPOSE

We aimed to demonstrate the clinical feasibility and safety of simulation-free hippocampal avoidance whole brain radiation therapy (HA-WBRT) in a pilot study (National Clinical Trial 05096286).

METHODS AND MATERIALS

Ten HA-WBRT candidates were enrolled for treatment on a commercially available computed tomography (CT)-guided linear accelerator with online adaptive capabilities. Planning structures were contoured on patient-specific diagnostic magnetic resonance imaging (MRI), which were registered to a CT of similar head shape, obtained from an atlas-based database (AB-CT). These patient-specific diagnostic MRI and AB-CT data sets were used for preplan calculation, using NRG-CC001 constraints. At first fraction, AB-CTs were used as primary data sets and deformed to patient-specific cone beam CTs (CBCT) to give patient-matched density information. Brain, ventricle, and brain stem contours were matched through rigid translation and rotation to the corresponding anatomy on CBCT. Lens, optic nerve, and brain contours were manually edited based on CBCT visualization. Preplans were then reoptimized through online adaptation to create final, simulation-free plans, which were used if they met all objectives. Workflow tasks were timed. In addition, patients underwent CT-simulation to create immobilization devices and for prospective dosimetric comparison of simulation-free and simulation-based plans.

RESULTS

Median time from MRI importation to completion of "preplan" was 1 weekday (range, 1-4). Median on-table workflow duration was 41 minutes (range, 34-70). NRG-CC001 constraints were achieved by 90% of the simulation-free plans. One patient's simulation-free plan failed a planning target volume coverage objective (89% instead of 90% coverage); this was deemed acceptable for first-fraction delivery, with an offline replan used for subsequent fractions. Both simulation-free and simulation CT-based plans otherwise met constraints, without clinically meaningful differences.

CONCLUSIONS

Simulation-free HA-WBRT using online adaptive radiation therapy is feasible, safe, and results in dosimetrically comparable treatment plans to simulation CT-based workflows while providing convenience and time savings for patients.

Experience of Implementing Deep Learning-Based Automatic Contouring in Breast Radiation Therapy Planning: Insights From Over 2000 Cases

PURPOSE

This study evaluated the impact and clinical utility of an auto-contouring system for radiation therapy treatments.

METHODS AND MATERIALS

The auto-contouring system was implemented in 2019. We evaluated data from 2428 patients who underwent adjuvant breast radiation therapy before and after the system's introduction. We collected the treatment's finalized contours, which were reviewed and revised by a multidisciplinary team. After implementation, the treatment contours underwent a finalization process that involved manual review and adjustment of the initial auto-contours. For the preimplementation group (n = 369), auto-contours were generated retrospectively. We compared the auto-contours and final contours using the Dice similarity coefficient (DSC) and the 95% Hausdorff distance (HD95).

RESULTS

We analyzed 22,215 structures from final and corresponding auto-contours. The final contours were generally larger, encompassing more slices in the superior or inferior directions. Among organs at risk (OAR), the heart, esophagus, spinal cord, and contralateral breast demonstrated significantly increased DSC and decreased HD95 postimplementation (all P < .05), except for the lungs, which presented inaccurate segmentation. Among target volumes, CTVn_L2, L3, L4, and the internal mammary node showed increased DSC and decreased HD95 postimplementation (all P < .05), although the increase was less pronounced than the OAR outcomes. The analysis also covered factors contributing to significant differences, pattern identification, and outlier detection.

CONCLUSIONS

In our study, the adoption of an auto-contouring system was associated with an increased reliance on automated settings, underscoring its utility and the potential risk of automation bias. Given these findings, we underscore the importance of considering the integration of stringent risk assessments and quality management strategies as a precautionary measure for the optimal use of such systems.

Evaluating stereotactic accuracy with patient-specific MRI distortion corrections for frame-based radiosurgery

PURPOSE

This study examines how MRI distortions affect frame-based SRS treatments and assesses the need for clinical distortion corrections.

METHODS

The study included 18 patients with 80 total brain targets treated using frame-based radiosurgery. Distortion within patients' MRIs were corrected using Cranial Distortion Correction (CDC) software, which utilizes the patient's CT to alter planning MRIs to reduce inherent intra-cranial distortion. Distortion was evaluated by comparing the original planning target volumes (PTVORIG) to targets contoured on corrected MRIs (PTVCORR). To provide an internal control, targets were also re-contoured on uncorrected (PTVRECON) MRIs. Additional analysis was done to assess if 1 mm expansions to PTVORIG targets would compensate for patient-specific distortions. Changes in target volumes, DICE and JACCARD similarity coefficients, minimum PTV dose (Dmin), dose to 95% of the PTV (D95%), and normal tissue receiving 12 Gy (V12Gy), 10 Gy (V10Gy), and 5 Gy (V5Gy) were calculated and evaluated. Student's t-tests were used to determine if changes in PTVCORR were significantly different than intra-contouring variability quantified by PTVRECON.

RESULTS

PTVRECON and PTVCORR relative changes in volume were 6.19% ± 10.95% and 1.48% ± 32.92%. PTVRECON and PTVCORR similarity coefficients were 0.90 ± 0.08 and 0.73 ± 0.16 for DICE and 0.82 ± 0.12 and 0.60 ± 0.18 for JACCARD. PTVRECON and PTVCORR changes in Dmin were -0.88% ± 8.77% and -12.9 ± 17.3%. PTVRECON and PTVCORR changes in D95% were -0.34% ± 5.89 and -8.68% ± 13.21%. The 1 mm expanded PTVORIG targets did not entirely cover 14 of the 80 PTVCORR targets. Normal tissue changes (V12Gy, V10Gy, V5Gy) calculated with PTVRECON were (-0.09% ± 7.39%, -0.38% ± 5.67%, -0.08% ± 2.04%) and PTVCORR were (-2.14% ± 7.34%, -1.42% ± 5.45%, -0.61% ± 1.93%). Except for V10Gy, all PTVCORR changes were significantly different (p < 0.05) than PTVRECON.

CONCLUSION

MRIs used for SRS target delineation exhibit notable geometric distortions that may compromise optimal dosimetric accuracy. A uniform 1 mm expansion may result in geometric misses; however, the CDC algorithm provides a feasible solution for rectifying distortions, thereby enhancing treatment precision.

Modelling systematic anatomical uncertainties of head and neck cancer patients during fractionated radiotherapy treatment

Objective.Head and neck cancer patients experience systematic as well as random day to day anatomical changes during fractionated radiotherapy treatment. Modelling the expected systematic anatomical changes could aid in creating treatment plans which are more robust against such changes.Approach.Inter- patient correspondence aligned all patients to a model space. Intra- patient correspondence between each planning CT scan and on treatment cone beam CT scans was obtained using diffeomorphic deformable image registration. The stationary velocity fields were then used to develop B-Spline based patient specific (SM) and population average (AM) models. The models were evaluated geometrically and dosimetrically. A leave-one-out method was used to compare the training and testing accuracy of the models.Main results.Both SMs and AMs were able to capture systematic changes. The average surface distance between the registration propagated contours and the contours generated by the SM was less than 2 mm, showing that the SM are able to capture the anatomical changes which a patient experiences during the course of radiotherapy. The testing accuracy was lower than the training accuracy of the SM, suggesting that the model overfits to the limited data available and therefore, also captures some of the random day to day changes. For most patients the AMs were a better estimate of the anatomical changes than assuming there were no changes, but the AMs could not capture the variability in the anatomical changes seen in all patients. No difference was seen in the training and testing accuracy of the AMs. These observations were highlighted in both the geometric and dosimetric evaluations and comparisons.Significance.In this work, a SM and AM are presented which are able to capture the systematic anatomical changes of some head and neck cancer patients over the course of radiotherapy treatment. The AM is able to capture the overall trend of the population, but there is large patient variability which highlights the need for more complex, capable population models.

DiffuseRT: predicting likely anatomical deformations of patients undergoing radiotherapy

Objective. Predicting potential deformations of patients can improve radiotherapy treatment planning. Here, we introduce new deep-learning models that predict likely anatomical changes during radiotherapy for head and neck cancer patients.Approach. Denoising diffusion probabilistic models (DDPMs) were developed to generate fraction-specific anatomical changes based on a reference cone-beam CT (CBCT), the fraction number and the dose distribution delivered. Three distinct DDPMs were developed: (1) theimage modelwas trained to directly generate likely future CBCTs, (2) the deformable vector field (DVF) model was trained to generate DVFs that deform a reference CBCT and (3) thehybrid modelwas trained similarly to the DVF model, but without relying on an external deformable registration algorithm. The models were trained on 9 patients with longitudinal CBCT images (224 CBCTs) and evaluated on 5 patients (152 CBCTs).Results. The generated images mainly exhibited random positioning shifts and small anatomical changes for early fractions. For later fractions, all models predicted weight losses in accordance with the training data. The distributions of volume and position changes of the body, esophagus, and parotids generated with the image and hybrid models were more similar to the ground truth distribution than the DVF model, evident from the lower Wasserstein distance achieved with the image (0.33) and hybrid model (0.30) compared to the DVF model (0.36). Generating several images for the same fraction did not yield the expected variability since the ground truth anatomical changes were only in 76% of the fractions within the 95% bounds predicted with the best model. Using the generated images for robust optimization of simplified proton therapy plans improved the worst-case clinical target volume V95 with 7% compared to optimizing with 3 mm set-up robustness while maintaining a similar integral dose.Significance. The newly developed DDPMs generate distributions similar to the real anatomical changes and have the potential to be used for robust anatomical optimization.

Applications of artificial intelligence for machine- and patient-specific quality assurance in radiation therapy: current status and future directions

Machine- and patient-specific quality assurance (QA) is essential to ensure the safety and accuracy of radiotherapy. QA methods have become complex, especially in high-precision radiotherapy such as intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT), and various recommendations have been reported by AAPM Task Groups. With the widespread use of IMRT and VMAT, there is an emerging demand for increased operational efficiency. Artificial intelligence (AI) technology is quickly growing in various fields owing to advancements in computers and technology. In the radiotherapy treatment process, AI has led to the development of various techniques for automated segmentation and planning, thereby significantly enhancing treatment efficiency. Many new applications using AI have been reported for machine- and patient-specific QA, such as predicting machine beam data or gamma passing rates for IMRT or VMAT plans. Additionally, these applied technologies are being developed for multicenter studies. In the current review article, AI application techniques in machine- and patient-specific QA have been organized and future directions are discussed. This review presents the learning process and the latest knowledge on machine- and patient-specific QA. Moreover, it contributes to the understanding of the current status and discusses the future directions of machine- and patient-specific QA.

Multi-institutional questionnaire-based survey on online adaptive radiotherapy performed using commercial systems in Japan in 2023

In this study, we aimed to conduct a survey on the current clinical practice of, staffing for, commissioning of, and staff training for online adaptive radiotherapy (oART) in the institutions that installed commercial oART systems in Japan, and to share the information with institutions that will implement oART systems in future. A web-based questionnaire, containing 107 questions, was distributed to nine institutions in Japan. Data were collected from November to December 2023. Three institutions each with the MRIdian (ViewRay, Oakwood Village, OH, USA), Unity (Elekta AB, Stockholm, Sweden), and Ethos (Varian Medical Systems, Palo Alto, CA, USA) systems completed the questionnaire. One institution (MRIdian) had not performed oART by the response deadline. Each institution had installed only one oART system. Hypofractionation, and moderate hypofractionation or conventional fractionation were employed in the MRIdian/Unity and Ethos systems, respectively. The elapsed time for the oART process was faster with the Ethos than with the other systems. All institutions added additional staff for oART. Commissioning periods differed among the oART systems owing to provision of beam data from the vendors. Chambers used during commissioning measurements differed among the institutions. Institutional training was provided by all nine institutions. To the best of our knowledge, this was the first survey about oART performed using commercial systems in Japan. We believe that this study will provide useful information to institutions that installed, are installing, or are planning to install oART systems.

Safety and Efficiency Analysis of Operational Decision-Making During Cone Beam Computed Tomography-Based Online Adaptive Radiation Therapy

PURPOSE

Cone beam computed tomography (CBCT)-based online adaptive radiation therapy (ART) is especially beneficial for patients with large interfractional anatomic changes. However, treatment planning and review decisions need to be made at the treatment console in real-time and may be delegated to clinical staff whose conventional scope of practice does not include making such decisions. Therefore, implementation can create new safety risks and inefficiencies. The objective of this work is to systematically analyze the safety and efficiency implications of human decision-making during the treatment session for CBCT-based online ART.

METHODS AND MATERIALS

The analysis was performed by applying the Systems-Theoretical Process Analysis technique and its extension for human decision-making. Four centers of different CBCT-based online ART practice models comprised the analysis team.

RESULTS

The general radiation therapy control structure was refined to model the interactions between routine treatment delivery staff and in-person or remote support staff. The treatment delivery staff perform 6 key control actions. Eighteen undesirable states of those control actions were identified as affecting safety and/or efficiency. In turn, 97 hazardous clinical scenarios were identified, with the control action "prepare and position patient" having the least number of scenarios and "delineate/edit influencer and target structures" having the most. Five of these are specific to either in-person or remote support during the treatment session, and 12 arise from staff support in general.

CONCLUSIONS

An optimally safe and efficient online ART program should require little to no support staff at the treatment console to reduce staff coordination. Uptraining of the staff already at the treatment console is needed to achieve this goal. Beyond the essential knowledge and skills such as contour editing and the selection of an optimal plan, uptraining should also target the specific cognitive biases identified in this work and the cognitive strategies to overcome these biases. Additionally, technological and organizational changes are necessary.

The synergy of artificial intelligence and personalized medicine for the enhanced diagnosis, treatment, and prevention of disease

INTRODUCTION

The completion of the Human Genome Project in 2003 marked the beginning of a transformative era in medicine. This milestone laid the foundation for personalized medicine, an innovative approach that customizes healthcare treatments.

CONTENT

Central to the advancement of personalized medicine is the understanding of genetic variations and their impact on drug responses. The integration of artificial intelligence (AI) into drug response trials has been pivotal in this domain. These technologies excel in handling large-scale genomic datasets and patient histories, significantly improving diagnostic accuracy, disease prediction and drug discovery. They are particularly effective in addressing complex diseases such as cancer and genetic disorders. Furthermore, the advent of wearable technology, when combined with AI, propels personalized medicine forward by offering real-time health monitoring, which is crucial for early disease detection and management.

SUMMARY

The integration of AI into personalized medicine represents a significant advancement in healthcare, promising more accurate diagnoses, effective treatment plans and innovative drug discoveries.

OUTLOOK

As technology continues to evolve, the role of AI in enhancing personalized medicine and transforming the healthcare landscape is expected to grow exponentially. This synergy between AI and healthcare holds great promise for the future, potentially revolutionizing the way healthcare is delivered and experienced.

Voxel-Level Dosimetry for Combined Iodine 131 Radiopharmaceutical Therapy and External Beam Radiation Therapy Treatment Paradigms for Head and Neck Cancer

PURPOSE

Targeted radiopharmaceutical therapy (RPT) in combination with external beam radiation therapy (EBRT) shows promise as a method to increase tumor control and mitigate potential high-grade toxicities associated with re-treatment for patients with recurrent head and neck cancer. This work establishes a patient-specific dosimetry framework that combines Monte Carlo-based dosimetry from the 2 radiation modalities at the voxel level using deformable image registration (DIR) and radiobiological constructs for patients enrolled in a phase 1 clinical trial combining EBRT and RPT.

METHODS AND MATERIALS

Serial single-photon emission computed tomography (SPECT)/computed tomography (CT) patient scans were performed at approximately 24, 48, 72, and 168 hours postinjection of 577.2 MBq/m2 (15.6 mCi/m2) CLR 131, an iodine 131-containing RPT agent. Using RayStation, clinical EBRT treatment plans were created with a treatment planning CT (TPCT). SPECT/CT images were deformably registered to the TPCT using the Elastix DIR module in 3D Slicer software and assessed by measuring mean activity concentrations and absorbed doses. Monte Carlo EBRT dosimetry was computed using EGSnrc. RPT dosimetry was conducted using RAPID, a GEANT4-based RPT dosimetry platform. Radiobiological metrics (biologically effective dose and equivalent dose in 2-Gy fractions) were used to combine the 2 radiation modalities.

RESULTS

The DIR method provided good agreement for the activity concentrations and calculated absorbed dose in the tumor volumes for the SPECT/CT and TPCT images, with a maximum mean absorbed dose difference of -11.2%. Based on the RPT absorbed dose calculations, 2 to 4 EBRT fractions were removed from patient EBRT treatments. For the combined treatment, the absorbed dose to target volumes ranged from 57.14 to 75.02 Gy. When partial volume corrections were included, the mean equivalent dose in 2-Gy fractions to the planning target volume from EBRT + RPT differed -3.11% to 1.40% compared with EBRT alone.

CONCLUSIONS

This work demonstrates the clinical feasibility of performing combined EBRT + RPT dosimetry on TPCT scans. Dosimetry guides treatment decisions for EBRT, and this work provides a bridge for the same paradigm to be implemented within the rapidly emerging clinical RPT space.

Impact of target degradation on the 6FFF output of a Varian TrueBeam Linac

The dosimetric output of a 6FFF beam, produced from a Varian TrueBeam linac exhibited an unexpected downward trend over time that was contrary to well-established expectations. To elucidate the cause of this uncharacteristic trend, a review of the linac's quality control results over its lifetime was performed, including, constancy checks of the dosimetric output, beam energy, flatness and symmetry, and percentage depth dose characteristics. These results were supplemented with a comprehensive series of measurements including flatness and symmetry measurements with a 1D-diode array, high-resolution measurements of the photon beam's build-up region with a parallel-plate chamber and measurement of the beam's output as a function of the x-ray target position. The review of the linac's QC results and supplemental tests identified no deviations in the linac's performance from its commissioning and baseline measurements. However, the 6FFF beam output exhibited a significant dependence on the target location relative to its default position, increasing by 5.43 % with a 0.5 mm target translation, indicating that target degradation was the cause of the atypical output trend. The change in output behaviour was believed to be the result of primary electrons escaping the degraded target and interacting with the linac's monitor chamber. Replacement of the x-ray target caused the 6FFF output to realign with expected trends. Target degradation was uncovered due to a robust quality control trending database and awareness of typical output behaviour. These results demonstrate the importance of data trending to identify component failure and provide centres with knowledge to recognise this potential fault.

A new approach to prevent radiation-induced xerostomia using intraglandular injection of mitochondria-boosting agents

Radiotherapy in patients with head and neck cancer fairly leads to xerostomia, profoundly affecting their quality of life. With limited effective preventive and therapeutic methods, attention has turned to exploring alternatives. This article outlines how intraglandular injection of mitochondria-boosting agents can serve as a potential strategy to reduce salivary acinar damage. This method can contribute to the thoughtful development of study protocols or medications to reduce radiation-induced salivary glands damage.

External validation of an artificial intelligence model for Gleason grading of prostate cancer on prostatectomy specimens

OBJECTIVES

To externally validate the performance of the DeepDx Prostate artificial intelligence (AI) algorithm (Deep Bio Inc., Seoul, South Korea) for Gleason grading on whole-mount prostate histopathology, considering potential variations observed when applying AI models trained on biopsy samples to radical prostatectomy (RP) specimens due to inherent differences in tissue representation and sample size.

MATERIALS AND METHODS

The commercially available DeepDx Prostate AI algorithm is an automated Gleason grading system that was previously trained using 1133 prostate core biopsy images and validated on 700 biopsy images from two institutions. We assessed the AI algorithm's performance, which outputs Gleason patterns (3, 4, or 5), on 500 1-mm2 tiles created from 150 whole-mount RP specimens from a third institution. These patterns were then grouped into grade groups (GGs) for comparison with expert pathologist assessments. The reference standard was the International Society of Urological Pathology GG as established by two experienced uropathologists with a third expert to adjudicate discordant cases. We defined the main metric as the agreement with the reference standard, using Cohen's kappa.

RESULTS

The agreement between the two experienced pathologists in determining GGs at the tile level had a quadratically weighted Cohen's kappa of 0.94. The agreement between the AI algorithm and the reference standard in differentiating cancerous vs non-cancerous tissue had an unweighted Cohen's kappa of 0.91. Additionally, the AI algorithm's agreement with the reference standard in classifying tiles into GGs had a quadratically weighted Cohen's kappa of 0.89. In distinguishing cancerous vs non-cancerous tissue, the AI algorithm achieved a sensitivity of 0.997 and specificity of 0.88; in classifying GG ≥2 vs GG 1 and non-cancerous tissue, it demonstrated a sensitivity of 0.98 and specificity of 0.85.

CONCLUSION

The DeepDx Prostate AI algorithm had excellent agreement with expert uropathologists and performance in cancer identification and grading on RP specimens, despite being trained on biopsy specimens from an entirely different patient population.

A method for empirically validating FMEA RPN scores in a radiation oncology clinic using physics QC data

In failure modes and effects analysis (FMEA), the components of the risk priority number (RPN) for a failure mode (FM) are often chosen by consensus. We describe an empirical method for estimating the occurrence (O) and detectability (D) components of a RPN. The method requires for a given FM that its associated quality control measure be performed twice as is the case when a FM is checked for in an initial physics check and again during a weekly physics check. If instances of the FM caught by these checks are recorded, O and D can be computed. Incorporation of the remaining RPN component, Severity, is discussed. This method can be used as part of quality management design ahead of an anticipated FMEA or afterwards to validate consensus values.

18F-FDG PET/CT- and MRI-Based Locally Advanced Cervical Cancer Early-Response Assessment after Concurrent Chemo- and Radiotherapy-Impact on Patient Outcomes and Survival Prediction

With one third of patients with locally advanced cervical cancer (LACC) expected to develop cancer recurrence in the first two years after therapy, accurate assessment of the response and timely detection of cancer recurrence after concurrent chemo- and radiotherapy (CCRT) treatment is of great importance. Although there is neither definite consensus about the preferred imaging modality, nor the time interval until the first diagnostic examination after CCRT, the National Comprehensive Cancer Network (NCCN) recommends the use of MRI and 18F-FDG PET/CT as a post-treatment LACC response-assessment imaging tools. In this study, we tried to appraise the early therapy response in LACC patients by both 18F-FDG PET/CT and MRI in regard to the follow-up imaging results and their mutual interrelationship, and to ascertain if the post-treatment 18F-FDG PET/CT and MRI results were related to the progression-free and overall survival rate in women with LACC after CCRT. We also aimed to estimate the early and follow-up diagnostic imaging impact on further therapy management. Based on our results, we concluded that 18F-FDG PET/CT did surpass MRI in the early assessment of therapeutic response in LACC patients after CCRT. Both modalities provided information that may serve as predictive biomarkers of outcome and LACC patients' survival.

Artificial intelligence innovations in neurosurgical oncology: a narrative review

PURPOSE

Artificial Intelligence (AI) has become increasingly integrated clinically within neurosurgical oncology. This report reviews the cutting-edge technologies impacting tumor treatment and outcomes.

METHODS

A rigorous literature search was performed with the aid of a research librarian to identify key articles referencing AI and related topics (machine learning (ML), computer vision (CV), augmented reality (AR), virtual reality (VR), etc.) for neurosurgical care of brain or spinal tumors.

RESULTS

Treatment of central nervous system (CNS) tumors is being improved through advances across AI-such as AL, CV, and AR/VR. AI aided diagnostic and prognostication tools can influence pre-operative patient experience, while automated tumor segmentation and total resection predictions aid surgical planning. Novel intra-operative tools can rapidly provide histopathologic tumor classification to streamline treatment strategies. Post-operative video analysis, paired with rich surgical simulations, can enhance training feedback and regimens.

CONCLUSION

While limited generalizability, bias, and patient data security are current concerns, the advent of federated learning, along with growing data consortiums, provides an avenue for increasingly safe, powerful, and effective AI platforms in the future.

Radiomics score derived from T1-w/T2-w ratio image can predict motor symptom progression in Parkinson's disease

OBJECTIVES

To clarify the association between a radiomics score (Rad-score) derived from T1-weighted signal intensity to T2-weighted signal intensity (T1-w/T2-w) ratio images and the progression of motor symptoms in Parkinson's disease (PD).

MATERIALS AND METHODS

This retrospective study included patients with PD enrolled in the Parkinson's Progression Markers Initiative. The Movement Disorders Society-Unified Parkinson's Disease Rating Scale Part III score ≥ 33 and/or Hoehn and Yahr stage ≥ 3 indicated motor function decline. The Rad-score was constructed using radiomics features extracted from T1-w/T2-w ratio images. The Kaplan-Meier analysis and Cox regression analyses were used to assess the time differences in motor function decline between the high and low Rad-score groups.

RESULTS

A total of 171 patients with PD were divided into training (n = 101, mean age at baseline, 61.6 ± 9.3 years) and testing (n = 70, mean age at baseline, 61.6 ± 10 years). The patients in the high Rad-score group had a shorter time to motor function decline than those in the low Rad-score group in the training dataset (log-rank test, p < 0.001) and testing dataset (log-rank test, p < 0.001). The multivariate Cox regression using the Rad-score and clinical factors revealed a significant association between the Rad-score and motor function decline in the training dataset (HR = 2.368, 95%CI:1.423-3.943, p < 0.001) and testing dataset (HR = 2.931, 95%CI:1.472-5.837, p = 0.002).

CONCLUSION

Rad-scores based on radiomics features derived from T1-w/T2-w ratio images were associated with the progression of motor symptoms in PD.

CLINICAL RELEVANCE STATEMENT

The radiomics score derived from the T1-weighted/T2-weighted ratio images offers a predictive tool for assessing the progression of motor symptom in patients with PD.

KEY POINTS

Radiomics score derived from T1-weighted/T2-weighted ratio images is correlated with the motor symptoms of Parkinson's disease. A high radiomics score correlated with faster motor function decline in patients with Parkinson's disease. The proposed radiomics score offers predictive insight into the progression of motor symptoms of Parkinson's disease.

Biomarker-driven molecular imaging probes in radiotherapy

Background: Biomarker-driven molecular imaging has emerged as an integral part of cancer precision radiotherapy. The use of molecular imaging probes, including nanoprobes, have been explored in radiotherapy imaging to precisely and noninvasively monitor spatiotemporal distribution of biomarkers, potentially revealing tumor-killing mechanisms and therapy-induced adverse effects during radiation treatment. Methods: We summarized literature reports from preclinical studies and clinical trials, which cover two main parts: 1) Clinically-investigated and emerging imaging biomarkers associated with radiotherapy, and 2) instrumental roles, functions, and activatable mechanisms of molecular imaging probes in the radiotherapy workflow. In addition, reflection and future perspectives are proposed. Results: Numerous imaging biomarkers have been continuously explored in decades, while few of them have been successfully validated for their correlation with radiotherapeutic outcomes and/or radiation-induced toxicities. Meanwhile, activatable molecular imaging probes towards the emerging biomarkers have exhibited to be promising in animal or small-scale human studies for precision radiotherapy. Conclusion: Biomarker-driven molecular imaging probes are essential for precision radiotherapy. Despite very inspiring preliminary results, validation of imaging biomarkers and rational design strategies of probes await robust and extensive investigations. Especially, the correlation between imaging biomarkers and radiotherapeutic outcomes/toxicities should be established through multi-center collaboration involving a large cohort of patients.

Localized fine-tuning and clinical evaluation of deep-learning based auto-segmentation (DLAS) model for clinical target volume (CTV) and organs-at-risk (OAR) in rectal cancer radiotherapy

BACKGROUND AND PURPOSE

Various deep learning auto-segmentation (DLAS) models have been proposed, some of which have been commercialized. However, the issue of performance degradation is notable when pretrained models are deployed in the clinic. This study aims to enhance precision of a popular commercial DLAS product in rectal cancer radiotherapy by localized fine-tuning, addressing challenges in practicality and generalizability in real-world clinical settings.

MATERIALS AND METHODS

A total of 120 Stage II/III mid-low rectal cancer patients were retrospectively enrolled and divided into three datasets: training (n = 60), external validation (ExVal, n = 30), and generalizability evaluation (GenEva, n = 30) datasets respectively. The patients in the training and ExVal dataset were acquired on the same CT simulator, while those in GenEva were on a different CT simulator. The commercial DLAS software was first localized fine-tuned (LFT) for clinical target volume (CTV) and organs-at-risk (OAR) using the training data, and then validated on ExVal and GenEva respectively. Performance evaluation involved comparing the LFT model with the vendor-provided pretrained model (VPM) against ground truth contours, using metrics like Dice similarity coefficient (DSC), 95th Hausdorff distance (95HD), sensitivity and specificity.

RESULTS

LFT significantly improved CTV delineation accuracy (p < 0.05) with LFT outperforming VPM in target volume, DSC, 95HD and specificity. Both models exhibited adequate accuracy for bladder and femoral heads, and LFT demonstrated significant enhancement in segmenting the more complex small intestine. We did not identify performance degradation when LFT and VPM models were applied in the GenEva dataset.

CONCLUSIONS

The necessity and potential benefits of LFT DLAS towards institution-specific model adaption is underscored. The commercial DLAS software exhibits superior accuracy once localized fine-tuned, and is highly robust to imaging equipment changes.

Assessing focal spot alignment in clinical linear accelerators: a comprehensive evaluation with triplet phantoms

A fundamental parameter to evaluate the beam delivery precision and stability on a clinical linear accelerator (linac) is the focal spot position (FSP) measured relative to the collimator axis of the radiation head. The aims of this work were to evaluate comprehensive data on FSP acquired on linacs in clinical use and to establish the ability of alternative phantoms to detect effects on patient plan delivery related to FSP. FSP measurements were conducted using a rigid phantom holding two ball-bearings at two different distances from the radiation source. Images of these ball-bearings were acquired using the electronic portal imaging device (EPID) integrated with each linac. Machine QA was assessed using a radiation head-mounted PTW STARCHECK phantom. Patient plan QA was investigated using the SNC ArcCHECK phantom positioned on the treatment couch, irradiated with VMAT plans across a complete 360° gantry rotation and three X-ray energies. This study covered eight Elekta linacs, including those with 6 MV, 18 MV, and 6 MV flattening-filter-free (FFF) beams. The largest range in the FSP was found for 6 MV FFF. The FSP of one linac, retrofitted with 6 MV FFF, displayed substantial differences in FSP compared to 6 MV FFF beams on other linacs, which all had FSP ranges less than 0.50 mm and 0.25 mm in the lateral and longitudinal directions, respectively. The PTW STARCHECK phantom proved effective in characterising the FSP, while the SNC ArcCHECK measurements could not discern FSP-related features. Minor variations in FSP may be attributed to adjustments in linac parameters, component replacements necessary for beam delivery, and the wear and tear of various linac components, including the magnetron and gun filament. Consideration should be given to the ability of any particular phantom to detect a subsequent impact on the accuracy of patient plan delivery.

First implementation of an innovative infra-red camera system integrated into a mobile CBCT scanner for applicator tracking in brachytherapy-Initial performance characterization

PURPOSE

To enable a real-time applicator guidance for brachytherapy, we used for the first time infra-red tracking cameras (OptiTrack, USA) integrated into a mobile cone-beam computed tomography (CBCT) scanner (medPhoton, Austria). We provide the first description of this prototype and its performance evaluation.

METHODS

We performed assessments of camera calibration and camera-CBCT registration using a geometric calibration phantom. For this purpose, we first evaluated the effects of intrinsic parameters such as camera temperature or gantry rotations on the tracked marker positions. Afterward, calibrations with various settings (sample number, field of view coverage, calibration directions, calibration distances, and lighting conditions) were performed to identify the requirements for achieving maximum tracking accuracy based on an in-house phantom. The corresponding effects on camera-CBCT registration were determined as well by comparing tracked marker positions to the positions determined via CBCT. Long-term stability was assessed by comparing tracking and a ground-truth on a weekly basis for 6 weeks.

RESULTS

Robust tracking with positional drifts of 0.02 ± 0.01 mm was feasible using the system after a warm-up period of 90 min. However, gantry rotations affected the tracking and led to inaccuracies of up to 0.70 mm. We identified that 4000 samples and full coverage were required to ensure a robust determination of marker positions and camera-CBCT registration with geometric deviations of 0.18 ± 0.03 mm and 0.42 ± 0.07 mm, respectively. Long-term stability showed deviations of more than two standard deviations from the initial calibration after 3 weeks.

CONCLUSION

We implemented for the first time a standalone combined camera-CBCT system for tracking in brachytherapy. The system showed high potential for establishing corresponding workflows.

A deep learning-based 3D Prompt-nnUnet model for automatic segmentation in brachytherapy of postoperative endometrial carcinoma

PURPOSE

To create and evaluate a three-dimensional (3D) Prompt-nnUnet module that utilizes the prompts-based model combined with 3D nnUnet for producing the rapid and consistent autosegmentation of high-risk clinical target volume (HR CTV) and organ at risk (OAR) in high-dose-rate brachytherapy (HDR BT) for patients with postoperative endometrial carcinoma (EC).

METHODS AND MATERIALS

On two experimental batches, a total of 321 computed tomography (CT) scans were obtained for HR CTV segmentation from 321 patients with EC, and 125 CT scans for OARs segmentation from 125 patients. The numbers of training/validation/test were 257/32/32 and 87/13/25 for HR CTV and OARs respectively. A novel comparison of the deep learning neural network 3D Prompt-nnUnet and 3D nnUnet was applied for HR CTV and OARs segmentation. Three-fold cross validation and several quantitative metrics were employed, including Dice similarity coefficient (DSC), Hausdorff distance (HD), 95th percentile of Hausdorff distance (HD95%), and intersection over union (IoU).

RESULTS

The Prompt-nnUnet included two forms of parameters Predict-Prompt (PP) and Label-Prompt (LP), with the LP performing most similarly to the experienced radiation oncologist and outperforming the less experienced ones. During the testing phase, the mean DSC values for the LP were 0.96 ± 0.02, 0.91 ± 0.02, and 0.83 ± 0.07 for HR CTV, rectum and urethra, respectively. The mean HD values (mm) were 2.73 ± 0.95, 8.18 ± 4.84, and 2.11 ± 0.50, respectively. The mean HD95% values (mm) were 1.66 ± 1.11, 3.07 ± 0.94, and 1.35 ± 0.55, respectively. The mean IoUs were 0.92 ± 0.04, 0.84 ± 0.03, and 0.71 ± 0.09, respectively. A delineation time < 2.35 s per structure in the new model was observed, which was available to save clinician time.

CONCLUSION

The Prompt-nnUnet architecture, particularly the LP, was highly consistent with ground truth (GT) in HR CTV or OAR autosegmentation, reducing interobserver variability and shortening treatment time.

Target coverage and organs at risk dose in hypofractionated salvage radiotherapy after prostatectomy

Background and purpose

Introducing moderately hypofractionated salvage radiotherapy (SRT) following prostatectomy obligates investigation of its effects on clinical target volume (CTV) coverage and organ-at-risk (OAR) doses. This study assessed interfractional volume and dose changes in OARs and CTV in moderately hypofractionated SRT and evaluated the 8-mm planning target volume (PTV) margin.

Materials and methods

Twenty patients from the PERYTON-trial were included; 10 received conventional SRT (35 × 2 Gy) and 10 hypofractionated SRT (20 × 3 Gy). OARs were delineated on 539 pre-treatment Cone Beam CT (CBCT) scans to compare interfractional OAR volume changes. CTVs for the hypofractionated group were delineated on 199 CBCTs. Dose distributions with 4 and 6 mm PTV margins were generated using voxel-wise minimum robustness evaluation of the original 8-mm PTV plan, and dose changes were assessed.

Results

Median volume changes for bladder and rectum were -26 % and -10 %, respectively. OAR volume changes were not significantly different between the two treatment schedules. The 8-mm PTV margin ensured optimal coverage for prostate bed and vesicle bed CTV (V95 = 100 % in >97 % fractions). However, bladder V60 <25 % was not achieved in 5 % of fractions, and rectum V60 <5 % was unmet in 33 % of fractions. A 6-mm PTV margin resulted in CTV V95 = 100 % in 92 % of fractions for prostate bed, and in 86 % for vesicle bed CTV.

Conclusions

Moderately hypofractionated SRT yielded comparable OAR volume changes to conventionally fractionated SRT. Interfractional changes remained acceptable with a PTV margin of 6 mm for prostate bed and 8 mm for vesicle bed.

The impact of advancing the standard of care in radiotherapy on operational treatment resources

PURPOSE

To demonstrate the impact of implementing hypofractionated prescription regimens and advanced treatment techniques on institutional operational hours and radiotherapy personnel resources in a multi-institutional setting. The study may be used to describe the impact of advancing the standard of care with modern radiotherapy techniques on patient and staff resources.

METHODS

This study uses radiation therapy data extracted from the radiotherapy information system from two tertiary care, university-affiliated cancer centers from 2012 to 2021. Across all patients in the analysis, the average fraction number for curative and palliative patients was reported each year in the decade. Also, the institutional operational treatment hours are reported for both centers. A sub-analysis for curative intent breast and lung radiotherapy patients was performed to contextualize the impact of changes to imaging, motion management, and treatment technique.

RESULTS

From 2012 to 2021, Center 1 had 42 214 patient plans and Center 2 had 43 252 patient plans included in the analysis. Averaged over both centers across the decade, the average fraction number per patient decreased from 6.9 to 5.2 (25%) and 21.8 to 17.2 (21%) for palliative and curative patients, respectively. The operational treatment hours for both institutions increased from 8 h 15 min to 9 h 45 min (18%), despite a patient population increase of 45%.

CONCLUSION

The clinical implementation of hypofractionated treatment regimens has successfully reduced the radiotherapy workload and operational treatment hours required to treat patients. This analysis describes the impact of changes to the standard of care on institutional resources.

Pelvic lymph node motion during cone-beam computed tomography guided stereotactic radiotherapy

Background and purpose

Stereotactic body radiotherapy (SBRT) is increasingly applied for pelvic lymph node recurrence. Thus far, knowledge on pelvic lymph node motion during CBCT-guided SBRT is lacking and the applied margins vary between institutions. This study evaluated pelvic lymph node motion during CBCT-guided SBRT and assessed the currently applied PTV margins of 3 and 5 mm.

Material and methods

In total, 45 pelvic lymph node metastases were included. One observer delineated 45 GTVs on planning CT, 224 GTVs on pre-fraction and 216 on post-fraction CBCT. The GTV centroid coordinates were derived from all images for inter- and intrafraction motion analysis. Additionally, we assessed the influence of treatment time and lesion location on lesion motion. The expected coverage of a 3-mm and 5-mm PTV margin was assessed using the inclusiveness index for GTVs on pre- and post-fraction CBCT.

Results

Lymph node interfraction motion was limited to 5 mm in 96-97 % of fractions for all translational directions and intrafraction lesion motion was limited to 3 mm in 97-100 % of fractions. Para-rectal lesions (11 %) were associated with significantly larger inter- and intrafraction motion compared to other pelvic locations and treatment duration showed no correlation with lesion motion. The mean (sd) lesion inclusiveness index was 99 % (5 %) for the 5-mm PTV margin and 96 % (9 %) for the 3-mm margin.

Conclusion

Pelvic lymph node motion during CBCT-guided stereotactic radiotherapy was within the widely applied PTV margin of 5 mm, providing an opportunity to reduce this margin for pelvic lymph node SBRT.

Repurposing DailyQA3 for an efficient and spot position sensitive daily quality assurance tool for proton therapy

INTRODUCTION

Daily quality assurance is an integral part of a radiotherapy workflow to ensure the dose is delivered safely and accurately to the patient. It is performed before the first treatment of the day and needs to be time and cost efficient for a multiple gantries proton center. In this study, we introduced an efficient method to perform QA for output constancy, range verification, spot positioning accuracy and imaging and proton beam isocenter coincidence with DailyQA3.

METHODS

A stepped acrylic block of specific dimensions is fabricated and placed on top of the DailyQA3 device. Treatment plans comprising of two different spread-out Bragg peaks and five individual spots of 1.0 MU each are designed to be delivered to the device. A mathematical framework to measure the 2D distance between the detectors and individual spot is introduced and play an important role in realizing the spot positioning and centering QA. Lastly, a 5 months trends of the QA for two gantries are presented.

RESULTS

The outputs are monitored by two ion chambers in the DailyQA3 and a tolerance of± 3 % $ \pm 3\% $ are used. The range of the SOBPs are monitored by the ratio of ion chamber signals and a tolerance of± 1 mm $ \pm 1\ {\mathrm{mm}}$ is used. Four diodes at± 10 cm $ \pm 10\ {\mathrm{cm}}$ from the central ion chambers are used for spot positioning QA, while the central ion chamber is used for imaging and proton beam isocenter coincidence QA. Using the framework, we determined the absolute signal threshold corresponding to the offset tolerance between the individual proton spot and the detector. A1.5 mm $1.5\ {\mathrm{mm}}$ tolerances are used for both the positioning and centering QA. No violation of the tolerances is observed in the 5 months trends for both gantries.

CONCLUSION

With the proposed approach, we can perform four QA items in the TG224 within 10 min.

Current role of magnetic resonance imaging on assessing and monitoring the efficacy of phototherapy

Phototherapy, also known as photobiological therapy, is a non-invasive and highly effective physical treatment method. Its broad use in clinics has led to significant therapeutic results. Phototherapy parameters, such as intensity, wavelength, and duration, can be adjusted to create specific therapeutic effects for various medical conditions. Meanwhile, Magnetic Resonance Imaging (MRI), with its diverse imaging sequences and excellent soft-tissue contrast, provides a valuable tool to understand the therapeutic effects and mechanisms of phototherapy. This review explores the clinical applications of commonly used phototherapy techniques, gives a brief overview of how phototherapy impacts different diseases, and examines MRI's role in various phototherapeutic scenarios. We argue that MRI is crucial for precise targeting, treatment monitoring, and prognosis assessment in phototherapy. Future research and applications will focus on personalized diagnosis and monitoring of phototherapy, expanding its applications in treatment and exploring multimodal imaging technology to enhance diagnostic and therapeutic precision and effectiveness.

Bringing online adaptive radiotherapy to a standard C-arm linac

Current online adaptive radiotherapy (oART) workflows require dedicated equipment. Our aim was to develop and implement an oART workflow for a C-arm linac which can be performed using standard clinically available tools. A workflow was successfully developed and implemented. Three patients receiving palliative radiotherapy for bladder cancer were treated, with 33 of 35 total fractions being delivered with the cone-beam computed tomography (CBCT)-guided oART workflow. Average oART fraction duration was 24 min from start of CBCT acquisition to end of beam on. This work shows how oART could be performed without dedicated equipment, broadening oART availability for application at existing treatment machines.

The effect of harmonization on the variability of PET radiomic features extracted using various segmentation methods

PURPOSE

This study aimed to examine the robustness of positron emission tomography (PET) radiomic features extracted via different segmentation methods before and after ComBat harmonization in patients with non-small cell lung cancer (NSCLC).

METHODS

We included 120 patients (positive recurrence = 46 and negative recurrence = 74) referred for PET scanning as a routine part of their care. All patients had a biopsy-proven NSCLC. Nine segmentation methods were applied to each image, including manual delineation, K-means (KM), watershed, fuzzy-C-mean, region-growing, local active contour (LAC), and iterative thresholding (IT) with 40, 45, and 50% thresholds. Diverse image discretizations, both without a filter and with different wavelet decompositions, were applied to PET images. Overall, 6741 radiomic features were extracted from each image (749 radiomic features from each segmented area). Non-parametric empirical Bayes (NPEB) ComBat harmonization was used to harmonize the features. Linear Support Vector Classifier (LinearSVC) with L1 regularization For feature selection and Support Vector Machine classifier (SVM) with fivefold nested cross-validation was performed using StratifiedKFold with 'n_splits' set to 5 to predict recurrence in NSCLC patients and assess the impact of ComBat harmonization on the outcome.

RESULTS

From 749 extracted radiomic features, 206 (27%) and 389 (51%) features showed excellent reliability (ICC ≥ 0.90) against segmentation method variation before and after NPEB ComBat harmonization, respectively. Among all, 39 features demonstrated poor reliability, which declined to 10 after ComBat harmonization. The 64 fixed bin widths (without any filter) and wavelets (LLL)-based radiomic features set achieved the best performance in terms of robustness against diverse segmentation techniques before and after ComBat harmonization. The first-order and GLRLM and also first-order and NGTDM feature families showed the largest number of robust features before and after ComBat harmonization, respectively. In terms of predicting recurrence in NSCLC, our findings indicate that using ComBat harmonization can significantly enhance machine learning outcomes, particularly improving the accuracy of watershed segmentation, which initially had fewer reliable features than manual contouring. Following the application of ComBat harmonization, the majority of cases saw substantial increase in sensitivity and specificity.

CONCLUSION

Radiomic features are vulnerable to different segmentation methods. ComBat harmonization might be considered a solution to overcome the poor reliability of radiomic features.

Comparison of 3-year local control using DAHANCA radiotherapy guidelines before and after implementation of five millimetres geometrical GTV to high-dose CTV margin

INTRODUCTION

Treatment planning using a five-millimetre geometrical margin from GTV to high-dose CTV (CTV1) has been used in DAHANCA treatment centres since 2013. We aimed to evaluate changes in CTV1 volumes, local control (LC), and recurrence pattern after the implementation of five-millimetre geometrical margins nationally.

MATERIALS AND METHODS

1,948 patients with pharyngeal, and laryngeal squamous cell carcinomas completed definitive IMRT-based treatment in 2010-2012 and 2013-2015 in three centres. The patient-specific margin was calculated as median surface distance from primary tumour GTV (GTV-T) to CTV1. Radiologically verified local recurrences were analysed using a centre of mass (COM) of the delineated recurrence volume, measuring the shortest distance between COM to GTV-T and CTV1 boundaries.

RESULTS

Median GTV-CTV1 was 0.9 (0.0-0.97) and 0.47 cm (0.4-0.5) for 2010-2012 and 2013-2015, respectively. Median CTV1 changed in three centres from 76, 28, 42 cm3 to 61, 53, 62 cm3 for 2010-2012 and 2013-2015, respectively. Local failures occurred at 247 patients during first three years after radiotherapy. The 3-year LC rate for 2010-2012 and 2013-2015 was 0.84 and 0.87 (p = 0.06). Out of 146 radiology-verified analysable local recurrences, 102 (69.9%) were inside the CTV1. In 74.6% and 91% of cases, the LRs were covered by 95% isodose in 2010-2012 and 2013-2015, respectively.

CONCLUSION

DAHANCA radiotherapy guidelines based on a geometrically generated isotropic CTV1 margin led to less variation in treatment volumes and between centres than previous guidelines. The transition towards consensus GTV-CTV1 margins did not influence local tumour control. The majority of local recurrences were inside CTV1 and covered by the prescription dose.

Clinical outcomes of pelvic bone marrow sparing radiotherapy for cervical cancer: A systematic review and meta-analysis of randomised controlled trials

Background

Concurrent chemoradiotherapy (CRT) is the standard treatment for locally advanced cervical cancer. We investigated how additional bone marrow sparing (BMS) affects the clinical outcomes.

Methods

We queried MEDLINE, Embase, Web of Science Core Collection, Google Scholar, Sinomed, CNKI, and Wanfang databases for articles published in English or Chinese between 2010/01/01 and 2023/10/31. Full-text manuscripts of prospective, randomised trials on BMS in cervical cancer patients treated with definitive or postoperative CRT were included. Risk of bias (RoB) was assessed using Cochrane Collaboration's RoB tool. Random-effects models were used for the meta-analysis.

Results

A total of 17 trials encompassing 1297 patients were included. The majority were single-centre trials (n = 1268) performed in China (n = 1128). Most trials used CT-based anatomical BMS (n = 1076). There was a comparable representation of trials in the definitive (n = 655) and postoperative (n = 582) settings, and the remaining trials included both.Twelve studies reported data on G ≥ 3 (n = 782) and G ≥ 2 (n = 754) haematologic adverse events. Both G ≥ 3 (OR 0.39; 95 % CI 0.28-0.55; p < 0.001) and G ≥ 2 (OR 0.29; 95 % CI 0.18-0.46; p < 0.001) toxicity were significantly lowered, favouring BMS. Seven studies (n = 635) reported data on chemotherapy interruptions, defined as receiving less than five cycles of cisplatin, which were significantly less frequent in patients treated with BMS (OR 0.44; 95 % CI 0.24-0.81; p = 0.016). There was no evidence of increased gastrointestinal or genitourinary toxicity.There were no signs of significant heterogeneity. Four studies were assessed as high RoB; sensitivity analyses excluding these provided comparable results for main outcomes. The main limitations include heterogeneity in BMS methodology between studies, low representation of populations most affected by cervical cancer, and insufficient data to assess survival outcomes.

Conclusions

The addition of BMS to definitive CRT in cervical cancer patients decreases hematologic toxicity and the frequency of interruptions in concurrent chemotherapy. However, data are insufficient to verify the impact on survival and disease control.

Response-adapted ultra-low-dose 4 Gy radiation as definitive therapy of gastric MALT lymphoma: a single-centre, pilot trial

BACKGROUND

Given the favourable prognosis of patients with gastric mucosa-associated lymphoid tissue (MALT) lymphoma, treatment-related toxicity should be minimised. We aimed to evaluate the efficacy of 4 Gy radiotherapy given in a response-adapted approach.

METHODS

We conducted a single-centre, single-arm, prospective trial at MD Anderson Cancer Center (Houston, TX, USA) of response-adapted ultra-low-dose radiotherapy. Eligible patients were 18 years or older and had newly diagnosed or relapsed Helicobacter pylori-negative gastric MALT lymphoma, with stage I-IV disease. Given the expected low toxicity profile of treatment, performance status was not an exclusion criterion. Patients received external beam photon-based radiotherapy for a total dose of 4 Gy in two fractions. Patients with a complete response to 4 Gy via endoscopy and imaging at 3-4 months were observed; patients with a partial response were re-evaluated in 6-9 months. Residual disease at 9-13 months or stable or progressive disease at any time required additional treatment with 20 Gy. The primary endpoint was gastric complete response at 1 year (second evaluation timepoint) after 4 Gy treatment. All analyses were performed as intention to treat. This trial is registered at ClinicalTrials.gov (NCT03680586) and is complete and closed to enrolment.

FINDINGS

Between March 27, 2019, and Oct 12, 2021, we enrolled 24 eligible patients. The median age of participants was 67 years (IQR 58-74; range 40-85); 15 (63%) were female and nine (37%) male; 18 (75%) were White, four (17%) Asian, and two (8%) Hispanic; 20 (83%) had stage I disease, one (4%) stage II, and three (13%) stage IV. Median follow-up time was 36 months (IQR 26-42). 20 patients (83%) had a complete response to 4 Gy (16 at 3-4 months, four at 9-13 months); two patients received 20 Gy for symptomatic stable disease at 3-4 months and two for residual disease at 9-13 months; all had a complete response. The 3-year local control rate was 96% (95% CI 88-100), with one local relapse at 14 months after 4 Gy radiotherapy salvaged successfully with 20 Gy. One patient with stage IV disease had a distant relapse. The most common adverse events were grade 1 nausea (nine [38%] of 24 patients who received 4 Gy and two [50%] of four patients who received 20 Gy) and grade 1 abdominal pain (five [21%] of 24 and zero of four, respectively). No grade 3 or worse adverse events were noted, including no treatment-related deaths.

INTERPRETATION

Most patients had a complete response after 4 Gy radiotherapy; all who required an additional 20 Gy had a complete response within 12 months. This response-adapted strategy could be used to select patients who would benefit from additional radiotherapy and spare others potential associated toxicity.

FUNDING

National Cancer Institute.

Treatment planning for MR-guided SBRT of pancreatic tumors on a 1.5 T MR-Linac: A global consensus protocol

Background and purpose

Treatment planning for MR-guided stereotactic body radiotherapy (SBRT) for pancreatic tumors can be challenging, leading to a wide variation of protocols and practices. This study aimed to harmonize treatment planning by developing a consensus planning protocol for MR-guided pancreas SBRT on a 1.5 T MR-Linac.

Materials and methods

A consortium was founded of thirteen centers that treat pancreatic tumors on a 1.5 T MR-Linac. A phased planning exercise was conducted in which centers iteratively created treatment plans for two cases of pancreatic cancer. Each phase was followed by a meeting where the instructions for the next phase were determined. After three phases, a consensus protocol was reached.

Results

In the benchmarking phase (phase I), substantial variation between the SBRT protocols became apparent (for example, the gross tumor volume (GTV) D99% ranged between 36.8 - 53.7 Gy for case 1, 22.6 - 35.5 Gy for case 2). The next phase involved planning according to the same basic dosimetric objectives, constraints, and planning margins (phase II), which led to a large degree of harmonization (GTV D99% range: 47.9-53.6 Gy for case 1, 33.9-36.6 Gy for case 2). In phase III, the final consensus protocol was formulated in a treatment planning system template and again used for treatment planning. This not only resulted in further dosimetric harmonization (GTV D99% range: 48.2-50.9 Gy for case 1, 33.5-36.0 Gy for case 2) but also in less variation of estimated treatment delivery times.

Conclusion

A global consensus protocol has been developed for treatment planning for MR-guided pancreatic SBRT on a 1.5 T MR-Linac. Aside from harmonizing the large variation in the current clinical practice, this protocol can provide a starting point for centers that are planning to treat pancreatic tumors on MR-Linac systems.

Multiparametric magnetic resonance imaging (MRI)-based radiomics model explained by the Shapley Additive exPlanations (SHAP) method for predicting complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicenter retrospective study

Background

Predicting the response to neoadjuvant chemoradiotherapy (nCRT) before initiating treatment is essential for tailoring therapeutic strategies and monitoring prognosis in locally advanced rectal cancer (LARC). In this study, we aimed to develop and validate radiomic-based models to predict clinical and pathological complete responses (cCR and pCR, respectively) by incorporating the Shapley Additive exPlanations (SHAP) method for model interpretation.

Methods

A total of 285 patients with complete pretreatment clinical characteristics and T1-weighted (T1W) and T2-weighted (T2W) magnetic resonance imaging (MRI) at 3 centers were retrospectively recruited. The features of tumor lesions were extracted by PyRadiomics and selected using least absolute shrinkage and selection operator (LASSO) algorithm. The selected features were used to build multilayer perceptron (MLP) models alone or combined with clinical features. Area under the receiver operating characteristic curve (AUC), decision curve, and calibration curve were applied to evaluate performance of models. The SHAP method was adopted to explain the prediction models.

Results

The radiomic-based models all showed better performances than clinical models. The clinical-radiomic models showed the best differentiation on cCR and pCR with mean AUCs of 0.718 and 0.810 in the validation set, respectively. The decision curves of the clinical-radiomic models showed its values in clinical application. The SHAP method powerfully interpreted the prediction models both at a holistic and individual levels.

Conclusions

Our study highlights that the radiomic-based prediction models have more excellent abilities than clinical models and can effectively predict treatment response and optimize therapeutic strategies for patients with LARCs.

Evaluation of Ethos intelligent optimization engine for left locally advanced breast cancer

Purpose

To evaluate the feasibility to use a standard Ethos planning template to treat left-sided breast cancer with regional lymph nodes.

Material/Methods

The tuning cohort of 5 patients was used to create a planning template. The validation cohort included 15 patients treated for a locally advanced left breast cancer randomly enrolled. The Ethos planning template was tuned using standard 3 partial arc VMAT and two collimator rotation configurations: 45/285/345° and 30/60/330°. Re-planning was performed automatically using the template without editing. The study was conducted with a schedule of 42.3 Gy in 18 fractions to the breast/chestwall, internal mammary chain (IMC) and regional lymph nodes ("Nodes"). The PTV was defined as a 3D extension of the CTV with a margin of 7 mm, excluding the 5mm below the skin. The manual treatment plans were performed using Eclipse treatment planning system with AAA and PO algorithms (v15.6) and a manual arc VMAT configuration and imported in Ethos TPS (v1.1) for a dose calculation with Ethos Acuros algorithm. The automated plans were compared with the manual plans using PTV and CTV coverage, homogeneity and conformity indices (HI and CN) and doses to organs at risk (OAR) via DVH metrics. For each plan, the patient quality assurance (QA) were performed using Mobius3D and gamma index. Finally, two breast radiation oncologists performed a blinded assessment of the clinical acceptability of each of the three plans (manual and automated) for each patient.

Results

The manual and automated plans provided suitable treatment planning as regards dose constraints. The dosimetric comparison showed the CTV_breast D99% were significantly improved with both automated plans (p< 0,002) while PTV coverage was comparable. The doses to the organs at risk were equivalent for the three plans. Concerning treatment delivery, the Ethos-45° and Ethos-30° plans led to an increase in MUs compared to the manual plans, without affecting the beam on time. The average gamma index pass rates remained consistently above 98% regardless of the type of plan utilized. In the blinded evaluation, clinicians 1 and 2 assessed 13 out of 15 plans for Ethos 45° and 11 out of 15 plans for Ethos 30° as clinically acceptable.

Conclusion

Using a standard planning template for locally advanced breast cancer, the Ethos TPS provided automated plans that were clinically acceptable and comparable in quality to manually generated plans. Automated plans also dramatically reduce workflow and operator variability.

Geometrical accuracy of magnetic resonance imaging for ocular proton therapy planning

Background & purpose

Magnetic resonance imaging (MRI) is increasingly used in treatment preparation of ocular proton therapy, but its spatial accuracy might be limited by geometric distortions due to susceptibility artefacts. A correct geometry of the MR images is paramount since it defines where the dose will be delivered. In this study, we assessed the geometrical accuracy of ocular MRI.

Materials & methods

A dedicated ocular 3 T MRI protocol, with localized shimming and increased gradients, was compared to computed tomography (CT) and X-ray images in a phantom and in 15 uveal melanoma patients. The MRI protocol contained three-dimensional T2-weighted and T1-weighted sequences with an isotropic reconstruction resolution of 0.3-0.4 mm. Tantalum clips were identified by three observers and clip-clip distances were compared between T2-weighted and T1-weighted MRI, CT and X-ray images for the phantom and between MRI and X-ray images for the patients.

Results

Interobserver variability was below 0.35 mm for the phantom and 0.30(T1)/0.61(T2) mm in patients. Mean absolute differences between MRI and reference were below 0.27 ± 0.16 mm and 0.32 ± 0.23 mm for the phantom and in patients, respectively. In patients, clip-clip distances were slightly larger on MRI than on X-ray images (mean difference T1: 0.11 ± 0.38 mm, T2: 0.10 ± 0.44 mm). Differences did not increase at larger distances and did not correlate to interobserver variability.

Conclusions

A dedicated ocular MRI protocol can produce images of the eye with a geometrical accuracy below half the MRI acquisition voxel (<0.4 mm). Therefore, these images can be used for ocular proton therapy planning, both in the current model-based workflow and in proposed three-dimensional MR-based workflows.