Journal of Applied Clinical Medical Physics最新文献

{"title":"Using deep learning generated CBCT contours for online dose assessment of prostate SABR treatments","authors":"Conor Sinclair Smith,&nbsp;Isabelle Gagne,&nbsp;Karl Otto,&nbsp;Carter Kolbeck,&nbsp;Joshua Giambattista,&nbsp;Abraham Alexander,&nbsp;Sonja Murchison,&nbsp;Andrew Pritchard,&nbsp;Erika Chin","doi":"10.1002/acm2.70098","DOIUrl":"10.1002/acm2.70098","url":null,"abstract":"<p>Prostate Stereotactic Ablative Body Radiotherapy (SABR) is an ultra-hypofractionated treatment where small setup errors can lead to higher doses to organs at risk (OARs). Although bowel and bladder preparation protocols reduce inter-fraction variability, inconsistent patient adherence still results in OAR variability. At many centers without online adaptive machines, radiation therapists use decision trees (DTs) to visually assess patient setup, yet their application varies. To evaluate our center's DTs, we employed deep learning-generated cone-beam computed tomography (CBCT) contours to estimate daily doses to the rectum and bladder, comparing these with planned dose-volume metrics to guide future personalized DT development. Two hundred pretreatment CBCT scans from 40 prostate SABR patients (each receiving 40 Gy in five fractions) were auto-contoured retrospectively, and daily rectum and bladder doses were estimated by overlaying the planned dose on the CBCT using online rigid registration data. Dose-volume metrics were classified as “no”, “minor”, or “major” violations based on meeting preferred or mandatory goals. Twenty-seven percent of fractions exhibited at least one major bladder violation (with an additional 34% minor), while 14% of fractions had a major rectum violation (10% minor). Across treatments, five patients had recurring bladder V37 Gy major violations and two had rectum V36 Gy major violations. Bowel and bladder preparation significantly influenced OAR position and volume, leading to unmet mandatory goals. Our retrospective analysis underscores the significant impact of patient preparation on dosimetric outcomes. Our findings highlight that DTs based solely on visual assessment miss dose metric violations due to human error; only 23 of 59 under-filled bladder fractions were flagged. In addition to the insensitivity of visual assessments, variability in DT application further compromises patient setup evaluation. These analyses confirm that reliance on visual inspection alone can overlook deviations, emphasizing the need for automated tools to ensure adherence to dosimetric constraints in prostate SABR.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 6","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70098","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144012551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unsupervised non-small cell lung cancer tumor segmentation using cycled generative adversarial network with similarity-based discriminator","authors":"Chengyijue Fang,&nbsp;Xiaoyang Li,&nbsp;Yidong Yang","doi":"10.1002/acm2.70107","DOIUrl":"10.1002/acm2.70107","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Tumor segmentation is crucial for lung disease diagnosis and treatment. Most existing deep learning-based automatic segmentation methods rely on manually annotated data for network training.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;This study aims to develop an unsupervised tumor segmentation network smic-GAN by using a similarity-driven generative adversarial network trained with cycle strategy. The proposed method does not rely on any manual annotations and thus reduce the training data preparation workload.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;A total of 609 CT scans of lung cancer patients are collected, of which 504 are used for training, 35 for validation, and 70 for testing. Smic-GAN is developed and trained to transform lung CT slices with tumors into synthetic images without tumors. Residual images are obtained by subtracting synthetic images from original CT slices. Thresholding, 3D median filtering, morphological erosion, and dilation operations are implemented to generate binary tumor masks from the residual images. Dice similarity, positive predictive value (PPV), sensitivity (SEN), 95% Hausdorff distance (HD95) and average surface distance (ASD) are used to evaluate the accuracy of tumor contouring.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The smic-GAN method achieved a performance comparable to two supervised methods UNet and Incre-MRRN, and outperformed unsupervised cycle-GAN. The Dice value for smic-GAN is significantly better than cycle-GAN (74.5% &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mo&gt;±&lt;/mo&gt;\u0000 &lt;annotation&gt;$ pm $&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; 11.2% vs. 69.1% &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mo&gt;±&lt;/mo&gt;\u0000 &lt;annotation&gt;$ pm $&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; 16.0%, &lt;i&gt;p&lt;/i&gt; &lt; 0.05). The PPV for smic-GAN, UNet, and Incre-MRRN are 83.8% &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mo&gt;±&lt;/mo&gt;\u0000 &lt;annotation&gt;$ pm $&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; 21.5%,75.1% &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mo&gt;±&lt;/mo&gt;\u0000 &lt;annotation&gt;$ pm $&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; 19.7%, and 78.2% &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mo&gt;±&lt;/mo&gt;\u0000 &lt;annotation&gt;$ pm $&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 6","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70107","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144019621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fast, automated optimization of virtual monoenergetic images with the dual-energy image synthesizer for cone-beam CT","authors":"Andrew Keeler,&nbsp;Jason Luce,&nbsp;Mathias Lehmann,&nbsp;John C. Roeske,&nbsp;Hyejoo Kang","doi":"10.1002/acm2.70083","DOIUrl":"10.1002/acm2.70083","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Dual-energy cone-beam CT (DE-CBCT) has become subject of recent interest due to the ability to produce virtual monoenergetic images (VMIs) with improved soft-tissue contrast and reduced nonuniformity artifacts. However, efficient production and optimization of VMIs remains an under-explored part of DE-CBCT's application.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This work reports on the creation of DISC (dual-energy image synthesizer for CBCT), a newly developed, open-source user interface to efficiently produce and optimize VMIs with the eventual goal of clinical application.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Two sets of CBCT scans of a Catphan 604 phantom were acquired sequentially (80 and 140 kVp) using the on-board imager of a commercial linear accelerator. Material decomposition into aluminum (Al) and polymethyl-methylacrylate (PMMA) basis materials in the projection-domain and reconstruction with the Feldkamp–Davis–Kress (FDK) algorithm of basis material images were performed in the open-source Tomographic Iterative GPU-based REconstruction (TIGRE) Matlab toolkit. Using DISC, a series of VMIs were generated via linear combinations of the basis material images without reconstructing individual VMIs at different energies. Hounsfield units (HU) were computed using an energy-dependent fit over the range of 20–150 keV. VMI energies that maximized contrast-to-noise ratio (CNR) for various materials and minimized nonuniformity artifacts were determined with 1 keV precision.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Optimal CNR values for all material inserts ranged from 55 to 62 keV, showing an average CNR enhancement of 25% over the polychromatic images. Optimal uniformity is observed at 65 keV. Computed HUs show good agreement with theoretical values, with root-mean-squared error of 16 HU across the range of energies and materials.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>A spectrum of VMIs from DE-CBCT was efficiently produced with 1 keV precision using DISC. Optimal energies for both soft tissue contrast and nonuniformity reduction were quickly computed with high precision. Future work will expand DISC to generate other DE-derived image types and will explore the acquisition and optimization of DE patient images.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 6","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144025448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fault, error, and failure","authors":"Mohammad Bakhtiari","doi":"10.1002/acm2.70106","DOIUrl":"10.1002/acm2.70106","url":null,"abstract":"&lt;p&gt;The sequence and terminology are crucial: cyclically, through causation, a fault &lt;b&gt;activates,&lt;/b&gt; triggering an error. Error &lt;b&gt;propagates&lt;/b&gt; as errors. When an error is observed in the external environment, it becomes a failure. This failure can subsequently &lt;b&gt;cause&lt;/b&gt; a fault in the system it serves, and the cycle continues.&lt;/p&gt;&lt;p&gt;Understanding systems and their boundaries is essential because the definitions of fault, error, and failure can vary based on whether they are internal or external to a system. A system is an entity that interacts with other entities, including hardware, software, humans, and the physical world. It is composed of components, each of which can be another system, creating a recursive structure that stops when a component is considered atomic. The system boundary is crucial as it defines the common frontier between the system and its environment, determining the inputs and outputs of the system. A fault can occur within this boundary, which is the cause of an error. This error propagates through the system's internal states and may eventually become visible as a failure in the system's external state. This failure, perceived at the service interface, can act as an external fault for another system, initiating a new cycle of fault, error, and failure. The line between error detection and error observation is subtle but significant. While error detection refers to identifying discrepancies internally within a system, error observation occurs when these discrepancies manifest externally, leading to an observable failure state.&lt;/p&gt;&lt;p&gt;From a safety perspective, if humans are involved in the system, failures can be categorized as follows: If a failure does not affect the human, it is considered a near miss. If failure affects humans but causes no harm, it is classified as an incident. If failure results in harm, it is deemed an accident.&lt;span&gt;&lt;sup&gt;5, 6&lt;/sup&gt;&lt;/span&gt;&lt;/p&gt;&lt;p&gt;Figure 1 provides a summary of these definitions. It illustrates the relationship between fault, error, and failure. The graph suggests that functional safety applies primarily to engineering or technical domains when no patient is involved. If a patient is engaged, functional safety falls under the broader category of patient safety.&lt;/p&gt;&lt;p&gt;The human factor covers the entire system and timeline. Human errors happen in just a short amount of time or an instance.&lt;span&gt;&lt;sup&gt;7&lt;/sup&gt;&lt;/span&gt;&lt;/p&gt;&lt;p&gt;Figure 2 illustrates an example of a wrong couch density override to demonstrate that the fault creates an error, which propagates to the patient. If the error is observed, it becomes a failure and, depending on the beam intensity is categorized as a near miss, incident, or accident (adverse event). Detailed scenarios of the case are summarized in Table 1.&lt;/p&gt;&lt;p&gt;Daily QA trending serves as a helpful example. For instance, the beam output deviates from the baseline every day. These daily deviations are observed but are not termed errors or failures, as we define an ","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 6","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70106","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143991314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk analysis of the Unity 1.5T MR-Linac adapt-to-shape workflow","authors":"Jiayi Liang,&nbsp;Eric Aliotta,&nbsp;Neelam Tyagi,&nbsp;Paola Godoy Scripes,&nbsp;Nicolas Côté,&nbsp;Ergys Subashi,&nbsp;Qijie Huang,&nbsp;Lian Sun,&nbsp;Ching-Yun Chan,&nbsp;Angela Ng,&nbsp;Theresa Wunner,&nbsp;Victoria Brennan,&nbsp;Kaveh Zakeri,&nbsp;James Mechalakos","doi":"10.1002/acm2.70095","DOIUrl":"10.1002/acm2.70095","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background and Purpose</h3>\u0000 \u0000 <p>The adapt-to-shape (ATS) workflow on the Unity MR-Linac (Elekta AB, Stockholm, Sweden) allows for full replanning including recontouring and reoptimization⁵. Additional complexity to this workflow is added when the adaptation involves the use of MIM Maestro (MIM Software, Cleveland, OH) software in conjunction with Monaco (Elekta AB, Stockholm, Sweden). Given the interplay of various systems and the inherent complexity of the ATS workflow, a risk analysis would be instructive.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>Failure modes and effects analysis (FMEA) following Task Group 100¹³ was completed to evaluate the ATS workflow. A multi-disciplinary team was formed for this analysis. The team created a process map detailing the steps involved in ATS treating both the standard Monaco workflow and a workflow with the use of MIM software in parallel. From this, failure modes were identified, scored using three categories (likelihood of occurrence, severity, and detectability which multiplied create a risk priority number), and then mitigations for the top 20^th percentile of failure modes were found.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Risk analysis found 264 failure modes in the ATS workflow. Of those, 82 were high-ranking failure modes that ranked in the top 20^th percentile for risk priority number and severity scores. Although high-ranking failure modes were identified in each step in the process, 62 of them were found in the contouring and planning steps, highlighting key differences from adapt-to-position (ATP), where the importance of these steps are minimized. Mitigations are suggested for all high-ranking failure modes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The flexibility of the ATS workflow, which enables reoptimization of the treatment plan, also introduces potential critical points where errors can occur. There are more opportunities for error in ATS that can create unintentionally negative dosimetric impact. FMEA can help mitigate these risks by identifying and addressing potential failure points in the ATS process.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 7","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70095","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143966732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of the glottic surface dose in three-dimensional conformal radiotherapy for early-stage glottic cancer using a treatment planning system","authors":"Yuki Saito,&nbsp;Takahiro Kanehira,&nbsp;Takahito Yuki,&nbsp;Miyako Myojin","doi":"10.1002/acm2.70011","DOIUrl":"https://doi.org/10.1002/acm2.70011","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>In radiotherapy for early-stage glottic cancer, evaluating the target surface dose at the glottic air–tissue boundary is crucial, as buildup effect can cause underdosing. The accuracy of dose evaluation in the surrounding tissues is affected by both the dose calculation algorithms and the accuracy of the Hounsfield unit values in the glottic air cavities.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The objective of this study is to investigate the impact of dose calculation algorithms and material override on glottic surface dose calculations in three-dimensional conformal radiotherapy (3DCRT) for glottic cancer.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We retrospectively included three patients with early-stage glottic cancer treated with 3DCRT. Treatment planning based on the collapsed cone convolution (CCC) algorithm in the treatment planning system with a 1-mm dose grid was conducted using a prescribed dose of 65 Gy in 26 fractions. The contours of the glottic air cavities and the surrounding glottic tissues were delineated for material override to air and water, respectively to assign correct materials in dose calculation. Each treatment plan was initially calculated using CCC without material override (CCC_w/o) and recalculated using CCC with material override (CCC_w) as well as photon Monte Carlo (pMC) algorithm with and without material override (pMC_w and pMC_w/o, respectively). A 1-mm glottic surface dose (D_99%) was evaluated using CCC_w/o, CCC_w, pMC_w, and pMC_w/o.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>PMC_w predicted a ∼13.0% reduction in the glottic surface dose compared with the prescribed dose. CCC_w/o, CCC_w, and pMC_w/o overestimated the dose by ∼10.0% compared with pMC_w. The difference between CCC_w/o and pMC_w/o was minimal (0.6%); pMC_w/o significantly overestimated (by 10.8%) the dose compared with pMC_w, indicating the significant impact of material override in pMC.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Monte Carlo dose calculations with material override are essential for the accurate surface dose calculation in 3DCRT for glottic cancer. Without appropriate material override, both CCC and pMC overestimate the surface dose.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143919850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel CT radiomics models for the postoperative prediction of early recurrence of resectable pancreatic adenocarcinoma: A single-center retrospective study in China","authors":"Xinze Du,&nbsp;Yongsu Ma,&nbsp;Kexin Wang,&nbsp;Xiejian Zhong,&nbsp;Jianxin Wang,&nbsp;Xiaodong Tian,&nbsp;Xiaoying Wang,&nbsp;Yinmo Yang","doi":"10.1002/acm2.70092","DOIUrl":"10.1002/acm2.70092","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To assess the predictive capability of CT radiomics features for early recurrence (ER) of pancreatic ductal adenocarcinoma (PDAC).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Postoperative PDAC patients were retrospectively selected, all of whom had undergone preoperative CT imaging and surgery. Both patients with resectable or borderline-resectable pancreatic cancer met the eligibility criteria in this study. However, owing to the differences in treatment strategies and such, this research mainly focused on patients with resectable pancreatic cancer. All patients were subject to follow-up assessments for a minimum of 9 months. A total of 250 cases meeting the inclusion criteria were included. A clinical model, a conventional radiomics model, and a deep-radiomics model were constructed for ER prediction (defined as occurring within 9 months) in the training set. A model based on the TNM staging was utilized as a baseline for comparison. Assessment of the models' performance was based on the area under the receiver operating characteristic curve (AUC). Additionally, precision-recall (PR) analysis and calibration assessments were conducted for model evaluation. Furthermore, the clinical utility of the models was evaluated through decision curve analysis (DCA), net reclassification improvement (NRI), and improvement of reclassification index (IRI).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>In the test set, the AUC values for ER prediction were as follows: TNM staging, ROC-AUC = 0.673 (95% CI: 0.550, 0.795), PR-AUC = 0.362 (95% CI: 0.493, 0.710); clinical model, ROC-AUC = 0.640 (95% CI: 0.504, 0.775), PR-AUC = 0.481 (95% CI: 0.520, 0.735); radiomics model, ROC-AUC = 0.722 (95% CI: 0.604, 0.839), PR-AUC = 0.575 (95% CI: 0.466, 0.686); and deep-radiomics model, which exhibited the highest ROC-AUC of 0.895 (95% CI: 0.820, 0.970), PR-AUC = 0.834 (95% CI: 0.767, 0.923). The difference in both ROC-AUC and PR-AUC for the deep-radiomics model was statistically significant when compared to the other scores (all <i>p</i> &lt; 0.05). The DCA curve of the deep-radiomics model outperformed the other models. NRI and IRI analyses demonstrated that the deep-radiomics model significantly enhances risk classification compared to the other prediction methods (all <i>p</i> &lt; 0.05).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The predictive performance of deep features based on CT images exhibits favorable outcomes in predicting early recurrence.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 6","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70092","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144005242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine stability and dosimetry for ultra-high dose rate FLASH radiotherapy human clinical protocol","authors":"Patrik Gonçalves Jorge,&nbsp;Reiner Geyer,&nbsp;Rémy Kinj,&nbsp;Luis Schiappacasse,&nbsp;Wendy Jeanneret-Sozzi,&nbsp;Jean Bourhis,&nbsp;Fernanda Herrera,&nbsp;François Bochud,&nbsp;Claude Bailat,&nbsp;Raphaël Moeckli","doi":"10.1002/acm2.70102","DOIUrl":"10.1002/acm2.70102","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The FLASH effect, induced by ultra-high dose rate (UHDR) irradiations, offers the potential to spare normal tissue while effectively treating tumors. It is important to achieve precise and accurate dose delivery and to establish reliable detector systems, particularly for clinical trials needed to help the clinical transfer of FLASH-Radiotherapy (FLASH-RT). However, the use of monitoring chambers with UHDR beams is presently limited, leading to the reliance on passive dosimetry and machine stability.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study aimed to investigate the energy and output stability of a UHDR Mobetron (IntraOp, USA) and to compare it with its conventional dose rate (CDR) mode. Furthermore, we assessed the dosimetric accuracy of a human clinical protocol for FLASH-RT.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Over a 26-month duration, we assessed the short- and long-term stability of the output and energy of the Mobetron system. Daily checks were conducted for 9 MeV CDR and UHDR. In parallel, the IMPulse clinical trial involving patients with skin metastases from melanoma was initiated. Prescription doses ranging from 22 to 28 Gy were administered. Pre-, post-, and <i>in vivo</i> dosimetry using alanine and thermoluminescent dosimeters (TLDs) was performed and compared to the prescription doses.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Short-term output fluctuations remained below 0.6 % and 1 % for 9 MeV CDR and UHDR, respectively. Long-term output fluctuations were within 2 % and the long-term energy fluctuations were below 2 mm (R₅₀) for both modes. The delivered doses of the IMPulse trial showed deviations below 4 % compared to prescription doses for all patients.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The Mobetron system demonstrated favorable short- and long-term stability. There was a good agreement between the prescribed and the measured dose for the clinical IMPulse trial. The stability of this UHDR machine allows us to effectively conduct human clinical protocols as well as preclinical experiments, even in the absence of a real-time monitoring system.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 6","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70102","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144007183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving dose delivery in non-coplanar cranial SRS: Stereoscopic x-ray-guided mitigation of table walkout errors","authors":"Yohan A. Walter,&nbsp;Philip F. Durham,&nbsp;Anne N. Hubbard,&nbsp;William E. Burrell,&nbsp;Hsinshun T. Wu","doi":"10.1002/acm2.70099","DOIUrl":"10.1002/acm2.70099","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>Linear accelerator (LINAC)-based single-isocenter multi-target (SIMT) treatment has streamlined the cranial stereotactic radiosurgery (SRS) workflow. Though efficient, SIMT delivery adds additional challenges that should be considered, including increased sensitivity to rotational errors for off-isocenter targets. Room-mounted imaging systems carry the advantage of allowing fast, low-dose imaging at nonzero couch angles, which may combat the effects of table walkout and residual rotational errors. Here, we performed a series of end-to-end tests to determine if these corrections correlate with a measurable difference in delivered dose and to assess the overall accuracy of SIMT delivery on our LINAC-based SRS platform.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Ten treatment plans of increasing complexity were created in the Elements 4.0 treatment planning system (TPS, Brainlab AG). Plans were delivered on an Elekta Versa HD LINAC (Elekta AB) with the ExacTrac (ETX) imaging system (Brainlab AG). A CT scan of a StereoPHAN with SRS MapCHECK (Sun Nuclear) was imported into the TPS. Measured targets were contoured on the detector plane. Plans used 4–15 treatment arcs and 4–6 couch angles. ETX was used for initial phantom positioning. Dose measurements were performed for each plan with and without ETX-guided corrections at all table angles.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Translational and rotational residual shifts were all submillimeter and ≤1.0 degrees, respectively, across all table angles. Using 3.0%/1.0 mm gamma criteria, all gamma pass rates (GPR) were either equal or improved when ETX shifts were executed, though the difference was not statistically significant (<i>p</i> = 0.076). However, using 2.0%/0.5 mm criteria, GPR improved significantly (<i>p</i> = 0.016) with ETX repositioning. The average GPR improvement was 4.5% ± 4.8%.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Results demonstrate that repositioning corrections at each table angle improve agreement between planned and delivered dose at the submillimeter level. The test treatment plans in this study may be used for assessment of end-to-end treatment delivery accuracy for complex LINAC-based stereotactic radiotherapy procedures.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 6","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70099","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144014969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Python package for fast GPU-based proton pencil beam dose calculation","authors":"Mahasweta Bhattacharya,&nbsp;Calin Reamy,&nbsp;Heng Li,&nbsp;Junghoon Lee,&nbsp;William T. Hrinivich","doi":"10.1002/acm2.70093","DOIUrl":"10.1002/acm2.70093","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>Open-source GPU-based Monte Carlo (MC) proton dose calculation algorithms provide high speed and unparalleled accuracy but can be complex to integrate with new applications and remain slower than GPU-based pencil beam (PB) methods, which sacrifice some physical accuracy for sub-second plan calculation. We developed and validated a Python package implementing a GPU-based double Gaussian PB algorithm for intensity-modulated proton therapy (IMPT) planning research applications requiring a simple, widely compatible, and ultra-fast proton dose calculation solution.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Beam parameters were derived from pristine Bragg peaks generated with MC for 98 energies in our clinical treatment planning system (TPS). We validated the PB approach against measurements by comparing lateral spot profiles (using single-Gaussian sigma) and proton ranges (using R80) for pristine Bragg peaks, as well as spread-out Bragg peaks (SOBPs) in water. Further comparisons of PB and MC from the TPS were performed in a heterogeneous digital phantom and patient plans for four cancer sites using 3D gamma passing rates and dose metrics.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The PB algorithm enabled dose calculation following a single Python import statement. Mean ± standard deviation (SD) errors in sigma, R80, and SOBP dose were 0.05 ± 0.01, 0.0 ± 0.1 mm, and 0.4 ± 1.1%, respectively. Mean ± SD patient plan computation time was 0.28 ± 0.07 s for PB versus 4.68 ± 2.68 s for MC. Mean ± SD gamma passing rate at 2%/2 mm criteria was 96.0 ± 5.1%, and the mean ± SD percent difference in dose metrics was 0.5 ± 3.6%. PB accuracy degraded beyond bone and lung boundaries, characterized by inaccuracies in lateral proton scatter.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>We developed a GPU-based proton PB algorithm compiled as a Python package, providing simple beam modeling, interface, and fast dose calculation for IMPT plan optimization research and development. Like other PB algorithms, accuracy is limited in highly heterogeneous regions such as the thorax.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 6","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70093","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144018787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}