Journal of Medical Physics最新文献

{"title":"Deep Learning-based Volumetric Modulated Arc Therapy Dose Prediction with Uncertainty Quantification for Left- and Right-sided Breast Cancer: A Single-institution Feasibility Study.","authors":"Thanakorn Sukha, Chanon Puttanawarut, Nat Sirirutbunkajorn, Pimolpun Changkaew, Nauljun Stansook, Suphalak Khachonkham","doi":"10.4103/jmp.jmp_147_26","DOIUrl":"10.4103/jmp.jmp_147_26","url":null,"abstract":"<p><strong>Background: </strong>Volumetric-modulated arc therapy (VMAT) enables precise tumor targeting while minimizing dose to organs at risk but requires time-consuming planning with quality variability. Most deep learning (DL) dose prediction studies focus on left-sided breast cancer, with limited exploration of right-sided cases or integrated uncertainty quantification. This study investigates the feasibility of a DL model for VMAT dose prediction in both left-and right-sided breast cancer, incorporating Monte Carlo dropout (MCD)-based uncertainty quantification within a single-institution dataset.</p><p><strong>Methods: </strong>A total of 68 clinically accepted breast cancer VMAT treatment plans (41 left-sided, 27 right-sided) were collected. A three-dimensional U-Net architecture was implemented. Two models with dropout rates of 0.25 (M0.25) and 0.50 (M0.50) were developed. MCD was applied for uncertainty quantification. Performance was evaluated using mean absolute error (MAE), gamma passing rate (GPR), and isodose Dice similarity coefficients.</p><p><strong>Results: </strong>The overall body MAE was 1.7 ± 0.11 Gy (M0.25) and 1.93 ± 0.11 Gy (M0.50). For the planning target volume (PTV) of 42.56 Gy, MAE was 0.93 ± 0.17 Gy (M0.25) and 0.88 ± 0.15 Gy (M0.50). The heart showed the lowest errors (M0.25: 0.87 ± 0.34 Gy; M0.50: 1.06 ± 0.38 Gy), while contralateral lung exhibited higher uncertainties. GPR at 3%/3 mm reached 85.5% for both models, with no significant difference between left- and right-sided cases. Uncertainty maps correlated with prediction errors (Pearson correlation =0.59) and identified regions with atypical anatomies.</p><p><strong>Conclusions: </strong>The proposed model supports the feasibility of VMAT dose prediction for both left-and right-sided breast cancer, while the uncertainty maps provide additional confidence information to assist clinical interpretation and decision-making. Uncertainty maps correlated with prediction error and flagged atypical cases. External validation with a larger, multi-institutional dataset is required before clinical implementation can be considered.</p>","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"51 3","pages":"504-512"},"PeriodicalIF":1.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13675035/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149217299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Benchmark Readiness of Open-access Musculoskeletal Radiograph Datasets for Foundation Model Segmentation: A Systematic Audit.","authors":"Sri Nikhil Zallipalli, Kaushik Rao Juvvadi, Gowthamini Asokan, Saichand Linga, Nikitha Devi Thotapalli, Preethii Asokan","doi":"10.4103/jmp.jmp_146_26","DOIUrl":"10.4103/jmp.jmp_146_26","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background and purpose: &lt;/strong&gt;Foundation models including the Segment Anything Model 2 have generated considerable interest as annotation-efficient tools for musculoskeletal (MSK) radiographic image segmentation. Rigorous evaluation requires open-access datasets with well-characterized, high-quality segmentation annotations. No systematic analysis of whether currently available open-access MSK radiograph datasets meet this requirement has been published. This study evaluates the annotation completeness, image quality, and benchmark readiness of three widely cited open-access MSK imaging resources used for radiographic benchmarking: FracAtlas and Musculoskeletal Radiographs (MURA), two native projectional radiograph datasets, and computed tomography (CT)-derived digitally reconstructed radiographs (DRRs) generated from TotalSegmentator, an open-access CT segmentation dataset.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Materials and methods: &lt;/strong&gt;A structured, reproducible audit framework was applied to FracAtlas (4083 images), a 500-study stratified random sample from MURA (seven body regions), and 120 DRRs generated from TotalSegmentator CT volumes. Annotation completeness was assessed across four tiers. Mask quality in FracAtlas was graded independently by two raters: two orthopedic surgical trainees and a radiologist, across four levels, with inter-rater agreement quantified by Cohen's weighted kappa. Image quality was characterized using five no-reference metrics. Domain gap was quantified using structural similarity (SSIM) and Fréchet Inception Distance (FID). A five-criterion, equally weighted Benchmark Readiness Score (BRS) framework was developed, applied to all three datasets, and evaluated through sensitivity analysis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;MURA contained no pixel-level segmentation annotations, rendering it unsuitable for direct segmentation benchmarking. FracAtlas provided whole-bone-quality (Grade A) masks for only 175 of 4,083 images (4.3%); 61.4% of masks were fracture-region-only. Inter-rater agreement for mask grading was substantial (Cohen's κw = 0.76; 95% confidence interval: 0.71-0.81). TotalSegmentator-digitally reconstructed radiograph (DRRs) provided complete synthetic bone masks but demonstrated a substantial domain gap from real radiographs (mean SSIM 0.41 ± 0.09; FID 87.3). BRS scores were: MURA 4/25, FracAtlas 8/25, FracAtlas-Complete subset 12/25, and TotalSegmentator-DRR 16/25. Dataset ranking was stable across sensitivity analysis thresholds of 18/25, 20/25, and 22/25; no dataset reached publication-readiness under any threshold.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;No currently available open-access MSK radiograph dataset fully meets the requirements for rigorous foundation model segmentation benchmarking. The FracAtlas-Complete subset (&lt;i&gt;n&lt;/i&gt; = 175) represents the highest-quality immediately available resource for whole-bone segmentation. The BRS framework provides a reproducible, transparent instrument for dataset q","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"51 3","pages":"662-671"},"PeriodicalIF":1.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13675026/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149217086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Simulation Study of Carbon Dot-Assisted Photothermal Heating in Tumor Tissue.","authors":"M Anne Jose, Bindu Krishnan","doi":"10.4103/jmp.jmp_90_26","DOIUrl":"10.4103/jmp.jmp_90_26","url":null,"abstract":"<p><strong>Purpose: </strong>The purpose of the study was to investigate photothermal heating and thermal damage in breast tumor tissue under laser irradiation in the presence of carbon dots acting as enhanced optical absorbers.</p><p><strong>Methods: </strong>A two-dimensional numerical model based on the transient bioheat transfer equation was developed by coupling radiative heat transfer and solved using the finite element method in COMSOL Multiphysics. The tumor region was modeled as a circular domain embedded within normal breast tissue. Thermal damage was quantified using the cumulative equivalent minutes at 43°C (CEM43) model. Parametric analysis was carried out to evaluate the effects of tumor absorption coefficient, carbon dot absorption coefficient, photothermal conversion efficiency, blood perfusion rate, and tumor depth on temperature evolution.</p><p><strong>Results: </strong>Localized heating was observed within the tumor region, with peak temperatures reaching approximately 60°C after 600s of irradiation. Temperature elevation increased with increasing optical absorption and irradiation duration, whereas blood perfusion reduced thermal accumulation. Thermal damage analysis indicated that the irreversible damage threshold (CEM43 >60 min) was achieved throughout the tumor region after 600s of irradiation, suggesting effective thermal destruction of tumor tissue, while damage to surrounding healthy tissue remained limited.</p><p><strong>Conclusions: </strong>The model demonstrates that carbon dots can effectively enhance photothermal heating in tumor tissue and achieve thermal damage. The findings provide a computational framework for optimizing laser-based tumor therapy while maintaining thermal safety.</p>","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"51 3","pages":"494-503"},"PeriodicalIF":1.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13675014/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[¹⁸F]F-Prostate-specific Membrane Antigen-1007 and [⁶⁸Ga] Ga-Prostate-specific Membrane Antigen-11 Positron Emission Tomography in Prostate Cancer: Do Standardized Uptake Value Correlations Dictate Choice?","authors":"Bal Sanghera, Gerry Lowe, Sophie Sanghera, Wai-Lup Wong","doi":"10.4103/jmp.jmp_56_26","DOIUrl":"10.4103/jmp.jmp_56_26","url":null,"abstract":"<p><strong>Objective: </strong>To characterize correlations (ρ) between imaging and patient-related parameters in [¹⁸F] F-prostate-specific membrane antigen (PSMA)-1007 and [⁶⁸Ga] Ga-PSMA-11 positron emission tomography-computed tomography (PET-CT) and to determine whether observed differences influence clinical tracer interchangeability.</p><p><strong>Materials and methods: </strong>Two retrospective PET-CT cohorts from a single-center database were analyzed, comprising patients imaged for suspected prostate cancer with either tracer. Spearman rank intra-tracer correlation coefficients were calculated between weight, body surface area (BSA), lean body mass (LBM), lesion maximum standardized uptake value (SUVmax) (T), liver mean SUV (SUVmean) (B), tumor-to-background ratio (T/B), and metabolic tumor volume. Inter-tracer comparisons were performed using Fisher-transformed paired correlation coefficients, with Bonferroni correction to control type I error.</p><p><strong>Results: </strong>Mann-Whitney testing demonstrated no significant differences in weight (<i>P</i> = 0.50), BSA (<i>P</i> = 0.50), or LBM (<i>P</i> = 0.47) distributions between cohorts. Intra-tracer heatmaps showed broadly similar correlation patterns and significance distributions across tracers. Inter-tracer analysis identified significant differences in correlations between liver SUVmean with weight (ρ = 0.43, <i>P</i> = 0.0001), BSA (ρ = 0.29, <i>P</i> = 0.0001), and LBM (ρ = 0.29, <i>P</i> = 0.0001). These differences were nullified when standard weight-based liver SUVmean normalization for [⁶⁸Ga] Ga-PSMA-11 was replaced with LBM normalization (weight: ρ = -0.01, <i>P</i> = 0.96; BSA: ρ = -0.04, <i>P</i> = 0.75; LBM: ρ = -0.04, <i>P</i> = 0.75) or BSA normalization (weight: ρ = -0.09, <i>P</i> = 0.49; BSA: ρ = -0.12, <i>P</i> = 0.36; LBM: ρ = -0.12, <i>P</i> = 0.36).</p><p><strong>Conclusion: </strong>Correlation structures for the two tracers were broadly comparable across most imaging and demographic variables. Observed differences in liver SUVmean correlations with body habitus parameters were mitigated by BSA or LBM normalization. These findings support clinical tracer interchangeability. However, full characterization is recommended when alternative SUV normalizations are applied or when liver SUVmean is used as background reference tissue in tumor-to-background trial selection criteria.</p>","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"51 3","pages":"672-679"},"PeriodicalIF":1.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13675025/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149217302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Commissioning of Medical Linear Accelerators in India: A Survey of Regulatory Compliance, Clinical Implementation, and Professional Challenges.","authors":"R C Muhammed Anaz, Shrikant Kale, Ritu Raj Upreti, Raghavendra Hajare, Shubham Gadge, Avdhoot Sutar, Udita Upreti, Yogesh Ghadi, Priyadarshini Sahoo, Ritesh Mhatre, Jai Prakash Agarwal","doi":"10.4103/jmp.jmp_124_26","DOIUrl":"10.4103/jmp.jmp_124_26","url":null,"abstract":"<p><strong>Purpose: </strong>This study evaluated current clinical practices, regulatory compliance, and professional challenges encountered during the commissioning of advanced Linear Accelerators (LINACs) in India.</p><p><strong>Materials and methods: </strong>A nationwide survey using a structured 20-item questionnaire was conducted from August 2025 to March 2026. The questionnaire covered commissioning practices, staffing and vendor support, quality assurance (QA) procedures, clinical implementation, and participant opinions and suggestions. Responses were analyzed to assess trends in technology adoption and variations in commissioning workflows.</p><p><strong>Results: </strong>Data were obtained for 75 LINACs commissioned between 2020 and 2025 across 67 institutions. Frequently commissioned systems included Varian TrueBeam (37.3%), Varian Halcyon (18.7%), and Elekta Versa HD (16%). Advanced treatment techniques such as Volumetric Modulated Arc Therapy (VMAT), Stereotactic Radio Surgery (SRS), and Stereotactic Body Radiotherapy (SBRT) were commonly implemented; however, Total Skin Electron Therapy (TSET) and Total Body Irradiation (TBI) commissioning remained limited. More than one-fourth of the participants (26.7%) had no prior experience in LINAC commissioning. 87.8% participants considered the existing Atomic Energy Regulatory Board's QA requirements adequate, 89.3% supported mandatory inclusion of Treatment Planning System QA within the licensing process. Small-field dosimetry following International Atomic Energy Agency (IAEA) TRS-483 guidelines was performed by 60.8% of centers, while 77.3% had not participated in external dosimetric audits. Major challenges included workforce shortages and balancing commissioning with routine clinical responsibilities. Independent peer review before clinical implementation was recommended by 81.3% of respondents.</p><p><strong>Conclusion: </strong>Advanced LINAC technologies are being successfully adopted in India; however, greater standardization, structured training, external audit participation, and independent verification frameworks are required to strengthen commissioning quality and patient safety across radiotherapy centers nationwide.</p>","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"51 3","pages":"610-617"},"PeriodicalIF":1.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13675031/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149217159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dosimetric Impact of Simultaneous and Separate Planning with Fixed/Iris Collimators on Brain Doses in Multiple Brain Metastases.","authors":"Hatice Tataroglu, Ela Delikgöz Soykut, Mustafa Özer, Seher Pirzirenli","doi":"10.4103/jmp.jmp_11_26","DOIUrl":"10.4103/jmp.jmp_11_26","url":null,"abstract":"<p><strong>Background: </strong>CyberKnife^® stereotactic radiosurgery enables frameless, image-guided treatment of multiple brain metastases. Collimator type (Fixed vs. Iris) and planning strategy (Simultaneous vs. Separate) may influence low-dose normal brain exposure, plan quality, and treatment delivery efficiency.</p><p><strong>Objectives: </strong>The objective of the study was to compare the dosimetric performance of different collimator types and planning strategies in patients with three or four brain metastases under standardized planning conditions.</p><p><strong>Materials and methods: </strong>Twenty patients were retrospectively replanned using four treatment configurations: Fixed Separate, Fixed Simultaneous, Iris Separate, and Iris Simultaneous. All plans were generated using identical prescription doses and planning objectives. Dosimetric evaluation included normal brain V8, V10, V12 (volumes receiving ≥8 Gy, ≥10 Gy, and ≥12 Gy), mean brain dose (Vmean), Paddick conformity index (PCI), and gradient index (GI). Comparisons were performed using Friedman and Wilcoxon signed-rank tests. Estimated treatment time was recorded for all plans.</p><p><strong>Results: </strong>Significant differences were observed for V8 (<i>P</i> = 0.039), Vmean (<i>P</i> = 0.019), and PCI (<i>P</i> < 0.001), whereas V10 (<i>P</i> = 0.180), V12 (<i>P</i> = 0.103), and GI (<i>P</i> = 0.420) showed no significant differences among the planning approaches. Median dosimetric values tended to favor Iris Simultaneous planning for reducing low-dose normal brain exposure. The significant PCI difference was primarily attributable to lower conformity in the Iris Separate plans. Total planning target volume demonstrated a near-significant positive association with V12 (<i>P</i> = 0.057). Iris-based plans were associated with shorter estimated treatment times.</p><p><strong>Conclusion: </strong>Under standardized planning conditions, Iris Simultaneous planning demonstrated favorable trends in reducing low-dose normal brain exposure while improving treatment delivery efficiency. Although conformity differed significantly among planning approaches, mainly because of lower PCI values in the Iris Separate plans, dose gradient characteristics remained comparable. These findings suggest that planning strategy may influence conformity more than dose fall-off in CyberKnife treatment of multiple brain metastases. Nevertheless, given the limited sample size, these results should be interpreted cautiously and validated in larger prospective studies.</p>","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"51 3","pages":"628-636"},"PeriodicalIF":1.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13675021/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149217330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond Gamma Analysis: An Accessibility-oriented Framework for Comparative Evaluation of Patient-specific Radiotherapy Quality Assurance.","authors":"Vikram, Raj Pal Singh, Manindra Bhushan","doi":"10.4103/jmp.jmp_115_26","DOIUrl":"10.4103/jmp.jmp_115_26","url":null,"abstract":"<p><strong>Background: </strong>Patient-specific quality assurance (QA) in radiotherapy is traditionally performed using measurement-based systems that require dedicated hardware, machine time, and manpower. Although software-based QA approaches have emerged as efficient alternatives, their comparative accessibility and operational performance remain insufficiently investigated. This study evaluated conventional and software-based QA methodologies using integrated dosimetric, temporal, and economic metrics.</p><p><strong>Materials and methods: </strong>A total of 90 clinically approved treatment plans, including head-and-neck, pelvis, and lung volumetric modulated arc therapy/stereotactic body radiotherapy cases, were retrospectively analysed. All plans were generated in Eclipse TPS v15.6 and evaluated using ArcCHECK phantom-based QA, electronic portal imaging device (EPID)-based QA, and log file-based QA. Gamma analysis was performed using 3%/2 mm criteria with a 10% dose threshold. Workflow time and relative cost burden were recorded for each method. Three composite indices were defined: QA Cost Efficiency Index (QCEI), QA Efficiency Index (QEI), and QA Accessibility Index (QAI). Statistical comparisons were performed using paired analysis with Bonferroni correction.</p><p><strong>Results: </strong>All QA methods achieved clinically acceptable gamma passing rates. Log file-based QA demonstrated the highest median gamma passing rate; however, the absolute differences between methodologies were small, and all approaches exceeded the predefined clinical acceptance criterion (≥95%). ArcCHECK showed the greatest workflow time and cost burden, whereas log file-based QA demonstrated the highest QCEI, QEI, and QAI values. EPID-based QA showed intermediate performance. All pairwise differences in accessibility metrics were statistically significant (<i>P</i> < 0.05).</p><p><strong>Conclusion: </strong>Software-based QA approaches significantly improved operational efficiency and accessibility while maintaining clinically acceptable dosimetric agreement. However, further prospective, multi-institutional validation of the proposed accessibility framework is warranted.</p>","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"51 3","pages":"637-645"},"PeriodicalIF":1.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13675054/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149217170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of Calculation Grid Size Impact on Plan Quality and Gamma Evaluation Metrics in Intracranial Stereotactic Radiotherapy.","authors":"Ravindra Shende, Joyce Alexis, R S Rahul","doi":"10.4103/jmp.jmp_70_26","DOIUrl":"10.4103/jmp.jmp_70_26","url":null,"abstract":"<p><strong>Background: </strong>This study aimed to evaluate the impact of dose calculation grid size on dosimetric accuracy, plan quality indices, and patient-specific quality-assurance (QA) outcomes in intracranial stereotactic radiotherapy (SRT).</p><p><strong>Materials and methods: </strong>Fifteen retrospective intracranial SRT plans treated with HyperArc volumetric modulated arc therapy were re-planned using five dose calculation grid sizes of 1.0, 1.25, 1.5, 1.75, and 2.0 mm in the Eclipse treatment planning system. All plans were made to deliver 30 Gy in 5 fractions using 6 MV flattening-filter-free beams and calculated using the Anisotropic analytical algorithm. Plan quality was evaluated using multiple plan quality metrics, including RTOG quality coverage index (QI), Paddick conformity index, homogeneity index, and gradient index (GI). Dose spillage outside the target was assessed using volumetric dose indices. Patient-specific QA was performed using SRS MapCHECK with gamma-analysis under multiple criteria. Statistical comparisons were conducted using dependent paired <i>t</i>-tests, repeated-measure analysis of variance, and Pearson correlation coefficient analysis.</p><p><strong>Results: </strong>Increasing the dose calculation grid size resulted in a systematic increase in planning target volume mean dose and a corresponding reduction in D_95%, indicating loss of target coverage attributable to spatial dose averaging. GI showed the strongest dependence on grid size, demonstrating progressive degradation of dose fall-off with coarser grids. Low-dose spillage to the normal brain and brainstem increased significantly with increasing grid size, while distant organs at risk were comparatively less affected. Gamma passing rates declined progressively with increasing grid size, particularly under the most stringent evaluation criteria.</p><p><strong>Conclusion: </strong>Study highlights that dose grid size variations significantly influence dosimetric accuracy, plan quality, and QA robustness in intracranial SRT. Coarser calculation grids compromise target coverage and gradient fidelity, while finer grids (≤1.25 mm) preserve near-reference dosimetric accuracy. Adopting a finer calculation grid ensures reliable dose computation, consistent patient-specific QA, and optimal clinical performance in SRT.</p>","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"51 3","pages":"546-556"},"PeriodicalIF":1.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13675050/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149217078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Linear-quadratic to Machine Learning in Radiobiological Modeling for Radiotherapy Planning: Resolving the Complexity-Utility Paradox with Hybrid Physics-informed Neural Networks.","authors":"Kalyan Mondal, Anuj Vijay, Abhijit Mandal, Ganeshkumar Patel","doi":"10.4103/jmp.jmp_105_26","DOIUrl":"10.4103/jmp.jmp_105_26","url":null,"abstract":"<p><p>Radiobiological modeling for radiotherapy planning has evolved across three overlapping paradigms: postplanning biological evaluation using empirical dose-response relationships (c. 1960), biological optimization integrated into treatment planning system objective functions (c. 2000), and artificial intelligence (AI)-driven adaptive prediction (c. 2012-present). Despite this progression, increasing model complexity has not produced proportional clinical utility - a tension we describe as the complexity-utility paradox. A structured search of PubMed, Scopus and Web of Science (January 1960 to March 2026) yielded 71 included studies; evidence is synthesized narratively with critical appraisal of landmark mechanistic models, consensus guidelines and recent meta-analytic data. We operationalize the paradox across three measurable gaps: A performance generalizability gap, in which external validation of machine-learning normal tissue complication probability (NTCP) models is approximately 9% lower in relative terms (absolute difference 0.07; pooled internal area under a receiver operating characteristic [AUROC]: 0.76, 95% confidence interval [CI]: 0.73-0.78, versus pooled external AUROC: 0.69, 95% CI: 0.64-0.73), noting that 121 of the 225 pooled models were rated at high risk of bias on PROBAST and that the internal and external estimates derive from different model sets; an adoption gap, in which Quantitative Analyses of Normal Tissue Effects in the Clinic-derived Lyman-Kutcher-Burman constraints retain primary plan-evaluation authority despite demonstrated AI improvements; and an interpretability gap, in which regulatory and clinical-trust barriers penalize opaque model architectures. We argue that principled integration, rather than paradigm replacement, offers the most tractable resolution. Hybrid physics-informed neural networks (PINNs), anchoring data-driven learning to validated mechanistic priors within a federated multi-institutional training framework and uncertainty propagation compliant with the Guide to the Expression of Uncertainty in Measurement, may narrow all three gaps simultaneously. This position is explicitly caveated: PINNs inherit the bias of the embedded prior, and current radiotherapy-specific evidence remains preliminary. To our knowledge, no externally validated PINN-based NTCP model has yet been published, so the proposal is offered as a research direction rather than as an evidenced clinical solution. A tiered validation framework is proposed for structured clinical translation.</p>","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"51 3","pages":"447-461"},"PeriodicalIF":1.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13675020/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149217335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Radiobiological Evaluation of Radiotherapy Techniques Using Equivalent Uniform Dose, Tumor Control Probability, and Normal Tissue Complication Probability in Postmastectomy Breast Cancer.","authors":"Garima Gaur, Gurpreet Kaur, Baltej Singh, Pardeep Garg, Vinod Kumar Dangwal, Raja Paramjeet Singh Banipal","doi":"10.4103/jmp.jmp_106_26","DOIUrl":"10.4103/jmp.jmp_106_26","url":null,"abstract":"<p><strong>Introduction: </strong>Postmastectomy radiotherapy plays a pivotal role in improving locoregional control and survival outcomes in early as well as in advanced-stage breast cancer patients. With the transition from conventional radiotherapy techniques to advanced intensity-modulated approaches, evaluation based solely on physical dose parameters is no longer sufficient. Radiobiological modelling provides a more clinically meaningful framework by quantifying tumor control and normal tissue complications.</p><p><strong>Objectives: </strong>The objective of this prospective study was to evaluate the radiobiological differences between three radiotherapy planning techniques, namely three-dimensional conformal radiotherapy (3DCRT), field-in-field forward planned intensity modulated radiotherapy (FIF-FP-IMRT), and inversely planned intensity modulated radiotherapy (IP-IMRT) in postmastectomy breast cancer patients.</p><p><strong>Materials and methods: </strong>The present prospective study involved 150 postmastectomy breast cancer patients who have undergone chemotherapy treatment. Each patient was planned with three radiotherapy planning techniques, namely 3DCRT, FIF-FP-IMRT, and IP-IMRT for comparative radiobiological analysis. Radiobiological parameters, namely tumor control probability (TCP) and normal tissue complication probability (NTCP), were then calculated and compared for the three planning techniques.</p><p><strong>Results: </strong>The mean TCP values were comparable among all three techniques, with no statistically significant difference. However, significant differences were observed in most of the NTCP parameters (<i>P</i> < 0.05).</p><p><strong>Conclusion: </strong>The present study demonstrates that advanced radiotherapy planning techniques provide significant radiobiological advantages over conventional techniques in postmastectomy breast cancer patients. Among the three evaluated techniques, IP-IMRT showed the most favorable radiobiological profile, followed by FIF-FP-IMRT, while 3DCRT showed comparatively higher NTCP.</p>","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"51 3","pages":"646-652"},"PeriodicalIF":1.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13675030/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149217343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}