Journal of Applied Clinical Medical Physics最新文献

Background: Dynamic chest radiography (DCR) is a recently developed low-dose pulmonary functional imaging method that can be performed in a general X-ray room. DCR provides sequential images during respiration, and the measured changes in lung area are a promising diagnostic indicator of lung function.

Purpose: To investigate lung volume estimation using deep learning from DCR images during respiration and evaluate its accuracy in comparison with previously proposed estimation methods.

Methods: Two convolutional neural networks (CNNs), VGG19 and DenseNet121, were trained using DCR image datasets from 257 patients, with reference lung volumes derived from corresponding computed tomography (CT) images. The performance of the models was evaluated using mean absolute error (MAE) and mean absolute percentage error (MAPE), and compared against that of a conventional linear regression model. Correlation between the estimated and reference lung volumes was assessed using Pearson's correlation coefficient (r) and the degrees-of-freedom-adjusted coefficient of determination (Rf²). Forced vital capacity (FVC) was also estimated by subtracting the lung volume at maximum exhalation from that at maximum inhalation.

Results: The VGG19 and DenseNet121 models demonstrated superior performance in estimating whole lung volume (combined right and left lung) compared to the linear regression method. Specifically, MAE was 373/376 mL, MAPE was 8.1%/7.9%, r was 0.88/0.90, and Rf² was 0.76/0.80 for VGG19/DenseNet121, respectively. In contrast, the linear regression model yielded an MAE of 568 mL, MAPE of 12.4%, r of 0.84, and Rf² of 0.69. Although the Rf² values for DCR-derived FVC using VGG19 and DenseNet121 indicated moderate correlation, the MAE and MAPE were relatively high at 1.3/1.4 L and 41.1%/47.0%, respectively.

Conclusion: The proposed deep learning-based approach for lung volume estimation from DCR images outperformed the conventional linear regression method. Further improvements in CNN model architecture and the incorporation of guided forced respiratory maneuvers may enhance the potential for image-based pulmonary function testing.

Background: Radiomics holds the potential to improve the diagnostic evaluation of equivocal lymph nodes in head and neck cancer (HNC). While conventional radiomics models utilize features such as intensity, geometry, and texture of individual lymph node, they often neglect key spatial and anatomical characteristics tied to lymphatic dissemination patterns.

Purpose: In this study, we propose a novel spatially aware radiomics model that integrates anatomical knowledge and clinical factors to enhance lymph node malignancy prediction.

Methods: A total of 1389 lymph nodes (1119 benign and 270 malignant), contoured on CT scans from 192 HNC patients were included. Two models were developed: a baseline model using conventional radiomics features and an enhanced model incorporating five additional spatial and anatomical features, such as primary tumor type, lymph node level, the laterality of the primary tumor, the laterality of the lymph node, and the distance from the lymph node to the primary tumor. Sensitivity (SEN), specificity (SPE), accuracy (ACC), positive predictive value (PPV), negative predictive value (NPV) and the area under the receiver operating characteristic curve (AUC) criteria were used to evaluate the model performance.

Results: The proposed spatially aware radiomics model significantly outperformed the baseline model. The baseline model achieved SEN = 0.915, SPE = 0.756, ACC = 0.787, PPV = 0.475, NPV = 0.974, and AUC = 0.931. The enhanced model achieved SEN = 0.919, SPE = 0.845, ACC = 0.860, PPV = 0.589, NPV = 0.977, and AUC = 0.953. Statistical testing confirmed a significant improvement in both accuracy (p = 3.71 × 10^-20) and AUC (p = 1.13 × 10^-4).

Conclusions: This study demonstrates that incorporating lymphatic anatomy and clinical context into radiomics models significantly improves predictive performance. The proposed approach enhances interpretability, aligns with clinical workflows, and holds promises for personalized radiation therapy planning.

Objective: This study aimed to evaluate the impact of rectal gas evacuation on organ-at-risk (OAR) volumes, dose-volume histogram (DVH) parameters, and applicator displacement during cervical cancer brachytherapy.

Methods: Twenty-one cervical cancer patients who received three-dimensional brachytherapy at our center between November and December 2024 and presented with rectal gas were retrospectively included. Planning computed tomography (CT) images were acquired before and after rectal gas evacuation to evaluate changes in rectal and bladder volumes, as well as radiation dose variations to OARs (bladder, rectum, sigmoid, and small intestine). Dosimetric parameters analyzed comprised D_0.1cc, D_1cc, D_2cc, and D_5cc (minimum doses delivered to the most irradiated 0.1, 1, 2, and 5 cm³ of the OARs, respectively), as well as D_max (maximum dose) and D_mean (mean dose). Displacements of the applicator tip and cervical stopper were quantified using a coordinate system based on pelvic bony landmarks.

Results: Rectal volume decreased by 40.1% after gas evacuation, while bladder volume increased by 18.2%. D_0.1cc, D_1cc, D_2cc, D_5cc, and D_max in the rectum decreased significantly (P < 0.001) after gas evacuation, whereas no significant changes were observed in the DVH parameters of the other OARs. The mean displacements of the applicator tip and cervical stopper were 5.86 ± 3.64 mm and 4.23 ± 3.30 mm, respectively.

Conclusions: Rectal gas evacuation results in a statistically significant and clinically relevant reduction in rectal volume and rectal dose, underscoring its importance as a routine clinical procedure. However, as it may induce millimeter-level applicator displacement with clinically measurable dosimetric consequences, careful monitoring is warranted, with post-evacuation replanning or, if necessary, applicator adjustment.

Background: Craniospinal irradiation (CSI) is essential for treating pediatric medulloblastoma (MB) but causes significant long-term toxicities. Existing dose-reduction or partial-sparing strategies improve neurocognitive outcomes but may compromise survival or fail to address other late effects.

Methods: A new functional preservation CSI (FP-CSI) technique was developed to spare the hippocampus, hypothalamic-pituitary axis (HPA), cochlea, and scalp while ensuring homogeneous vertebral coverage. Eight pediatric patients with average-risk MB were retrospectively planned with volumetric modulated arc therapy (VMAT) using both FP-CSI and standard CSI (S-CSI). Dosimetric parameters for the planning target volume (PTV) and organs at risk (OARs), radiobiological effects, plan robustness, plan complexity, and plan quality assurance (QA) were compared.

Results: FP-CSI significantly reduced mean doses to the hippocampus (12.4 vs. 23.9 Gy), hypothalamus (14.7 vs. 23.9 Gy), and pituitary gland (15.4 vs. 24.1 Gy, all p < 0.01). Vertebral dose gradients were halved (4.7 vs. 8.7 Gy). Moderate dose reductions were also achieved for the cochlea and scalp. Compared with S-CSI, FP-CSI exhibited slightly inferior PTV homogeneity (HI: 0.16 vs. 0.07) and conformity (CI: 0.88 vs. 0.93), but coverage remained clinically acceptable. Normal tissue complication probability (NTCP) modeling showed pronounced decreases in predicted neurocognitive and endocrine toxicity risks, with probability of neurocognitive impairment reduced from 84.5% to 24.9% and probability of endocrine dysfunction from 44.7% to 27.3%. FP-CSI increased modulation complexity and produced slightly lower gamma passing rates for cranial beams, while spinal beam deliverability remained similar to S-CSI. Robustness analysis indicated greater sensitivity of FP-CSI to setup and rotational errors. Nevertheless, 3D dose reconstruction confirmed accurate delivery, with volumetric dose deviations generally below 1 Gy.

Conclusion: FP-CSI effectively spares critical functional structures while maintaining clinically acceptable target coverage, and offers a promising strategy to reduce long-term radiotherapy-induced toxicities in pediatric MB.

Purpose: This study evaluates the dosimetric impact of integrating thin metallic scatter foils with Cerrobend contact skin collimators to improve dose uniformity, conformality, and distal tissue sparing in small superficial electron fields.

Materials and methods: Electron beams of 8, 12, and 15 MeV from an Elekta Versa HD LINAC were delivered through a Cerrobend skin collimator with a 2.0 cm aperture at 100.0 cm SSD. Thin aluminum (Al) and lead (Pb) foils (< 2.50 mm) were placed on the collimator. Gafchromic EBT3 film in a solid-water phantom was used to measure depth-dose distributions and isodose profiles following TG-235-consistent calibration.

Results: Scatter foils produced thickness- and Z-dependent modulation of beam characteristics. At 8 MeV, the thickest Pb foil (1.07 mm) reduced the practical range (R_p) by ∼45% and shifted D_{m a x} proximally by ∼0.5 cm, yielding substantial distal tissue sparing. Al foils caused smaller R_p reductions (15%-25%) but improved lateral dose uniformity, producing smoother and more symmetric isodose contours. The 90% isodose diameter decreased with foil thickness for both materials, with Pb showing the largest contraction (∼20%-25%, energy-dependent), enhancing field conformality. Penumbra width increased slightly for thin foils but stabilized or narrowed for larger thicknesses. These effects diminished at 15 MeV, indicating reduced sensitivity of high-energy electrons to thin-foil perturbation.

Conclusions: Thin metallic foils placed on Cerrobend skin collimators enable a controllable balance between dose uniformity (improved with Al) and conformality with distal sparing (enhanced with Pb). This simple, LINAC-independent configuration offers a cost-effective method for modulating small-field electron beam characteristics and may serve as a practical adjunct for treating superficial lesions.

A resident satisfaction and well-being survey was developed and administered within a Multi-Institutional Journal Club (MIJC) including therapy medical physics residency programs within the Karmanos Cancer Institute, the University of Maryland, the University of Utah, and the University of Alabama-Birmingham. The survey was designed as a tool for quality improvement and program evaluation within each individual program. Survey items were derived in part from existing well-established question inventories and included 26 questions, 4 of which were derived from the Maslach Burnout Inventory (MBI) and 12 from the American Psychological Association Work and Well-being Survey. The survey was administered anonymously via email link annually from 2022 to 2025, and 41 residents responded to the survey during this period. Mean Likert scores for positively keyed survey items (higher score is better) ranged from 4.00/5 to 4.78/5. Mean Likert scores for negatively keyed survey items (lower score is better) ranged from 1.37/5 to 2.71/5. Items were subsequently grouped into five themes: "Burnout," "Work-Life Balance," "Interpersonal Relationships," "Institutional Values," and "Job Satisfaction." Mean scores for these themes were universally positive and ranged from 4.55/5 for "Job Satisfaction" to 3.63/5 for "Work-Life Balance." For the "Interpersonal Relationships," "Institutional Values," and "Job Satisfaction" themes, 11 of 12 survey items had a median Likert score of 5/5. No respondent indicated a Likert score under '3' for any of the items in the "Job Satisfaction" theme, making it the most consistently positive theme of the survey. Free-text comments were categorized as "Positive," "Neutral," or "Negative." Of 70 total free-text comments, 25 (36%) were categorized as "Positive," 39 (56%) as "Neutral" and 6 (9%) as "Negative." Approximately 20% of respondents felt a strong sense of burnout or emotional exhaustion. However, nearly 90% felt that their program and program faculty made them feel valued and that they would recommend their residency program to trainees looking for a position. These results compare favorably with previously published data for radiation oncology residents and represent a strong positive sentiment about the characteristics of these residency programs and the residency process itself. While stress and difficulties maintaining work/life balance were clearly acknowledged, quantitative and free-text comments indicate that the positive aspects of residency training substantially outweigh these negative aspects. The survey has provided a substantial amount of information supporting the success and best practices involved in our programs as well as some constructive negative feedback, which can allow us to further improve our respective programs and potentially serve as a model to help improve medical physics residency training throughout our profession.

Purpose: This study systematically quantifies the effects of five variables-respiratory cycle, tumor size, and motion amplitudes in the superior-inferior (SI), anterior-posterior (AP), and left-right (LR) directions-on the registration accuracy between four-dimensional computed tomography (4D CT) and four-dimensional cone-beam CT (4D CBCT) images, thereby providing a theoretical basis for optimizing registration strategies in image-guided radiotherapy (IGRT).

Materials and methods: A CIRS 008A dynamic phantom fitted with 1 and 3 cm tumor inserts was utilized to simulate various respiratory motion scenarios by manipulating respiratory cycles (T = 0, 2, 4, and 8 s) and three-dimensional motion amplitudes (SI, AP, and LR ranging from 0 to 15 mm, with AP and LR limited to 0, 1, and 5 mm). Corresponding four-dimensional images were acquired using a GE Discovery RT CT simulator and a Varian VitalBeam linear accelerator. Rigid registration between the 4D CT and 4D CBCT images was subsequently performed using the Varian imaging system, with registration quality quantitatively assessed via the Dice similarity coefficient (DSC). Furthermore, a Random Forest regression model was employed to determine the relative importance of each factor, and multifactor analysis of variance (ANOVA) was conducted to verify statistical significance.

Results: The Random Forest analysis indicated that, for the overall registration average intensity projection, the factors were ranked in order of importance as follows: tumor size (0.509), SI motion (0.315), respiratory cycle (0.094), LR motion (0.055), and AP motion (0.028). In the maximum intensity projection, tumor size (0.722) was found to have a particularly significant impact. The multifactor ANOVA further supported these findings, demonstrating that tumor size (p < 0.001) and SI motion (p < 0.001) have a highly significant influence on registration quality, whereas the respiratory cycle and AP/LR motions did not reach statistical significance (p > 0.05). Notably, when the tumor size was small (1 cm) and accompanied by considerable SI motion (>10 mm), registration accuracy markedly deteriorated, with the greatest variability observed under these conditions.

Conclusion: This study demonstrated that the registration quality between 4D CT and 4D CBCT images was significantly influenced by both tumor size and the amplitude of motion in the SI direction.

Purpose: We evaluated the feasibility of a magnetic resonance (MR)-only simulation, planning, and treatment (MROSPT) workflow for prostate cancer patients using synthetic computed tomography (sCT) generated from magnetic resonance imaging (MRI) data. By validating sCT-based dose calculations, we aimed to streamline radiotherapy workflows, eliminate the need for CT simulation, and enable reliable clinical implementation of MR-based radiotherapy for MR-linac (MRL).

Methods: We developed a comprehensive workflow encompassing the entire process from initial consultation to treatment delivery. After developing the workflow, a retrospective dosimetric validation study was performed on nine men with prostate cancer. They underwent CT and MRI simulations, and sCTs were generated from the MRI data. Contours and intensity-modulated radiation therapy treatment plans were created on the reference simulation CT (rCT) and transferred to sCTs for dose-calculation comparisons. Dosimetric accuracy was evaluated using gamma analysis (dose/distance; 2%/2mm). Bulk density sCTs (bCTs) were created by overriding organ density values with their mean (bulk) sCT-determined densities. bCT based on sCT allows treatment planning directly on MRI for MRL workflow efficiency.

Results: Minimal non-bone Hounsfield units (HU)-value differences between rCT and sCT (5.5 ± 2.9 HU for prostate) demonstrated the reliability of the sCT generation process. Dosimetric comparisons between treatment plans (rCT vs. sCT, rCT vs. bCT) showed agreement within ± 2% in gamma analysis, confirming robust accuracy. The gamma index pass rate for rCT versus sCT and rCT versus bCT were consistently > 95% using 2%/2 mm criteria. A dry run of the entire simulation-to-treatment workflow was successfully completed.

Conclusion: The MROSPT workflow using sCT is clinically feasible and dosimetrically accurate for prostate cancer patients. Dose calculations based on sCT demonstrated high dosimetric agreement with simulation CT, with no statistically significant differences across all evaluated metrics. These findings support the adoption of sCT‑based planning for prostate cancer radiotherapy and suggest its potential applicability in other anatomical regions especially in the pelvis. Integration of robust quality‑assurance processes and treatment‑delivery flexibility will further enhance its clinical utility.