Left Femoral Head Research Articles

Improvement of treatment plan quality with modified fixed field volumetric modulated arc therapy in cervical cancer.

This study aims to introduce modified fixed field volumetric modulated arc therapy (MF-VMAT) which manually opened the field size by fixing the jaws and comparing it to the typical planning technique, auto field volumetric modulated arc therapy (AF-VMAT) in cervical cancer treatment planning. Previously treated twenty-eight cervical cancer plans were retrospectively randomly selected and replanned in this study using two different planning techniques: AF-VMAT and MF-VMAT, resulting in a total of fifty-six treatment plans. In this study, we compared both planning techniques in three parts: (1) Organ at Risk (OARs) and whole-body dose, (2) Treatment plan efficiency, and (3) Treatment plan accuracy. For OARs dose, bowel bag (p-value=0.001), rectum (p-value=0.002), and left femoral head (p-value=0.001) and whole-body (p-value=0.000) received a statistically significant dose reduction when using the MF-VMAT plan. Regarding plan efficiency, MF-VMAT exhibited a statistically significant increase in both number of monitor units (MUs) and control points (p-values=0.000), while beam-on time, maximum leaf travel, average maximum leaf travel, and maximum leaf travel per gantry rotation were statistically significant decreased (p-values=0.000). In terms of plan accuracy, the average gamma passing rate was higher in the MF-VMAT plan for both absolute dose (AD) (p-value=0.001, 0.004) and relative dose (RD) (p-value=0.000, 0.000) for 3%/3 and 3%/2mm gamma criteria, respectively. The MF-VMAT planning technique significantly reduces OAR doses and decreases the spread of low doses to normal tissues in cervical cancer patients. Additionally, this planning approach demonstrates efficient plans with lower beam-on time and reduced maximum leaf travel. Furthermore, it indicates higher plan accuracy through an increase in the average gamma passing rate compared to the AF-VMAT plan. Consequently, MF-VMAT offers an effective treatment planning technique for cervical cancer patients.

Femoral head and acetabular necrosis combined with hip subluxation in people with HIV: a case report and literature review

IntroductionHIV is widely prevalent in all regions of the world. The use of antiretroviral drugs has dramatically reduced the mortality rate of HIV-related diseases, but correspondingly increased the incidence of chronic complications in HIV-positive people. Related studies have found that the incidence of osteonecrosis of the femoral head is higher in HIV-positive people, but the co-occurrence of femoral head necrosis, acetabular necrosis and hip joint dislocation in HIV-positive patients is rare.MethodsWe report a 50-year-old man with a 15-month history of progressively worsening right hip pain with movement restriction. According to the CT findings of the other hospital, the patient was admitted to the hospital with femoral head necrosis. After the admission, the relevant X-ray, CT and MRI examinations showed that the right femoral head collapsed and deformed, with the surrounding bone sclerosis, bone fragments, loose body of the joint, right hip subluxation, acetabular marginal osteogeny, and local microcystic degeneration. The left femoral head was in good shape, and cystic degeneration can be seen under the articular surface. The patient was finally diagnosed with femoral head necrosis and acetabular necrosis combined with hip subluxation.ResultsThe pain of the patient was significantly relieved after the operation, and the patient was discharged from the hospital one week after the start of treatment to continue rehabilitation training. During the follow-up one month after the operation, the self-reported pain disappeared completely, and the limitation of activity was significantly improved.

Clinical evaluation of the convolutional neural network‑based automatic delineation tool in determining the clinical target volume and organs at risk in rectal cancer radiotherapy.

Delineating the clinical target volume (CTV) and organs at risk (OARs) is crucial in rectal cancer radiotherapy. However, the accuracy of manual delineation (MD) is variable and the process is time consuming. Automatic delineation (AD) may be a solution to produce quicker and more accurate contours. In the present study, a convolutional neural network (CNN)-based AD tool was clinically evaluated to analyze its accuracy and efficiency in rectal cancer. CT images were collected from 148 supine patients in whom tumor stage and type of surgery were not differentiated. The rectal cancer contours consisted of CTV and OARs, where the OARs included the bladder, left and right femoral head, left and right kidney, spinal cord and bowel bag. The MD contours reviewed and modified together by a senior radiation oncologist committee were set as the reference values. The Dice similarity coefficient (DSC), Jaccard coefficient (JAC) and Hausdorff distance (HD) were used to evaluate the AD accuracy. The correlation between CT slice number and AD accuracy was analyzed, and the AD accuracy for different contour numbers was compared. The time recorded in the present study included the MD time, AD time for different CT slice and contour numbers and the editing time for AD contours. The Pearson correlation coefficient, paired-sample t-test and unpaired-sample t-test were used for statistical analyses. The results of the present study indicated that the DSC, JAC and HD for CTV using AD were 0.80±0.06, 0.67±0.08 and 6.96±2.45 mm, respectively. Among the OARs, the highest DSC and JAC using AD were found for the right and left kidney, with 0.91±0.06 and 0.93±0.04, and 0.84±0.09 and 0.88±0.07, respectively, and HD was lowest for the spinal cord with 2.26±0.82 mm. The lowest accuracy was found for the bowel bag. The more CT slice numbers, the higher the accuracy of the spinal cord analysis. However, the contour number had no effect on AD accuracy. To obtain qualified contours, the AD time plus editing time was 662.97±195.57 sec, while the MD time was 3294.29±824.70 sec. In conclusion, the results of the present study indicate that AD can significantly improve efficiency and a higher number of CT slices and contours can reduce AD efficiency. The AD tool provides acceptable CTV and OARs for rectal cancer and improves efficiency for delineation.

Automated contouring of CTV and OARs in planning CT scans using novel hybrid convolution-transformer networks for prostate cancer radiotherapy

Purpose objective(s)Manual contouring of the prostate region in planning computed tomography (CT) images is a challenging task due to factors such as low contrast in soft tissues, inter- and intra-observer variability, and variations in organ size and shape. Consequently, the use of automated contouring methods can offer significant advantages. In this study, we aimed to investigate automated male pelvic multi-organ contouring in multi-center planning CT images using a hybrid convolutional neural network-vision transformer (CNN-ViT) that combines convolutional and ViT techniques.Materials/methodsWe used retrospective data from 104 localized prostate cancer patients, with delineations of the clinical target volume (CTV) and critical organs at risk (OAR) for external beam radiotherapy. We introduced a novel attention-based fusion module that merges detailed features extracted through convolution with the global features obtained through the ViT.ResultsThe average dice similarity coefficients (DSCs) achieved by VGG16-UNet-ViT for the prostate, bladder, rectum, right femoral head (RFH), and left femoral head (LFH) were 91.75%, 95.32%, 87.00%, 96.30%, and 96.34%, respectively. Experiments conducted on multi-center planning CT images indicate that combining the ViT structure with the CNN network resulted in superior performance for all organs compared to pure CNN and transformer architectures. Furthermore, the proposed method achieves more precise contours compared to state-of-the-art techniques.ConclusionResults demonstrate that integrating ViT into CNN architectures significantly improves segmentation performance. These results show promise as a reliable and efficient tool to facilitate prostate radiotherapy treatment planning.

Feasibility Study on Constructing Dosimetric Correlated Geometric Parameters for Automatic Segmentation Evaluation.

To investigate the feasibility of constructing new geometric parameters that correlate well with dosimetric parameters. 100 rectal cancer patients were enrolled. The targets were identified manually, while the organs at risk (bladder, small bowel, left and right femoral heads) were segmented both manually and automatically. The radiotherapy plans were optimized according to the automatically contoured organs at risk. Forty cases were randomly selected to establish the relationship between dose and distance for each organ at risk, termed "dose-distance curves," which were then applied to the new geometric parameters. The correlation between these new geometric parameters and dosimetric parameters was analyzed in the remaining 60 test cases. The "dose-distance curves" were similar across the four organs at risk, exhibiting an inverse function shape with a rapid decrease initially and a slower rate at a later stage. The Pearson correlation coefficients of new geometric parameters and dosimetric parameters in the bladder, small intestine, and left and right femur heads were 0.96, 0.97, 0.88, and 0.70, respectively. The new geometric parameters predicated on "distance from the target" showed a high correlation with corresponding dosimetric parameters in rectal cancer cases. It is feasible to utilize the new geometric parameters to evaluate the dose deviation attributable to automatic segmentation.

OTHR-01. ALK-DRIVEN INFANT-TYPE HEMISPHERIC HIGH-GRADE GLIOMA AND CNS HISTIOCYTOSIS SUCCESSFULLY TREATED WITH A SECOND GENERATION ALK-INHIBITOR: MOLECULAR CHARACTERIZATION SAVES THE DAY

Abstract BACKGROUND Molecular characterization of brain tumors (BT) is increasingly becoming essential for diagnosing and managing central nervous system (CNS) tumors. Infant BT is a particular category of pediatric CNS tumors that poses a challenge to the neuro-oncologist. METHODS Here, we present two distinctive cases where molecular characterization significantly influenced diagnosis, management, and outcome. RESULTS The first case is a 2-year-old boy presented with generalized seizures. MRI of the brain showed multiple dural-based tumors. Biopsy was consistent with ALK-positive histiocytosis (KIF5B-ALK fusion). Additional lesions were detected in the right kidney and left femoral head. Despite three months of LCH-based therapy, the tumor progressed. Chemotherapy was halted, and treatment with alectinib was initiated. Follow-up MRIs after 3 and 6 months showed marked regression and a complete resolution of the lesions, respectively. The second case is a 12-month-old boy who exhibited developmental regression and increased head circumference over the past three months. MRI revealed a large complex right ventricle mass measuring 8.6x7.2x8 cm. Subtotal resection was complicated by severe intracranial bleeding, requiring prolonged intensive care stay. The initial pathological diagnosis was anaplastic ependymoma. He received two cycles of neoadjuvant chemotherapy followed by a second look surgery, which failed to achieve a gross total resection. Molecular testing by this time identified an ALK-CCDC85A fusion, altering the diagnosis to infant-type hemispheric glioma. He was started on alectinib. MRI after 3 and 6 months showed a favorable response with an overall improvement in his cognitive function. Both patients tolerated alectinib well, remaining on therapy for 6 and 12 months, respectively. No toxicities were reported. CONCLUSION These cases illustrate the transformative role of molecular characterization in managing infants with CNS tumors, highlighting its potential as a game-changer in pediatric neuro-oncology.

Dosimetric Features of Ultra-Hypofractionated Intensity Modulated Proton Therapy for Prostate Cancer

PurposeTo report clinical and dosimetric characteristics of 5-fraction stereotactic ablative radiotherapy (SABR) using intensity modulated proton therapy (IMPT) for localized prostate cancer. Materials and MethodsAll patients receiving IMPT SABR from 2017 to 2021 for localized prostate cancer at our institution were included. Five fractions were delivered every other day to the prostate +/− seminal vesicles [clinical target volume (CTV)] with 3 mm/3% robustness. A 4-field arrangement with 2 anterior oblique and 2 opposed lateral beams was used in most patients (97%), and most (99%) had a retroprostatic hydrogel spacer. ResultsA total of 534 patients with low (14%), favorable intermediate (45%), unfavorable intermediate (36%), high (4.0%), or very high-risk (0.6%) disease are evaluated. Prescription dose was 36.25 Gy (31%), 38 Gy (38%), or 40 Gy (31%) was prescribed. Median volume percentage of CTV receiving at least 100% of prescription dose [V100% (%)] was 100% [interquartile range: 99.99–100]. Rectum V50% (%), V80% (%), and V90% (%) were significantly lower in patients who had spacer, with a mean difference of −9.70%, −6.59%, and −4.42%, respectively, compared to those who did not have spacer. Femoral head dose was lower with a 4-field arrangement. Mean differences in left and right femoral head V40% (%) were −6.99% and −10.74%, respectively. ConclusionWe provide a large, novel report of patients treated with IMPT SABR for localized prostate cancer. Four-field IMPT with hydrogel spacer provides significant sparing of rectum and femoral heads without compromising target coverage.

Photon Dose Distribution Analysis Using Three-Dimensional Conformal Radiation Therapy (3DCRT) And Intensity Modulated Radiotherapy (IMRT) Techniques in Cervical Cancer Cases Based on Histogram Volume Dose Curve (DVH)

Radiotherapy is one of the effective treatment methods for cervical cancer, although it is faced with great challenges in delivering an accurate dose of radiation. The purpose of this study was to compare the effectiveness of two cervical cancer treatment techniques, namely Three-Dimensional Conformal Radiation Therapy (3DCRT) and Intensity Modulated Radiotherapy (IMRT) in terms of conformity, homogeneity, and protection of Organs at Risk (OAR). The study used primary data from DVH 3DCRT and IMRT techniques collected from 15 cervical cancer patients. Conformity index (CI) and homogeneity (HI) were evaluated, and dose at OAR was assessed according to QUANTEC rules. The results showed the average CI value for the 3DCRT technique was (96,7 ± 1,5) x 10-2, while for IMRT it was (98,8 ± 0,8) x 10-2. In addition, the average HI score for the 3DCRT technique was (9,6 ± 1,5) x 10-2, and for IMRT it was (6,8 ± 1,9) x 10-2. In the 3DCRT technique, V50 for the rectum ranges from 0 – 57%, V65 for the bladder is 0%, and V50 for the left and right femoral heads ranges from 0 – 15.92% and 0 – 10.86%. While in the IMRT technique, V50 for the rectum ranges from 0 – 40,56%, V65 for bladder is 0%, and V50 for the left and right femoral heads ranges from 0 – 0,3% and 0 – 1,05%. The results of this study showed that the IMRT technique was better and more effective in treating cervical cancer than the 3DCRT technique. The IMRT technique has a higher CI value, indicating a better level of conformity, as well as a lower HI value, signifying a higher level of homogeneity. In addition, the IMRT technique is also able to provide better OAR protection.

Synthetic MRI-Assisted and Self-Supervised Adaptive Segmentation of Organs-at-Risk (OARs) in MRI-Based Radiation Therapy

This study proposes a self-supervised solution for OAR segmentation, combining patch-based adaptation and unsupervised synthesis of T2-weighted MRI data to finetune the segmentation model. The aim is to improve adaptation to patient anatomy, overcome limited annotated MRI data, and enhance the generalizability of automatic segmentation models for gynecological cancers. The study used a patch-based cycle consistent generative adversarial network (cycle-GAN) for unsupervised MRI synthesis from CT scans of 20 patients, and a residual U-Net model for OARs segmentation. The segmentation model was trained and validated on synthetic MRI (sMRI) of 103 and 25 patient scans respectively, then finetuned on 78 MRI scans from radiation therapy fractions of 15 additional patients through three-fold cross validation. Self-supervised adaptation was applied, incorporating affine and elastic deformations, intensity shifting, and scaling. The model was trained on 96 × 96 × 96 sub-volumes and validated on entire pelvic sections of the same images. A combination of Dice and weighted cross entropy (CE) losses, with weights assigned for bladder (1), small bowel (1), rectum (2), sigmoid (2), left femoral head (0), and right femoral head (0), was used for OAR segmentation. The performance was evaluated against the model trained only on a limited number of acquired MRI data, as well as sMRI pretrained models with encoder weight freezing and either equal weighting or soft-tissue adjusted weighting. Our sMRI-assisted approach showed improved performance for challenging pelvic OARs compared to the method using only the acquired MRI data. The self-supervised fraction-adaptive segmentation results indicated better performance in target soft-tissues when using at least one treatment fraction for organ-specific adaptation. Our framework leverages pre-existing CT planning data for gynecological cancers to enhance the segmentation performance of OARs during MR-guided adaptive treatments. This approach offers substantial benefits for the radiation therapy workflow, including reduced variability in per-fraction segmentation and clinical burden. Further studies that involve human expert evaluations will be conducted to assess the practicality of this approach in radiation therapy.

Impact of AI-Based Auto-Segmentation on Radiotherapy Processes in Low and Middle-Income Countries

Radiotherapy processes require significant human resources and expertise, creating a barrier for rapid deployment in low and middle-income countries (LMICs). Optimal radiotherapy (RT) relies on accurate segmentation of tumor targets and organs-at-risk (OARs) during the RT planning process. This study reports the impact of AI-based auto-segmentation on RT processes in an LMIC. Ten patients including five head and neck (HN), and five prostate cancer patients were randomly selected. Their planning CT images were subjected to auto- segmentation using an FDA-approved AI software tool, and manual segmentation by an experienced radiation oncologist from a Sub-Saharan African RT clinic. The control data consisted of contours from an experienced radiation oncologist and dosimetrists at a large academic institution in the US. For prostate cases, the contours included the prostate, seminal vesicles, bladder, rectum, penile bulb, and both femoral heads. For HN cases, the contours included the brain, brainstem, bilateral eyes, lens, optic nerves, cochlea, parotids, optic chiasm, spinal cord, oral cavity, and mandible. The time to complete the segmentation was recorded for both auto-segmentation and manual contours from the LMIC. The DICE similarity coefficients were used for comparative evaluation. The average time for contouring per patient was 2 minutes for AI compared to 57 minutes for manual contouring in the LMIC. When comparing the control data, AI pelvic contours provide a slightly better agreement than LMIC manual contours for all the OARs, with the following mean DICE coefficients for AI vs LMIC manual contours: bladder (0.971 vs 0.958), left femoral head (0.960 vs 0.949), right femoral head (0.959 vs 0.941), rectum (0.880 vs 0.867), prostate (0.836 vs 0.824), seminal vesicles (0.696 vs 0.580), and penile bulb (0.536 vs 0.528). For HN contours, AI provide a better agreement for 7 of 11 OARs than the LMIC manual contours, with the following mean DICE coefficients: brain (0.972 vs 0.982), mandible (0.877 vs 0.925), right parotid (0.847 vs 0.800), left parotid (0.798 vs 0.792), spinal cord (0.837 vs 0.821), left eye (0.875 vs 0.832), right eye (0.867 vs 0.836), brainstem (0.866 vs 852), oral cavity (0.796 vs 0.787), left lens (0.650 vs 0.729) and right lens (0.671 vs 0.682). Neither AI contours nor LMIC manual contours had good agreement with the control data (<0.600) for optic nerves, chiasm, and cochlea due to their small volumes. AI-based auto-segmentation tools are capable of producing contours of comparable quality to those generated by manual segmentation for both pelvic and HN cancer patients in LMICs, while also resulting in substantial time savings. AI-based auto-segmentation holds tremendous potential for improving radiotherapy care in LMICs with limited sources.

Reproducibility and Repeatability of Pelvic Radiomics Features with Daily Imaging on a Novel Biology-Guided Radiotherapy Machine Compared to Daily Imaging on Other Radiotherapy Delivery Systems

Radiomics features extracted from computed tomography (CT) images have the potential of being used as imaging biomarkers in diagnostic and prognostic models for prostate cancer. This study aims to evaluate reproducibility and repeatability of radiomics features using daily CT images from a novel biology-guided radiotherapy (BgRT)-capable machine compared to other daily CT imaging modalities from other radiotherapy delivery systems. We retrospectively selected thirty male patients treated to the pelvic region at our institution. Among the thirty patients, ten patients were treated on a BgRT-capable machine with daily fan-beam kilovoltage CT (kVCT) scans, ten patients were treated on a conventional Linac with daily cone-beam CT (CBCT) scans, and ten patients were treated on helical tomotherapy with daily megavoltage CT (MVCT) scans. The prostate gland and femoral heads were delineated on planning CT images and on CT images at the first treatment fraction. After all the CT images were resampled to have an identical voxel size, 107 radiomics features were calculated for the prostate and femoral heads on both the planning CT images and daily CT images. Concordance correlation coefficient (CCC) and Pearson's correlation coefficient (r) were calculated to evaluate radiomics feature reproducibility between the planning CT images and pre-treatment CT scans at the first treatment fraction. To evaluate radiomics feature repeatability with kVCT images on the BgRT-capable machine, a total of ten treatment sessions with repeat kVCT scans on the BgRT-capable machine were retrieved and radiomics features were compared between the first and the second kVCT scans. For the prostate gland volume, high concordance (CCC > 0.8) was found for 8, 6, and 0 radiomics features with the first-fraction kVCT, CBCT, and MVCT images, respectively, in relation to the planning CT images, while there was strong correlation (r > 0.8) in 38, 22, and 5 radiomics features in the kVCT, CBCT, and MVCT images, respectively, in relation to the planning CT images. For the left and right femoral heads, high concordance (CCC > 0.8) was found in no more than two radiomics features with the kVCT, CBCT, or MVCT images in relation to the planning CT images. Based on repeat kVCT scan data, 97 (90.7%) radiomics features showed high concordance (CCC > 0.8) in repeat kVCT scans with 98 (91.6%) features showing high correlation (Pearson's correlation coefficient > 0.8) for the prostate gland volume. A small number of radiomics features were found to show strong reproducibility between daily CT images on the first fraction and the planning CT images with the three IGRT CT modalities in this study. On the other hand, our results indicated that strong reproducibility was found with more radiomics features with daily kVCT images from the BgRT-capable machine compared to daily CBCT and MVCT images for the prostate gland. Strong repeatability was also found with most radiomics features in daily kVCT images.

Performance Evaluation in Automatic Plan Generation for Ethos Intelligent Optimization Engine

To evaluate the automatic optimization performance and clinical feasibility of the Intelligent Optimization Engine (IOE) of Ethos online adaptive radiotherapy platform. Eleven patients with cervical cancer treated with Halcyon accelerator were retrospectively selected. All the patients manually planned with four full arc volume rotating intensity modulated radiotherapy (VMAT) (Manual-4Arc), and the prescription dose was 45 Gy/25F. All patient images and structures were imported into Ethos simulator, and clinical goals were added appropriately based on clinical requirements. The target coverage was normalized to 95%. 7F, 9F, 12F IMRT plans and 2Arc, 3Arc VMAT plans were automatically generated by IOE. Dosimetric index comparisons were made among the Manual-4Arc plans and five group IOE generated plan to evaluate the automatic optimization performance of IOE. In terms of hot dose area, for PTV, D1% of IMRT-12F plans was the lowest, and there were significant differences between IMRT-12F plans and Manual-4Arc plans (46.936 ± 0.241 vs 48.639 ± 2.395, p = 0.004); In terms of target coverage, the CTVs of all groups meet clinical requirements. Although the Ethos online adaptive plans have been normalized during planning, the PTV coverage is slightly insufficient (12F: 94.913 ± 0.154; 9F: 94.585 ± 1.148). For OARs close to target, such as bladder, V30Gy, V40Gy and Dmean have significant differences among the six group plans. The order of bladder dose is basically followed by IMRT-12F<IMRT-9F<Manual-4Arc<IMRT-7F<VMAT-3Arc<VMAT-2Arc. There was significant statistical difference between V30Gy and Dmean of rectum, and the order of them was basically the same as that of bladder, except for the exchange between IMRT-7F and Manual-4Arc plan. Rectal V40Gy, small intestine Dmax and Dmean had no significant difference among the six group plans. For OARs far away from the target, such as the left and right femoral head, spinal cord and bone marrow, the order is basically followed by IMRT-12F<IMRT-9F<IMRT-7F<VMAT-2Arc<VMAT-3Arc<Manual-4Arc. The plans automatically generated by Ethos IOE can achieve similar performance as the manual plan, and the automatically generated IMRT-12F and 9F plans are preferred for clinical use.

Automatic dose prediction using deep learning and plan optimization with finite-element control for intensity modulated radiation therapy.

Automatic solutions for generating radiotherapy treatment plans using deep learning (DL) have been investigated by mimicking the voxel's dose. However, plan optimization using voxel-dose features has not been extensively studied. This study aims to investigate the efficiency of a direct optimization strategy with finite elements (FEs) after DL dose prediction for automatic intensity-modulated radiation therapy (IMRT) treatment planning. A double-UNet DL model was adapted for 220 cervical cancer patients (200 for training and 20 for testing), who underwent IMRT between 2016 and 2020 at our clinic. The model inputs were computed tomography (CT) slices, organs at risk (OARs), and planning target volumes (PTVs), and the outputs were dose distributions of uniformly generated high-dose region-controlled plans. The FEs were discretized into equal intervals of the dose prediction value within the [OARs avoid PTV(O-P)] and [body avoids OARs & PTV(B-OP)] regions in the test cohort and used to define the objectives for IMRT plan optimization. The plans were optimized using a two-step process. In the beginning, the plans of two extra cases with and without low-dose region control were compared to pursue robust and optimal dose adjustment degree pattern of FEs. In the first step, the mean dose of O-P FEs were constrained to differing degrees according to the pattern. The further the FEs from the PTV, the tighter the constraints. In the second step, the mean dose of O-P FEs from first step were constrained again but weakly and the dose of the B-OP FEs from dose prediction and PTV were tightly regulated. The dosimetric parameters of the OARs and PTV were evaluated and compared using an interstep approach. In another 10 cases, the plans optimized via the aforementioned steps (method 1) were compared with those directly generated by the double-UNet dose prediction model trained by low and high region-controlled plans (method 2). The mean differences in dose metrics between the UNet-predicted dose and the clinical plans were: 0.47Gy for bladder D50% ; 0.62Gy for rectum D50% ; 0% for small intestine V30Gy ; 1% for small intestine V40Gy ; 4% for left femoral head V30Gy ; and 6% for right femoral head V30Gy . The reductions in mean dose (p<0.001) after FE-based optimization were: 4.0, 1.9, 2.8, 5.9, and 5.7Gy for the bladder, rectum, small intestine, left femoral head, and right femoral head, respectively, with flat PTV homogeneity and conformity. Method 1 plans produced lower mean doses than those of method 2 for the bladder (0.7Gy), rectum (1.0Gy), and small intestine (0.6Gy), while maintaining PTV homogeneity and conformity. FE-based direct optimization produced lower OAR doses and adequate PTV doses after DL prediction. This solution offers rapid and automatic plan optimization without manual adjustment, particularly in low-dose regions.

Avascular Necrosis

Osteonecrosis can be defined as bone cell death resulting from impaired blood flow to the bone due to traumatic or non-traumatic events. Case Presentation: We present a case in which a 62-year-old female came to the Orthopedic Surgery Polyclinic with complaints of pain in her groin. The pain is intermittent and worsens when the patient moves his legs. This makes it difficult for patients to sit and have pain when walking, so they need a cane as a tool. This complaint has been felt for ± 1.5 years and has been getting worse since this 1 month. During the pain the patient said he was taking pain relievers but his complaints did not decrease. On local examination in the left Hip Joint region, it was found that on look examination there was no visible deformity, swelling and skin color that was the same as the surrounding area. On examination of the feel found positive tenderness. On AP pelvic X-ray examination, there was a fracture of the left femoral head with superior displacement. Treatment for this patient includes non-surgical and surgical (total hip replacement). Conclusion: The patient was diagnosed with Avascular necrosis.

Human factors in the clinical implementation of deep learning-based automated contouring of pelvic organs at risk for MRI-guided radiotherapy.

Deep neural nets have revolutionized the science of auto-segmentation and present great promise for treatment planning automation. However, little data exists regarding clinical implementation and human factors. We evaluated the performance and clinical implementation of a novel deep learning-based auto-contouring workflow for 0.35T magnetic resonance imaging (MRI)-guided pelvic radiotherapy, focusing on automation bias and objective measures of workflow savings. An auto-contouring model was developed using a UNet-derived architecture for the femoral heads, bladder, and rectum in 0.35T MR images. Training data was taken from 75 patients treated with MRI-guided radiotherapy at our institution. The model was tested against 20 retrospective cases outside the training set, and subsequently was clinically implemented. Usability was evaluated on the first 30 clinical cases by computing Dice coefficient (DSC), Hausdorff distance (HD), and the fraction of slices that were used un-modified by planners. Final contours were retrospectively reviewed by an experienced planner and clinical significance of deviations was graded as negligible, low, moderate, and high probability of leading to actionable dosimetric variations. In order to assess whether the use of auto-contouring led to final contours more or less in agreement with an objective standard, 10 pre-treatment and 10 post-treatment blinded cases were re-contoured from scratch by three expert planners to get expert consensus contours (EC). EC was compared to clinically used (CU) contours using DSC. Student's t-test and Levene's statistic were used to test statistical significance of differences in mean and standard deviation, respectively. Finally, the dosimetric significance of the contour differences were assessed by comparing the difference in bladder and rectum maximum point doses between EC and CU before and after the introduction of automation. Median (interquartile range) DSC for the retrospective test data were 0.92(0.02), 0.92(0.06), 0.93(0.06), 0.87(0.04) for the post-processed contours for the right and left femoral heads, bladder, and rectum, respectively. Post-implementation median DSC were 1.0(0.0), 1.0(0.0), 0.98(0.04), and 0.98(0.06), respectively. For each organ, 96.2, 95.4, 59.5, and 68.21 percent of slices were used unmodified by the planner. DSC between EC and pre-implementation CU contours were 0.91(0.05*), 0.91*(0.05*), 0.95(0.04), and 0.88(0.04) for right and left femoral heads, bladder, and rectum, respectively. The corresponding DSC for post-implementation CU contours were 0.93(0.02*), 0.93*(0.01*), 0.96(0.01), and 0.85(0.02) (asterisks indicate statistically significant difference). In a retrospective review of contours used for planning, a total of four deviating slices in two patients were graded as low potential clinical significance. No deviations were graded as moderate or high. Mean differences between EC and CU rectum max-doses were 0.1±2.6Gy and -0.9±2.5Gy for pre- and post-implementation, respectively. Mean differences between EC and CU bladder/bladder wall max-doses were -0.9±4.1Gy and 0.0±0.6Gy for pre- and post-implementation, respectively. These differences were not statistically significant according to Student's t-test. We have presented an analysis of the clinical implementation of a novel auto-contouring workflow. Substantial workflow savings were obtained. The introduction of auto-contouring into the clinical workflow changed the contouring behavior of planners. Automation bias was observed, but it had little deleterious effect on treatment planning.

Prostate Cancer Volumetric Arc Therapy (VMAT) Analysis

Aim: The purpose of this study was to derive radiobiological parameters from the dose volume histogram of patients that undergone prostate cancer radiotherapy via the VMAT technique. Material and Methods: A total of twenty-two cases of prostate cancer of 70.2 and 81 Gy were selected for this study. Radiobiological parameters were evaluated by the Poisson equation for the tumor and LKB model for organs at risk. Results: For prostate tumor, the average TCP was 96.28, 86.45, 68.57 for α/β of 1.2, 3.0, and 10, respectively. The average EUD was 37.98 Gy, 59.23 Gy, 21.19 Gy, 20.65 Gy, 16.32 Gy, and 21.87 Gy for bladder, rectum, right femoral head, left femoral head, small bowel, and large bowel respectively. The average NTCP was 7.6 % for the rectum and negligible for other organs at risk. Conclusion: NTCP for critical organs and TCP for prostate cancer can be determined via dose volume histogram.

Delineation of clinical target volume and organs at risk in cervical cancer radiotherapy by deep learning networks.

Delineation of the clinical target volume (CTV) and organs-at-risk (OARs) is important in cervical cancer radiotherapy. But it is generally labor-intensive, time-consuming, and subjective. This paper proposes a parallel-path attention fusion network (PPAF-net) to overcome these disadvantages in the delineationtask. The PPAF-net utilizes both the texture and structure information of CTV and OARs by employing a U-Net network to capture the high-level texture information, and an up-sampling and down-sampling (USDS) network to capture the low-level structure information to accentuate the boundaries of CTV and OARs. Multi-level features extracted from both networks are then fused together through an attention module to generate the delineationresult. The dataset contains 276 computed tomography (CT) scans of patients with cervical cancer of staging IB-IIA. The images are provided by the West China Hospital of Sichuan University. Simulation results demonstrate that PPAF-net performs favorably on the delineation of the CTV and OARs (e.g., rectum, bladder and etc.) and achieves the state-of-the-art delineation accuracy, respectively, for the CTV and OARs. In terms of the Dice Similarity Coefficient (DSC) and the Hausdorff Distance (HD), 88.61% and 2.25 cm for the CTV, 92.27% and 0.73 cm for the rectum, 96.74% and 0.68 cm for the bladder, 96.38% and 0.65 cm for the left kidney, 96.79% and 0.63 cm for the right kidney, 93.42% and 0.52 cm for the left femoral head, 93.69% and 0.51 cm for the right femoral head, 87.53% and 1.07 cm for the small intestine, and 91.50% and 0.84 cm for the spinalcord. The proposed automatic delineation network PPAF-net performs well on CTV and OARs segmentation tasks, which has great potential for reducing the burden of radiation oncologists and increasing the accuracy of delineation. In future, radiation oncologists from the West China Hospital of Sichuan University will further evaluate the results of network delineation, making this method helpful in clinicalpractice.

Effects of bone marrow-derived mesenchymal stem cell transplantation in piglet Legg-Calve-Perthes disease models: a pilot study.

This preliminary study investigated the efficacy and safety of bone marrow-derived mesenchymal stem cell transplantation in a piglet Legg-Calve-Perthes disease (LCPD) model. The LCPD model was induced in two Landrace piglets (6- and 7-week-old, weighing 12 and 17 kg, respectively) by ligaturing the femoral neck. In the first piglet, the natural LCPD course was observed. In the second piglet, 4 weeks after ligaturing the femoral neck, simple medium and medium containing 2.44 × 10 7 bone marrow-derived mesenchymal stem cells were transplanted into the right and left femoral heads after core decompression, respectively. Plain radiographs were obtained every 4 weeks, and the epiphyseal quotient was calculated by dividing the maximum epiphysis height by the maximum epiphysis diameter. The piglets were sacrificed at 14 weeks postoperatively. The femoral heads were extracted and evaluated grossly, pathologically, and by using computed tomography. The transplanted cell characteristics were evaluated using flow cytometry. Flattening of the epiphysis was observed in both femoral heads of the first piglet and only in the right hip of the second piglet. The epiphyseal quotients immediately and at 14 weeks postoperatively in the right femoral head of the second piglet were 0.40 and 0.14, respectively, while those of the left femoral head were 0.30 and 0.42, respectively. Hematoxylin and eosin staining did not reveal physeal bar or tumor cell formation. The transplanted cells were 99.2%, 65.9%, 18.2%, and 0.16% positive for CD44, CD105, CD29, and CD31, respectively. Core decompression combined with bone marrow-derived mesenchymal stem cell transplantation prevented epiphyseal collapse.

Phase-contrast imaging with synchrotron hard X-ray reveals the effect of icariin on bone tissue morphology and microstructure in rabbits with early glucocorticoid-induced osteonecrosis of the femoral head.

Background: Phase-contrast imaging (PCI) with synchrotron hard X-ray was used to observe the changes in bone tissue morphology and microstructure in rabbit models of early glucocorticoid-induced osteonecrosis of the femoral head (ONFH), and to evaluate the intervention effect of Icariin. Methods: Fifty mature New Zealand rabbits (weighing 2.5-3.0kg) were randomly divided into a control group (n = 10), a glucocorticoid group (n = 20), and an Icariin group (n = 20). The glucocorticoid group and the Icariin group were sequentially injected with lipopolysaccharide (LPS) and methylprednisolone (MPS) to establish a glucocorticoid-induced ONFH animal model. The Icariin group was given Icariin solution when methylprednisolone was injected for the first time, and the control group and glucocorticoid group were given the same amount of normal saline. Animals were sacrificed after 6weeks, and bilateral femoral head specimens were taken for research. The right femoral head was observed by PCI with synchrotron hard X-ray technology, and the left femoral head was verified by Micro-CT scanning and HE staining. Results: Forty-three animals (nine in the control group, sixteen in the glucocorticoid group, and eighteen in the Icariin group) were included in the study. PCI with synchrotron hard X-ray revealed that the trabecular bone in the glucocorticoid group was thinned, broken, and structurally damaged, whereas the trabecular bone in the Icariin group had normal volume, thickness, and a relatively intact structure. Micro-CT scan reconstruction and HE staining were used to verify the reliability of this technique in identifying osteonecrosis. Conclusion: The effects of Icariin were observed in an early glucocorticoid-induced ONFH rabbit model using PCI with synchrotron hard X-ray. Icariin weakens the destructive effect of glucocorticoids on bone tissue structure, improves bone tissue morphology, and stabilizes bone microstructure. This technique may provide a definitive, non-invasive alternative to histological examination for the diagnosis of early ONFH.

Comprehensive clinical evaluation of deep learning-based auto-segmentation for radiotherapy in patients with cervical cancer.

Deep learning-based models have been actively investigated for various aspects of radiotherapy. However, for cervical cancer, only a few studies dealing with the auto-segmentation of organs-at-risk (OARs) and clinical target volumes (CTVs) exist. This study aimed to train a deep learning-based auto-segmentation model for OAR/CTVs for patients with cervical cancer undergoing radiotherapy and to evaluate the model's feasibility and efficacy with not only geometric indices but also comprehensive clinical evaluation. A total of 180 abdominopelvic computed tomography images were included (training set, 165; validation set, 15). Geometric indices such as the Dice similarity coefficient (DSC) and the 95% Hausdorff distance (HD) were analyzed. A Turing test was performed and physicians from other institutions were asked to delineate contours with and without using auto-segmented contours to assess inter-physician heterogeneity and contouring time. The correlation between the manual and auto-segmented contours was acceptable for the anorectum, bladder, spinal cord, cauda equina, right and left femoral heads, bowel bag, uterocervix, liver, and left and right kidneys (DSC greater than 0.80). The stomach and duodenum showed DSCs of 0.67 and 0.73, respectively. CTVs showed DSCs between 0.75 and 0.80. Turing test results were favorable for most OARs and CTVs. No auto-segmented contours had large, obvious errors. The median overall satisfaction score of the participating physicians was 7 out of 10. Auto-segmentation reduced heterogeneity and shortened contouring time by 30 min among radiation oncologists from different institutions. Most participants favored the auto-contouring system. The proposed deep learning-based auto-segmentation model may be an efficient tool for patients with cervical cancer undergoing radiotherapy. Although the current model may not completely replace humans, it can serve as a useful and efficient tool in real-world clinics.