Purpose: Retroperitoneal sarcomas (RPS) are rare tumors that present unique challenges, often due to late presentation, and the proximity of critical organs makes complete surgical resection challenging. This study aimed to assess the feasibility of neoadjuvant short-course radiotherapy (SCRT) targeting margins-at-risk and to assess its potential impact on outcomes. Materials and Methods: This is a single-center, prospective, non-randomized feasibility study. SCRT was administered via image-guided volumetric modulated arc therapy, consisting of 5 fractions of daily radiotherapy followed by immediate surgery. As a starting dose, patients were prescribed 25 Gy in 5 fractions. For the escalation stage, patients were prescribed 30 Gy in 5 fractions. Only the presumed threatened surgical margins were delineated for large tumors. Results: Patients with either primary or recurrent RPS were recruited. Eight patients underwent SCRT but one patient did not have a resection as planned. Seven patients underwent surgical resection, of whom one passed away 3 months postoperative from a cardiac event. After a median follow-up of 20.5 months for the six postoperative survivors, there were no overt long-term toxicities and one patient relapsed out-of-radiotherapy-field. Conclusion SCRT to RPS with a margin boost followed by immediate surgery is worth investigating. A starting dose of 30 Gy in 5 fractions is recommended for further studies. Longer-term follow-up is necessary.

Purpose: Retroperitoneal sarcomas (RPS) are rare tumors that present unique challenges, often due to late presentation, and the proximity of critical organs makes complete surgical resection challenging. This study aimed to assess the feasibility of neoadjuvant short-course radiotherapy (SCRT) targeting margins-at-risk and to assess its potential impact on outcomes. Materials and Methods: This is a single-center, prospective, non-randomized feasibility study. SCRT was administered via image-guided volumetric modulated arc therapy, consisting of 5 fractions of daily radiotherapy followed by immediate surgery. As a starting dose, patients were prescribed 25 Gy in 5 fractions. For the escalation stage, patients were prescribed 30 Gy in 5 fractions. Only the presumed threatened surgical margins were delineated for large tumors. Results: Patients with either primary or recurrent RPS were recruited. Eight patients underwent SCRT but one patient did not have a resection as planned. Seven patients underwent surgical resection, of whom one passed away 3 months postoperative from a cardiac event. After a median follow-up of 20.5 months for the six postoperative survivors, there were no overt long-term toxicities and one patient relapsed out-of-radiotherapy-field. Conclusion SCRT to RPS with a margin boost followed by immediate surgery is worth investigating. A starting dose of 30 Gy in 5 fractions is recommended for further studies. Longer-term follow-up is necessary.

Purpose: Retroperitoneal sarcomas (RPS) are rare tumors that present unique challenges, often due to late presentation, and the proximity of critical organs makes complete surgical resection challenging. This study aimed to assess the feasibility of neoadjuvant short-course radiotherapy (SCRT) targeting margins-at-risk and to assess its potential impact on outcomes. Materials and Methods: This is a single-center, prospective, non-randomized feasibility study. SCRT was administered via image-guided volumetric modulated arc therapy, consisting of 5 fractions of daily radiotherapy followed by immediate surgery. As a starting dose, patients were prescribed 25 Gy in 5 fractions. For the escalation stage, patients were prescribed 30 Gy in 5 fractions. Only the presumed threatened surgical margins were delineated for large tumors. Results: Patients with either primary or recurrent RPS were recruited. Eight patients underwent SCRT but one patient did not have a resection as planned. Seven patients underwent surgical resection, of whom one passed away 3 months postoperative from a cardiac event. After a median follow-up of 20.5 months for the six postoperative survivors, there were no overt long-term toxicities and one patient relapsed out-of-radiotherapy-field. Conclusion SCRT to RPS with a margin boost followed by immediate surgery is worth investigating. A starting dose of 30 Gy in 5 fractions is recommended for further studies. Longer-term follow-up is necessary.

Purpose: Retroperitoneal sarcomas (RPS) are rare tumors that present unique challenges, often due to late presentation, and the proximity of critical organs makes complete surgical resection challenging. This study aimed to assess the feasibility of neoadjuvant short-course radiotherapy (SCRT) targeting margins-at-risk and to assess its potential impact on outcomes. Materials and Methods: This is a single-center, prospective, non-randomized feasibility study. SCRT was administered via image-guided volumetric modulated arc therapy, consisting of 5 fractions of daily radiotherapy followed by immediate surgery. As a starting dose, patients were prescribed 25 Gy in 5 fractions. For the escalation stage, patients were prescribed 30 Gy in 5 fractions. Only the presumed threatened surgical margins were delineated for large tumors. Results: Patients with either primary or recurrent RPS were recruited. Eight patients underwent SCRT but one patient did not have a resection as planned. Seven patients underwent surgical resection, of whom one passed away 3 months postoperative from a cardiac event. After a median follow-up of 20.5 months for the six postoperative survivors, there were no overt long-term toxicities and one patient relapsed out-of-radiotherapy-field. Conclusion SCRT to RPS with a margin boost followed by immediate surgery is worth investigating. A starting dose of 30 Gy in 5 fractions is recommended for further studies. Longer-term follow-up is necessary.

Purpose: Retroperitoneal sarcomas (RPS) are rare tumors that present unique challenges, often due to late presentation, and the proximity of critical organs makes complete surgical resection challenging. This study aimed to assess the feasibility of neoadjuvant short-course radiotherapy (SCRT) targeting margins-at-risk and to assess its potential impact on outcomes. Materials and Methods: This is a single-center, prospective, non-randomized feasibility study. SCRT was administered via image-guided volumetric modulated arc therapy, consisting of 5 fractions of daily radiotherapy followed by immediate surgery. As a starting dose, patients were prescribed 25 Gy in 5 fractions. For the escalation stage, patients were prescribed 30 Gy in 5 fractions. Only the presumed threatened surgical margins were delineated for large tumors. Results: Patients with either primary or recurrent RPS were recruited. Eight patients underwent SCRT but one patient did not have a resection as planned. Seven patients underwent surgical resection, of whom one passed away 3 months postoperative from a cardiac event. After a median follow-up of 20.5 months for the six postoperative survivors, there were no overt long-term toxicities and one patient relapsed out-of-radiotherapy-field. Conclusion SCRT to RPS with a margin boost followed by immediate surgery is worth investigating. A starting dose of 30 Gy in 5 fractions is recommended for further studies. Longer-term follow-up is necessary.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.