Chimeric antigen receptor (CAR)-T cell therapy, which has demonstrated remarkable efficacy in hematologic malignancies, is being extended to the treatment of refractory solid tumors, including brain tumors. Lymphodepletion (LD) is an essential preconditioning process that enhances CAR-T efficacy by promoting CAR-T cell expansion and persistence in the body, and has become a standard regimen for hematologic cancers. Recent clinical results of CAR-T therapy for solid tumors, including brain tumors, have shown that cyclophosphamide/fludarabine-based preconditioning has potential benefits and is gradually becoming adopted in solid tumor CAR-T trials. Furthermore, some CAR-T trials for solid tumors are attempting to develop LD regimens optimized specifically for solid tumors, distinct from the standard LD regimens used in hematologic cancers. In contrast, CAR-T therapy targeting brain tumors frequently employs locoregionally repeated administration in tumors or cerebrospinal fluid, resulting in less frequent use of LD compared to other solid tumors. Nevertheless, several clinical studies suggest that LD may still provide potential benefits for CAR-T expansion and improvement in clinical responses in systemic CAR-T administration. The studies presented in this review suggest that while LD can be beneficial for enhancing CAR-T efficacy, considerations must be made regarding its compatibility with the CAR-T administration route, potential excessive activation based on CAR-T structural characteristics, and target expression in normal organs. Additionally, given the unique characteristics of brain tumors, optimized selection of LD agents, as well as dosing and regimens, may be required, highlighting the need for further research.

Chimeric antigen receptor (CAR)-T cell therapy, which has demonstrated remarkable efficacy in hematologic malignancies, is being extended to the treatment of refractory solid tumors, including brain tumors. Lymphodepletion (LD) is an essential preconditioning process that enhances CAR-T efficacy by promoting CAR-T cell expansion and persistence in the body, and has become a standard regimen for hematologic cancers. Recent clinical results of CAR-T therapy for solid tumors, including brain tumors, have shown that cyclophosphamide/fludarabine-based preconditioning has potential benefits and is gradually becoming adopted in solid tumor CAR-T trials. Furthermore, some CAR-T trials for solid tumors are attempting to develop LD regimens optimized specifically for solid tumors, distinct from the standard LD regimens used in hematologic cancers. In contrast, CAR-T therapy targeting brain tumors frequently employs locoregionally repeated administration in tumors or cerebrospinal fluid, resulting in less frequent use of LD compared to other solid tumors. Nevertheless, several clinical studies suggest that LD may still provide potential benefits for CAR-T expansion and improvement in clinical responses in systemic CAR-T administration. The studies presented in this review suggest that while LD can be beneficial for enhancing CAR-T efficacy, considerations must be made regarding its compatibility with the CAR-T administration route, potential excessive activation based on CAR-T structural characteristics, and target expression in normal organs. Additionally, given the unique characteristics of brain tumors, optimized selection of LD agents, as well as dosing and regimens, may be required, highlighting the need for further research.

Chimeric antigen receptor (CAR)-T cell therapy, which has demonstrated remarkable efficacy in hematologic malignancies, is being extended to the treatment of refractory solid tumors, including brain tumors. Lymphodepletion (LD) is an essential preconditioning process that enhances CAR-T efficacy by promoting CAR-T cell expansion and persistence in the body, and has become a standard regimen for hematologic cancers. Recent clinical results of CAR-T therapy for solid tumors, including brain tumors, have shown that cyclophosphamide/fludarabine-based preconditioning has potential benefits and is gradually becoming adopted in solid tumor CAR-T trials. Furthermore, some CAR-T trials for solid tumors are attempting to develop LD regimens optimized specifically for solid tumors, distinct from the standard LD regimens used in hematologic cancers. In contrast, CAR-T therapy targeting brain tumors frequently employs locoregionally repeated administration in tumors or cerebrospinal fluid, resulting in less frequent use of LD compared to other solid tumors. Nevertheless, several clinical studies suggest that LD may still provide potential benefits for CAR-T expansion and improvement in clinical responses in systemic CAR-T administration. The studies presented in this review suggest that while LD can be beneficial for enhancing CAR-T efficacy, considerations must be made regarding its compatibility with the CAR-T administration route, potential excessive activation based on CAR-T structural characteristics, and target expression in normal organs. Additionally, given the unique characteristics of brain tumors, optimized selection of LD agents, as well as dosing and regimens, may be required, highlighting the need for further research.

Objective: To evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs. Materials and Methods: Of 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience. Results: The area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988–0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618– 0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (p = 0.180). However, the proposed model showed higher sensitivity, specificity, and PPV, compared to the radiologist without experience in pediatric radiology (p < 0.001). Conclusion The proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.

Objective: To evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs. Materials and Methods: Of 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience. Results: The area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988–0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618– 0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (p = 0.180). However, the proposed model showed higher sensitivity, specificity, and PPV, compared to the radiologist without experience in pediatric radiology (p < 0.001). Conclusion The proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.