Han-Soo Kim

Background: Patients undergoing surgery for bone metastases typically have advanced disease, and postoperative survival varies substantially. Accurate survival estimation is important for surgical decision-making and patient counseling. Several prognostic models have been externally validated in East Asian populations, but these tools were originally developed in Western cohorts and do not incorporate region-specific epidemiology or treatment patterns. Questions/purposes: (1) To develop, internally evaluate, and select a machine learning–based survival prediction model for patients undergoing surgery for nonspinal bone metastases using a multinational East Asian cohort. (2) To compare the performance of the selected model with that of an established Western prognostic tool developed by the Skeletal Oncology Research Group (SORG). (3) To identify which clinical features carried the greatest importance in the new model that we developed. Methods: All patients who underwent surgery for nonspinal bone metastases at three tertiary referral centers in the Republic of Korea, Taiwan, and Japan between January 2009 and December 2022 were included. In total, 1045 patients met the inclusion criteria. The median (range) age at surgery was 64 years (19 to 96), 46% (478 of 1045) of patients were female, and the femur was the most common metastatic site (66% [690]). Data for 3-month, 6-month, 1-year, 3-year, and 5-year overall survival were available for 82% (854), 68% (709), 51% (529), 23% (243), and 15% (160) of patients, respectively. The corresponding survival proportions were 84%, 71%, 56%, 36%, and 31%. Data on routinely available clinical, functional, and laboratory variables were collected, and candidate predictors were predefined based on clinical relevance and data availability across institutions. Missing data were < 4% for all variables in each institution and were handled by multivariate imputation by chained equations. We trained four models using different machine-learning algorithms, and the performance of each model was evaluated using leave-one-site-out validation, in which models were trained on data from two institutions and tested on the remaining institution to ensure separation between training and testing data sets. Model performance was assessed using the Concordance Index (C-index; the ability of the model to correctly rank patients according to their expected survival), Brier score (overall prediction error), time-dependent area under the curve (tdAUC; how well the model distinguishes patients with different survival outcomes at specific time points), calibration slope and intercept (agreement between predicted and observed survival), and decision curve analysis (the potential clinical benefit of using the model to guide treatment decisions). The best-performing model was designated as the East Asian Survival Tool for Bone Metastasis Surgery (EAST-BMS) and was compared with the SORG model. To allow a fair comparison, the performance of the SORG model was evaluated on the same held-out test data sets in each iteration of the leave-one-site-out validation, applying the same performance metrics used to select the final model. Results: Gradient boosting survival analysis demonstrated the most favorable overall performance and was selected as the EAST-BMS. The number of outcome events used for model evaluation was 170 at 3 months and 447 at 12 months. The EAST-BMS achieved tdAUC values of 0.81 (95% confidence interval [CI] 0.78 to 0.85) at 3 months and 0.78 (95% CI 0.70 to 0.84) at 12 months, compared with 0.81 (95% CI 0.74 to 0.86) and 0.76 (95% CI 0.67 to 0.83), respectively, for the SORG model, indicating comparable ability to distinguish patients with different survival outcomes. Brier scores were 0.12 (95% CI 0.09 to 0.15) and 0.23 (95% CI 0.17 to 0.28) for EAST-BMS versus 0.14 (95% CI 0.12 to 0.16) and 0.25 (95% CI 0.15 to 0.34) for SORG, indicating lower prediction error in EAST-BMS. Calibration intercepts were -0.08 (95% CI -0.25 to 0.09) versus -1.06 (95% CI -1.26 to -0.86) at 3 months and -0.35 (95% CI -0.49 to -0.22) versus -1.23 (95% CI -1.37 to -1.08) at 12 months, indicating better agreement between predicted and observed survival in EAST-BMS. Decision curve analysis showed wider threshold probability ranges with positive net clinical benefit for EAST-BMS (0.04 to 0.96 versus 0.05 to 0.66 at 3 months; 0.17 to 0.77 versus 0.08 to 0.67 at 12 months), which means that using the EAST-BMS to guide treatment decisions may provide greater clinical benefit than the SORG model. Albumin, Karnofsky performance status, percentage of lymphocytes, and C-reactive protein level were among the most influential predictors. Conclusion: The EAST-BMS, the first multinational machine-learning survival model for patients from East Asia undergoing surgery for nonspinal bone metastases of which we are aware, demonstrated favorable predictive accuracy and clinical utility. This web-based tool may support personalized prognostic assessment and surgical decision-making. It is freely available as a web-based tool at https://bms.east-mskonco.org. Level of Evidence: Level III, therapeutic study.

Background: Although radiographs are the first-line imaging modality, differentiating between neoplastic pathologic fractures and nonpathologic fractures on radiographs can sometimes be challenging. In this study, we aimed to develop and evaluate a deep learning model capable of distinguishing neoplastic pathologic fractures from nonpathologic fractures on hip radiographs to enhance diagnostic accuracy. Methods: This retrospective, multicenter study analyzed anteroposterior hip radiographs from patients who visited the emergency department at 4 different institutions. The deep learning model was trained on, and tested using, data from 338 patients at a single institution and externally validated on data from 488 patients across 3 additional institutions. Results: The model achieved an overall accuracy of 0.880, with a sensitivity of 0.882 and a specificity of 0.879, on the internal test set. It was then externally validated using the data of 488 patients (67 with neoplastic pathologic fracture and 421 with nonpathologic fracture) from institutions separate from where the model was developed. The model achieved an overall accuracy of 0.848, sensitivity of 0.910, and specificity of 0.786. Its performance was comparable with that of general orthopaedic surgeons. Conclusions: The developed deep learning model is a reliable and valid tool for distinguishing neoplastic pathologic fractures from nonpathologic fractures on hip radiographs. It has the potential to assist orthopaedic surgeons in resource-limited settings, where optimizing the interpretation of radiographs is critical for patient care. The model is publicly available at https://pathfxdx.org. Level of Evidence: Diagnostic Level III . See Instructions for Authors for a complete description of levels of evidence.

Kim S, et al. Clin Orthop Surg. 2025 May;17:e35. https://doi.org/10.4055/cios24336

Aims: This study aimed to compare the performance of survival prediction models for bone metastases of the extremities (BM-E) with pathological fractures in an Asian cohort, and investigate patient characteristics associated with survival.Methods: This retrospective cohort study included 469 patients, who underwent surgery for BM-E between January 2009 and March 2022 at a tertiary hospital in South Korea. Postoperative survival was calculated using the PATHFx3.0, SPRING13, OPTIModel, SORG, and IOR models. Model performance was assessed with area under the curve (AUC), calibration curve, Brier score, and decision curve analysis. Cox regression analyses were performed to evaluate the factors contributing to survival.Results: The SORG model demonstrated the highest discriminatory accuracy with AUC (0.80 (95% confidence interval (CI) 0.76 to 0.85)) at 12 months. In calibration analysis, the PATHfx3.0 and OPTIModel models underestimated survival, while the SPRING13 and IOR models overestimated survival. The SORG model exhibited excellent calibration with intercepts of 0.10 (95% CI -0.13 to 0.33) at 12 months. The SORG model also had lower Brier scores than the null score at three and 12 months, indicating good overall performance. Decision curve analysis showed that all five survival prediction models provided greater net benefit than the default strategy of operating on either all or no patients. Rapid growth cancer and low serum albumin levels were associated with three-, six-, and 12-month survival.Conclusion: State-of-art survival prediction models for BM-E (PATHFx3.0, SPRING13, OPTIModel, SORG, and IOR models) are useful clinical tools for orthopaedic surgeons in the decision-making process for the treatment in Asian patients, with SORG models offering the best predictive performance. Rapid growth cancer and serum albumin level are independent, statistically significant factors contributing to survival following surgery of BM-E. Further refinement of survival prediction models will bring about informed and patient-specific treatment of BM-E.Cite this article: Bone Joint J 2024;106-B(2):203–211.

Background: Osteosarcoma is the most common secondary malignancy among survivors of retinoblastoma. Most previous reports on secondary malignancy of retinoblastoma included all types of secondary malignancies without a focus on osteosarcoma, owing to its rarity. In addition, there are few studies suggesting tools for regular surveillance for early detection. Questions/purposes: (1) What are the radiologic and clinical characteristics of secondary osteosarcoma after retinoblastoma? (2) What is the clinical survivorship? (3) Is a radionuclide bone scan a reasonable imaging modality for early detection in patients with retinoblastoma? Methods: Between February 2000 and December 2019, we treated 540 patients for retinoblastoma. Twelve patients (six male, six female) subsequently developed an osteosarcoma in the extremities; two of these patients had two sites of osteosarcoma (10 femurs, four tibiae). A Technetium-99m bone scan image was examined annually in all patients for regular surveillance after the treatment of retinoblastoma as per our hospital’s policy. All patients were treated with the same strategy as that used for primary conventional osteosarcoma, namely neoadjuvant chemotherapy, wide excision, and adjuvant chemotherapy. The median follow-up period was 12 years (range 8 to 21 years). The median age at the time of diagnosis of osteosarcoma was 9 years (range 5 to 15 years), and the median interval from retinoblastoma diagnosis to osteosarcoma diagnosis was 8 years (range 5 to 15 years). Radiologic characteristics were assessed with plain radiographs and MRI, while clinical characteristics were assessed through a retrospective review of medical records. For clinical survivorship, we evaluated overall survival, local recurrence-free survival, and metastasis-free survival. We reviewed the results of bone scans and clinical symptoms at the time of diagnosis for osteosarcoma after retinoblastoma. Results: In nine of 14 patients, the tumor had a diaphyseal center, and five of the tumors were located at the metaphysis. The femur was the most common site (n = 10), followed by the tibia (n = 4). The median tumor size was 9 cm (range 5 to 13 cm). There was no local recurrence after surgical resection of the osteosarcoma, and the 5-year overall survival rate after the diagnosis of osteosarcoma was 86% (95% CI 68% to 100%). In all 14 tumors, the Technetium bone scan showed increased uptake in the lesions. Ten of 14 tumors were examined in clinic because of patient complaints of pain in the affected limb. Four patients showed no clinical symptoms detected by abnormal uptake on bone scan. Conclusion: For unclear reasons, secondary osteosarcomas in patients who were alive after the treatment of retinoblastoma had a slight predilection for the diaphysis of the long bone compared with patients with spontaneous osteosarcoma in other reports. The clinical survivorship of osteosarcoma as a secondary malignancy after retinoblastoma may not be inferior to that of conventional osteosarcoma. Close follow-up with at least yearly clinical assessment and bone scans or other imaging modalities appears to be helpful in detecting secondary osteosarcoma after the treatment of patients with retinoblastoma. Larger multi-institutional studies will be needed to substantiate these observations. Level of Evidence Level IV, therapeutic study.

Surgery for proximal femur metastases: endoprosthesis reconstruction or intramedullary nailing? Suraj Hindiskere, Han-Soo Kim, Yongsung Kim, Ilkyu Han

In orthopedic oncology, revisional surgery due to mechanical failure or local recurrence is not uncommon following limb salvage surgery using an endoprosthesis. However, due to the lack of clinical experience in limb salvage surgery using 3D-printed custom-made implants, there have been no reports of revision limb salvage surgery using a 3D-printed implant. Herein, we present two cases of representative revision limb salvage surgeries that utilized another 3D-printed custom-made implant while retaining the previous 3D-printed custom-made implant. A 3D-printed connector implant was used to connect the previous 3D-printed implant to the proximal ulna of a 40-year-old man and to the femur of a 69-year-old woman. The connector bodies for the two junctions of the previous implant and the remaining host bone were designed for the most functional position or angle by twisting or tilting. Using the previous 3D-printed implant as a taper, the 3D-printed connector was used to encase the outside of the previous implant. The gap between the previous implant and the new one was subsequently filled with bone cement. For both the upper and lower extremities, the 3D-printed connector showed stable reconstruction and excellent functional outcomes (Musculoskeletal Tumor Society scores of 87% and 100%, respectively) in the short-term follow-up. To retain the previous 3D-printed implant during revision limb salvage surgery, an additional 3D-printed implant may be a feasible surgical option.

A titanium alloy implant of appropriate pore size can potentially enhance osseointegration and soft tissue integration. However, the human clinical application of such implants has not been reported. Here, we present a case of limb salvage surgery for a bone tumor using customized three-dimensional (3D)-printed Ti6Al4V radius and ulna implants. The patient presented with local recurrence at the proximal junction of the ulna and underwent a re-wide excision. Single forearm bone surgery was performed using another 3D-printed implant after resection of the recurrent tumor with an ulnar implant. Host osseointegration and soft tissue integration of the retrieved implant were quantified through histological evaluation. The total tissue integration rates of the implant at the proximal and distal bone junctions were 45.96% and 15.03%, respectively. The mesh structure enhanced bone integration by up to 10.81% in the proximal and by up to 8.91% in the distal bone junction. Furthermore, the soft tissue adhesion rates of the implant shaft were 59.50% and 50.26% in the axial and longitudinal cuts, respectively. No area was left unoccupied throughout the shaft of the implant. Overall, these results indicate that the 3D-printed Ti6Al4V titanium alloy implant with a rough surface has considerable tissue integration ability.