Search

The present retrospective study investigated the clinical features and prognosis of secondary hematological malignancies (SHMs) in patients with sarcoma at Korea Cancer Center Hospital (Seoul, South Korea). Patients who had been diagnosed with SHMs after having received treatment for sarcoma between January 2000 and May 2023 were enrolled. Clinical data were collected from the patients' medical records. Clinical characteristics were analyzed, including SHM incidence, type and prognosis. Of 2,953 patients with sarcoma, 18 (0.6%) were diagnosed with SHMs. Their median age at the time of sarcoma diagnosis was 39.5 (range, 9–72) years, and 74% (n=14) of these patients were male. The histological features of sarcoma varied, with osteosarcoma diagnosed in nine patients (50%). All patients with sarcoma underwent surgical treatment, and 16 (88.8%) received chemotherapy. The most common type of SHMs was acute myeloid leukemia (n=6; 33.3%), followed by myelodysplastic syndrome (n=5; 27.7%). The median latency period between the sarcoma diagnosis and SHM identification was 30 (range, 11–121) months. A total of 13 (72.2%) patients received treatment for the SHM. The median overall survival after SHM diagnosis was 15.7 (range, 0.4–154.9) months. The incidence of SHMs in sarcoma in the present study was consistent with that reported previously. The presence of SHMs was associated with a poor patient prognosis, especially if treatment for SHMs was not administered. [ABSTRACT FROM AUTHOR]

Purpose. Patients with high-grade osteosarcoma undergo several chemotherapy cycles before surgical intervention. Response to chemotherapy, however, is affected by intratumor heterogeneity. In this study, we assessed the ability of a machine learning approach using baseline 18F-fluorodeoxyglucose (18F-FDG) positron emitted tomography (PET) textural features to predict response to chemotherapy in osteosarcoma patients. Materials and Methods. This study included 70 osteosarcoma patients who received neoadjuvant chemotherapy. Quantitative characteristics of the tumors were evaluated by standard uptake value (SUV), total lesion glycolysis (TLG), and metabolic tumor volume (MTV). Tumor heterogeneity was evaluated using textural analysis of 18F-FDG PET scan images. Assessments were performed at baseline and after chemotherapy using 18F-FDG PET; 18F-FDG textural features were evaluated using the Chang-Gung Image Texture Analysis toolbox. To predict the chemotherapy response, several features were chosen using the principal component analysis (PCA) feature selection method. Machine learning was performed using linear support vector machine (SVM), random forest, and gradient boost methods. The ability to predict chemotherapy response was evaluated using the area under the receiver operating characteristic curve (AUC). Results. AUCs of the baseline 18F-FDG features SUVmax, TLG, MTV, 1st entropy, and gray level co-occurrence matrix entropy were 0.553, 0538, 0.536, 0.538, and 0.543, respectively. However, AUCs of the machine learning features linear SVM, random forest, and gradient boost were 0.72, 0.78, and 0.82, respectively. Conclusion. We found that a machine learning approach based on 18F-FDG textural features could predict the chemotherapy response using baseline PET images. This early prediction of the chemotherapy response may aid in determining treatment plans for osteosarcoma patients. [ABSTRACT FROM AUTHOR]

Background: Pasteurized autograft is regarded as a biologic reconstructive option for managing bone defects after tumor resection; however, reports on long-term outcomes from large patient series are scarce. Contrary to previous favorable reports, we have observed many patients with failures, in particular as the duration of followup increased. Because pasteurized autografts are used in many countries as a reconstruction option, we wished to formally evaluate patients who underwent this approach at one specialty center.Questions/purposes: (1) What is the graft survival and what proportion of patients achieved union when pasteurized autografts were used for bone defects after tumor resection? (2) What are the complications and causes of graft removal? (3) What factors are related to the likelihood of union and graft survival? (4) What is the survival and cause of failure by type of pasteurized autograft reconstruction?Methods: Over a 26-year period from 1988 to 2013, we performed 1358 tumor resections in our center. Of these, 353 were reconstructed with pasteurized autograft. Other reconstructions included endoprostheses (508 patients), instant arthrodesis using an intramedullary nail and bone cement (286 patients), allografts (97 patients), and resection only (114 patients). During the period in question, we generally used this approach when tumor showed an osteoblastic pattern and less than one-third cortical destruction in osteolytic tumor. We generally avoided this approach when the tumor showed an extensive osteolytic pattern. We excluded 75 (21% [75 of 353]) patients, 21 (6% [21 of 353]) for incomplete clinical data and 54 (15% [54 of 353]) with a followup < 2 years or those lost to followup leaving 278 autografts eligible. The mean followup was 113 months (range, 25-295 months). Of these 278 patients, 242 patients had primary bone sarcomas, 22 patients had soft tissue tumor invading bone, seven patients had metastatic carcinoma, and seven patients had aggressive benign bone tumors. From a chart review, we obtained the age, sex, location, tumor volume, histologic diagnosis, use of chemotherapy, graft length, fixation modality, type of pasteurized bone used, proportion of union, complications, and oncologic outcome of the patients. In total, 377 junctional sites were assessed for union with serial radiographs. We defined junctions showing union < 2 years as union and > 2 years as delayed union. We grouped our patients into type of pasteurized bone use: pasteurized autograft-prosthesis composites (PPCs) were performed in 149, intercalary grafts in 71, hemicortical grafts in 15, osteoarticular in 12, and fusion of a joint in 31 patients. The endpoint of interest included removal of the autograft with implant loosening, infection, fracture of the graft, or any reoperation resulting in removal. Survival of the graft was determined by Kaplan-Meier plot and intergroup differences were determined using log-rank test.Results: Five, 10-, and 20-year survival of 278 autografts was 73% ± 5.5%, 59% ± 6.7%, and 40% ± 13.6%, respectively. Of 278 autografts, 105 (38%) were removed with complications. Cause of removal included infection in 13% (33 patients), nonunion in 7% (18 patients), fracture of graft in 6% (16 patients), resorption of the graft in 5% (14 patients), and local recurrence in 4% (11 patients). Univariate survival analysis revealed that patient age ≤ 15 years (p = 0.027; hazard ratio [HR], 1.541), male sex (p = 0.004; HR, 1.810), and pelvic location (p = 0.05; HR, 2.518) were associated with graft removal. The 20-year survival rate of osteoarticular and hemicortical methods was 92% (95% confidence interval, -15.6% to +8.3%) and 80% ± 20%, respectively. For intercalary and fusion, it was 46% ± 15% and 28% ± 22%, respectively, although for PPC, it was 37% ± 22%. Log-rank survival analysis showed the osteoarticular and hemicortical groups had better graft survival compared with other types of reconstruction (p = 0.028; HR, 0.499). The most prevalent cause of graft removal in three major types of reconstruction was as follows: (1) PPC type was infection (30% [17 of 56]); (2) intercalary graft was infection, nonunion, and local recurrence in even proportions of 29% (86% [24 of 28]); and (3) fusion was infection (35% [six of 17]). Two hundred ten (56%) of 377 junctional sites showed union within 2 years (average, 14 months), 51 (13%) junctions showed delayed union after 2 years (average, 40 months), and the remaining 116 (31%) junctions showed nonunion. Diaphyseal junction (p = 0.029) and male sex (p = 0.004) showed a higher proportion of nonunion by univariate analysis.Conclusions: Compared with the favorable short-term and small cohort reports, survival of pasteurized autograft in this long-term large cohort was disappointing. We believe that pasteurized autograft should be used with caution in children and adolescents, in the pelvic region, and in PPC form. When bone stock destruction is minimal, it is worth considering this approach for small intercalary or distal long bone reconstruction. We believe this procedure is best indicated after hemicortical resection of long bone.Level Of Evidence: Level III, therapeutic study. [ABSTRACT FROM AUTHOR]

We compared the accuracy of prediction of the response to neoadjuvant chemotherapy (NAC) in osteosarcoma patients between machine learning approaches of whole tumor utilizing fluorine−18fluorodeoxyglucose (18F-FDG) uptake heterogeneity features and a convolutional neural network of the intratumor image region. In 105 patients with osteosarcoma, 18F-FDG positron emission tomography/computed tomography (PET/CT) images were acquired before (baseline PET0) and after NAC (PET1). Patients were divided into responders and non-responders about neoadjuvant chemotherapy. Quantitative 18F-FDG heterogeneity features were calculated using LIFEX version 4.0. Receiver operating characteristic (ROC) curve analysis of 18F-FDG uptake heterogeneity features was used to predict the response to NAC. Machine learning algorithms and 2-dimensional convolutional neural network (2D CNN) deep learning networks were estimated for predicting NAC response with the baseline PET0 images of the 105 patients. ML was performed using the entire tumor image. The accuracy of the 2D CNN prediction model was evaluated using total tumor slices, the center 20 slices, the center 10 slices, and center slice. A total number of 80 patients was used for k-fold validation by five groups with 16 patients. The CNN network test accuracy estimation was performed using 25 patients. The areas under the ROC curves (AUCs) for baseline PET maximum standardized uptake value (SUVmax), total lesion glycolysis (TLG), metabolic tumor volume (MTV), and gray level size zone matrix (GLSZM) were 0.532, 0.507, 0.510, and 0.626, respectively. The texture features test accuracy of machine learning by random forest and support vector machine were 0.55 and 0. 54, respectively. The k-fold validation accuracy and validation accuracy were 0.968 ± 0.01 and 0.610 ± 0.04, respectively. The test accuracy of total tumor slices, the center 20 slices, center 10 slices, and center slices were 0.625, 0.616, 0.628, and 0.760, respectively. The prediction model for NAC response with baseline PET0 texture features machine learning estimated a poor outcome, but the 2D CNN network using 18F-FDG baseline PET0 images could predict the treatment response before prior chemotherapy in osteosarcoma. Additionally, using the 2D CNN prediction model using a tumor center slice of 18F-FDG PET images before NAC can help decide whether to perform NAC to treat osteosarcoma patients. [ABSTRACT FROM AUTHOR]