Purpose: To define patients and tumor characteristics as well as therapy results, patients with pelvic osteosarcoma who were registered in the Cooperative Osteosarcoma Study Group (COSS) were analyzed. Patients and Methods: Sixty-seven patients with a high-grade pelvic osteosarcoma were eligible for this analysis. Fifteen patients had primary metastases. All patients received chemotherapy according to COSS protocols. Thirty-eight patients underwent limb-sparing surgery, 12 patients underwent hemipelvectomy, and 17 patients did not undergo definitive surgery. Eleven patients received irradiation to the primary tumor site: four postoperatively and seven as the only form of local therapy. Results: Local failure occurred in 47 of all 67 patients (70%) and in 31 of 50 patients (62%) who underwent definitive surgery. Five-year overall survival (OS) and progression-free survival rates were 27% and 19%, respectively. Large tumor size (P = .0137), primary metastases (P = .0001), and no or intralesional surgery (P < .0001) were poor prognostic factors. In 30 patients with no or intralesional surgery, 11 patients with radiotherapy had better OS than 19 patients without radiotherapy (P = .0033). Among the variables, primary metastasis, large tumor, no or intralesional surgery, no radiotherapy, existence of primary metastasis (relative risk [RR] = 3.456; P = .0009), surgical margin (intralesional or no surgical excision; RR = 5.619; P < .0001), and no radiotherapy (RR = 4.196; P = .0059) were independent poor prognostic factors. Conclusion: An operative approach with wide or marginal margins improves local control and OS. If the surgical margin is intralesional or excision is impossible, additional radiotherapy has a positive influence on prognosis.

This study considers the use of deep learning to diagnose osteoporosis from hip radiographs, and whether adding clinical data improves diagnostic performance over the image mode alone. For objective labeling, we collected a dataset containing 1131 images from patients who underwent both skeletal bone mineral density measurement and hip radiography at a single general hospital between 2014 and 2019. Osteoporosis was assessed from the hip radiographs using five convolutional neural network (CNN) models. We also investigated ensemble models with clinical covariates added to each CNN. The accuracy, precision, recall, specificity, negative predictive value (npv), F1 score, and area under the curve (AUC) score were calculated for each network. In the evaluation of the five CNN models using only hip radiographs, GoogleNet and EfficientNet b3 exhibited the best accuracy, precision, and specificity. Among the five ensemble models, EfficientNet b3 exhibited the best accuracy, recall, npv, F1 score, and AUC score when patient variables were included. The CNN models diagnosed osteoporosis from hip radiographs with high accuracy, and their performance improved further with the addition of clinical covariates from patient records.

Ewing sarcoma (ES) is rare in Japanese people, and only 30-40 patients develop the disease annually. To diagnose ES, molecular techniques that aim to detect characteristic fusion genes are commonly used in combination with conventional histological and immunohistochemical examinations. The treatment strategy for ES is characterized by multi-disciplinary collaboration between pediatric oncologists, medical oncologists, radiation oncologists, and orthopedic surgeons. In recent years, numerous large-scale national or international multi-institutional studies of ES have been performed. Pre- and postoperative intensive systemic chemotherapy with multiple anticancer drugs is the standard treatment method for ES. Depending on the obtained surgical margin, postoperative radiation might also be performed. If preoperative radiological examinations indicate that surgical excision would be difficult, preoperative radiation can be administered. As the treatment outcomes of ES have improved, late complications and secondary malignancies have become a problem. After treatment, patients with ES require very long-term follow-up in order to detect secondary malignancies and growth-related musculoskeletal complications.

Adenosine-to-inosine (A-to-I) RNA editing, a process mediated by adenosine deaminases that act on the RNA (ADAR) gene family, is a recently discovered epigenetic modification dysregulated in human cancers. However, the clinical significance and the functional role of RNA editing in colorectal cancer (CRC) remain unclear. We have systematically and comprehensively investigated the significance of the expression status of ADAR1 and of the RNA editing levels of antizyme inhibitor 1 (AZIN1 ), one of the most frequently edited genes in cancers, in 392 colorectal tissues from multiple independent CRC patient cohorts. Both ADAR1 expression and AZIN1 RNA editing levels were significantly elevated in CRC tissues when compared with corresponding normal mucosa. High levels of AZIN1 RNA editing emerged as a prognostic factor for overall survival and disease-free survival and were an independent risk factor for lymph node and distant metastasis. Furthermore, elevated AZIN1 editing identified high-risk stage II CRC patients. Mechanistically, edited AZIN1 enhances stemness and appears to drive the metastatic processes. We have demonstrated that edited AZIN1 functions as an oncogene and a potential therapeutic target in CRC. Moreover, AZIN1 RNA editing status could be used as a clinically relevant prognostic indicator in CRC patients.