A deep learning-machine learning fusion approach for the classification of benign, malignant, and intermediate bone tumors.

OBJECTIVES: To build and validate deep learning and machine learning fusion models to classify benign, malignant, and intermediate bone tumors based on patient clinical characteristics and conventional radiographs of the lesion. METHODS: In this retrospective study, data were collected with pathologically confirmed diagnoses of bone tumors between 2012 and 2019. Deep learning and machine learning fusion models were built to classify tumors as benign, malignant, or intermediate using conventional radiographs of the lesion and potentially relevant clinical data. Five radiologists compared diagnostic performance with and without the model. Diagnostic performance was evaluated using the area under the curve (AUC). RESULTS: A total of 643 patients' (median age, 21 years; interquartile range, 12-38 years; 244 women) 982 radiographs were included. In the test set, the binary category classification task, the radiological model of classification for benign/not benign, malignant/nonmalignant, and intermediate/not intermediate had AUCs of 0.846, 0.827, and 0.820, respectively; the fusion models had an AUC of 0.898, 0.894, and 0.865, respectively. In the three-category classification task, the radiological model achieved a macro average AUC of 0.813, and the fusion model had a macro average AUC of 0.872. In the observation test, the mean macro average AUC of all radiologists was 0.819. With the three-category classification fusion model support, the macro AUC improved by 0.026. CONCLUSION: We built, validated, and tested deep learning and machine learning models that classified bone tumors at a level comparable with that of senior radiologists. Model assistance may somewhat help radiologists' differential diagnoses of bone tumors. KEY POINTS: • The deep learning model can be used to classify benign, malignant, and intermediate bone tumors. • The machine learning model fusing information from radiographs and clinical characteristics can improve the classification capacity for bone tumors. • The diagnostic performance of the fusion model is comparable with that of senior radiologists and is potentially useful as a complement to radiologists in a bone tumor differential diagnosis.

Predicting the molecular subtype of breast cancer and identifying interpretable imaging features using machine learning algorithms.

OBJECTIVES: To evaluate the performance of interpretable machine learning models in predicting breast cancer molecular subtypes. METHODS: We retrospectively enrolled 600 patients with invasive breast carcinoma between 2012 and 2019. The patients were randomly divided into a training (n = 450) and a testing (n = 150) set. The five constructed models were trained based on clinical characteristics and imaging features (mammography and ultrasonography). The model classification performances were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity. Shapley additive explanation (SHAP) technique was used to interpret the optimal model output. Then we choose the optimal model as the assisted model to evaluate the performance of another four radiologists in predicting the molecular subtype of breast cancer with or without model assistance, according to mammography and ultrasound images. RESULTS: The decision tree (DT) model performed the best in distinguishing triple-negative breast cancer (TNBC) from other breast cancer subtypes, yielding an AUC of 0.971; accuracy, 0.947; sensitivity, 0.905; and specificity, 0.941. The accuracy, sensitivity, and specificity of all radiologists in distinguishing TNBC from other molecular subtypes and Luminal breast cancer from other molecular subtypes have significantly improved with the assistance of DT model. In the diagnosis of TNBC versus other subtypes, the average sensitivity, average specificity, and average accuracy of less experienced and more experienced radiologists increased by 0.090, 0.125, 0.114, and 0.060, 0.090, 0.083, respectively. In the diagnosis of Luminal versus other subtypes, the average sensitivity, average specificity, and average accuracy of less experienced and more experienced radiologists increased by 0.084, 0.152, 0.159, and 0.020, 0.100, 0.048. CONCLUSIONS: This study established an interpretable machine learning model to differentiate between breast cancer molecular subtypes, providing additional values for radiologists. KEY POINTS: • Interpretable machine learning model (MLM) could help clinicians and radiologists differentiate between breast cancer molecular subtypes. • The Shapley additive explanations (SHAP) technique can select important features for predicting the molecular subtypes of breast cancer from a large number of imaging signs. • Machine learning model can assist radiologists to evaluate the molecular subtype of breast cancer to some extent.

Dihydroartemisinin enhances the radiosensitivity of breast cancer by targeting ferroptosis signaling pathway through hsa_circ_0001610.

OBJECTIVE: This study aimed to elucidate how DHA enhances the radiosensitivity of BC and to explain its potential mechanisms of action. METHODS: The circular structure of hsa_circ_0001610 was confirmed by Sanger sequencing, RNase R treatment, RT-PCR analysis using gDNA or cDNA. Cellular localization of hsa_circ_0001610 and microRNA-139-5p (miR-139-5p) was detected by fluorescence in situ hybridization. Cell counting kit-8 assay, wound healing and colony formation tests for assessing cell proliferation, while flow cytometry was utilized to estimate cell cycle progression and apoptosis. Reactive oxygen species and malondialdehyde experiments were conducted to validate ferroptosis of BC cells. The expression of ncRNAs and mRNAs was quantified via qRT-PCR, and protein expression was analyzed using Western blot. The effects of hsa_circ_0001610 and DHA on radiosensitivity of BC in vivo were studied by establishing BC mice model. RESULTS: In vivo and in vitro experimental results indicate that DHA promotes ferroptosis of BC cells at least partly by inhibiting hsa_circ_0001610/miR-139-5p/SLC7A11 pathway, thereby enhancing the radiosensitivity of BC cells. CONCLUSIONS: Our findings showed that DHA can induce ferroptosis of BC cells by down-regulation of hsa_circ_0001610, thus enhancing radiosensitivity, suggesting a promising therapeutic strategy for enhancing BC radiosensitivity that is worthy of further exploration.

Clinical and imaging features of 112 patients with irregular and flat bone osteosarcoma.

Background: This study evaluated the clinical characteristics and imaging findings of 112 patients with irregular and flat bone osteosarcoma (IFBO). Methods: The age, gender, location, tumor size, density and signal intensity, osteoid matrix, periosteal reaction, and histological subtypes were analyzed for 112 patients with IFBO. Results: A total of 112 patients with IFBO, including 64 males and 48 females, with a mean age of 34.8 years were enrolled in this study. Over half of the tumors (54.5%) were detected in the craniofacial region and the skull (24 in the maxilla bone, 17 in the mandible bone, 11 in the sphenoid bone, 7 in the temporal bone, 1 in the frontal bone, and 1 in the occipital bone). Other tumor locations included the pelvic region (20.5%; 20 in the ilium and 3 in the pubis), the chest (18.8%; 11 in the scapula, 7 in the ribs, and 3 in the clavicle), and the vertebrae (6.3%; 3 in the thoracic spine, 2 in the lumbar spine, 1 in the sacrum, and 1 in the cervical spine). Transarticular extension occurred in 11 of the 23 pelvic cases (47.8%), primarily involving the sacroiliac joint (90.9%; 10 of 11). Six cases (6/7; 85.7%) of vertebral osteosarcoma arose from the transverse process and the pedicle, and 1 (1/7; 14.3%) arose from the sacral tuberosity and the ala, with partial vertebral body involvement. Additionally, 27 patients (24.1%) presented with secondary osteosarcoma related to prior radiotherapy, and 2 (1.8%) were associated with osteoblastoma and fibrous dysplasia. Histological examination revealed high-grade tumors in 88 (78.6%) cases. The tumors presented as soft-tissue masses with a diameter of 7.5±3.2 cm. A total of 91 patients underwent X-ray examination and/or computed tomography (CT) examinations. The osteoid matrix was detected in 84 patients (84/91;92.3%). A periosteal reaction was detected in 56 cases (56/91; 61.5%), including a lamellar periosteal reaction in 10 patients (11.0%) and a spiculated periosteal reaction in 46 cases (50.5%). All 74 cases who underwent magnetic resonance imaging (MRI) examinations presented with heterogeneous masses in the surrounding soft tissue. Enhancement was homogenous in 12 cases (18.5%) and heterogeneous in 53 cases (81.5%). Peripheral rim enhancement was observed in 10 cases (13.5%). Conclusions: IFBO should be considered when diagnosing patients over 30 years of age who exhibit osteoid matrix in bone lesions. Maxillofacial osteosarcoma is commonly associated with a history of radiation exposure. Pelvic osteosarcoma is more likely to invade the sacroiliac joint. Vertebral osteosarcoma frequently arises in the transverse process and pedicle, with partial body involvement.

Using Machine Learning to Unravel the Value of Radiographic Features for the Classification of Bone Tumors.

OBJECTIVES: To build and validate random forest (RF) models for the classification of bone tumors based on the conventional radiographic features of the lesion and patients' clinical characteristics, and identify the most essential features for the classification of bone tumors. MATERIALS AND METHODS: In this retrospective study, 796 patients (benign bone tumors: 412 cases, malignant bone tumors: 215 cases, intermediate bone tumors: 169 cases) with pathologically confirmed bone tumors from Nanfang Hospital of Southern Medical University, Foshan Hospital of TCM, and University of Hong Kong-Shenzhen Hospital were enrolled. RF models were built to classify tumors as benign, malignant, or intermediate based on conventional radiographic features and potentially relevant clinical characteristics extracted by three musculoskeletal radiologists with ten years of experience. SHapley Additive exPlanations (SHAP) was used to identify the most essential features for the classification of bone tumors. The diagnostic performance of the RF models was quantified using receiver operating characteristic (ROC) curves. RESULTS: The features extracted by the three radiologists had a satisfactory agreement and the minimum intraclass correlation coefficient (ICC) was 0.761 (CI: 0.686-0.824, P < .001). The binary and tertiary models were built to classify tumors as benign, malignant, or intermediate based on the imaging and clinical features from 627 and 796 patients. The AUC of the binary (19 variables) and tertiary (22 variables) models were 0.97 and 0.94, respectively. The accuracy of binary and tertiary models were 94.71% and 82.77%, respectively. In descending order, the most important features influencing classification in the binary model were margin, cortex involvement, and the pattern of bone destruction, and the most important features in the tertiary model were margin, high-density components, and cortex involvement. CONCLUSIONS: This study developed interpretable models to classify bone tumors with great performance. These should allow radiographers to identify imaging features that are important for the classification of bone tumors in the clinical setting.

CT and MRI features of calvarium and skull base osteosarcoma (CSBO).

OBJECTIVE: This study aims to assess the CT and MRI features of calvarium and skull base osteosarcoma (CSBO). METHODS: The CT and MRI features and pathological characteristics of 12 cases of pathologically confirmed CSBO were analyzed retrospectively. RESULTS: 12 patients (age range 9-67 years; 3 male, 9 female) were included in the study. Tumours occurred in skull base (7, 58.3%), temporal (4, 33.3%) and frontal (1, 8.3%). Among all, six patients received radiotherapy for nasopharyngeal carcinoma. According to pathology, 11 out of 12 tumours were high-grade (91.7%). On CT, all the tumours had soft tissue mass penetrated into cortical bone with invasion of surrounding soft tissue. Six tumours were shown to have lytic density and six were mixed density. Matrix mineralization was present in 10 cases (83.3%). On MRI, tumours presented as soft-tissue masses measuring 5.9 ± 2.4 (3.9-8.0) cm. Five tumours showed low signal intensities on T1 weighted imaging with seven having heterogeneous signal intensities. One showed low signal intensity on T2 weighted imaging, two showed high signal intensities and nine heterogeneous signal intensities. All the tumours showed low signal intensities on diffusion-weighted imaging. On contrast enhanced images, seven cases showed heterogeneous enhancement, three showed peripheral enhancementand and two showed homogeneous enhancement. Dural tail sign were detected in nine cases. CONCLUSION: CSBO is rare, and is commonly associated with previous radiation exposure. A presumptive diagnosis for osteosarcoma should be considered when calvarium and skull base tumours with osteoid matrix and duraltail sign are found. ADVANCES IN KNOWLEDGE: CT and MR features of CSBO have not been reported. The study helps to identify CSBO and other sarcomas.