A multitask deep learning model that provided classification of primary bone tumors on radiographs demonstrated performance that surpassed that of radiologic residents and was comparable with that ... Background An artificial intelligence model that assesses primary bone tumors on radiographs may assist in the diagnostic workflow. Purpose To develop a multitask deep learning (DL) model for simul...

Background: In patients with soft-tissue sarcomas, tumor grading constitutes a decisive factor to determine the best treatment decision. Tumor grading is obtained by pathological work-up after focal biopsies. Deep learning (DL)-based imaging analysis may pose an alternative way to characterize STS tissue. In this work, we sought to non-invasively differentiate tumor grading into low-grade (G1) and high-grade (G2/G3) STS using DL techniques based on MR-imaging. Methods: Contrast-enhanced T1-weighted fat-saturated (T1FSGd) MRI sequences and fat-saturated T2-weighted (T2FS) sequences were collected from two independent retrospective cohorts (training: 148 patients, testing: 158 patients). Tumor grading was determined following the French Federation of Cancer Centers Sarcoma Group in pre-therapeutic biopsies. DL models were developed using transfer learning based on the DenseNet 161 architecture. Results: The T1FSGd and T2FS-based DL models achieved area under the receiver operator characteristic curve (AUC) values of 0.75 and 0.76 on the test cohort, respectively. T1FSGd achieved the best F1-score of all models (0.90). The T2FS-based DL model was able to significantly risk-stratify for overall survival. Attention maps revealed relevant features within the tumor volume and in border regions. Conclusions: MRI-based DL models are capable of predicting tumor grading with good reproducibility in external validation.

To evaluate the diagnostic value of MR imaging for the differentiation of lipomas and atypical lipomatous tumors (ALT) in comparison with histology and MDM2 amplification status. Patients with well-differentiated lipomatous tumors (n = 113), of which 66 were diagnosed as lipoma (mean age 53 years (range, 13–82); 47% women) and 47 as atypical lipomatous tumor (ALT; mean age 60 years (range, 28–88); 64% women), were included into this study using histology and MDM2 amplification status by fluorescence in situ hybridization (FISH) as standard of reference. Preoperative MR images were retrospectively assessed by two radiologists for the following imaging features: maximum tumor diameter (mm) as well as the affected compartment (intramuscular, intermuscular or subcutaneous), septa (absent, thin (< 2 mm) or thick septa (> 2 mm) with nodular components); contrast enhancing areas within the lipomatous tumor (< 1/3 of the tumor volume, > 1/3 of the tumor volume); Of the 47 patients with ALT, 40 (85.1%) presented thick septa (> 2 mm) and this finding significantly increased the likelihood of ALT (OR 6.24, 95% CI 3.36–11.59; P < 0.001). The likelihood of ALT was increased if the tumor exceeded a maximum diameter of 130.0 mm (OR 2.74, 95% CI 1.82–4.11, P < 0.001). The presence of contrast enhancement in lipomatous tumors significantly increased the likelihood of ALT (Odds ratio (OR) 2.95, 95% confidence interval (CI) 2.01–4.31; P < 0.001). Of the lipomas, 21.1% were located subcutaneously, 63.6% intramuscularly and 15.2% intermuscularly. On the other hand, none of the ALTs were located subcutaneously, the majority was located intermuscularly (87.3%) and a small number of ALTs was located intramuscularly (12.7%). Our results suggest that using specific morphological MR imaging characteristics (maximum tumor diameter, thick septa and contrast enhancement) and the information on the localization of the lipomatous tumor, a high sensitivity and substantial specificity can be achieved for the diagnosis of lipomas and ALTs.

Soft tissue sarcomas are rare entities with over 50 histological subtypes. Liposarcoma (LS) is the most common neoplasm in this group; it is a complex neoplasm that is divided into different histological subtypes. Different therapy options, such as surgical resection, radiation, and chemotherapy, are available. Depending on the subtype, location, status of the resection margins and metastatic status, different therapy options are used. Therefore, the aim of this study was to determine the prognostic factors influencing the survival of patients affected by LS with consideration for the grading, histological subtype, state of the resection margin, size, location, metastases and local recurrence in a retrospective, single-centre analysis over 15 years. We included 133 patients (male/female = 67/66) in this study. We recorded the histologic subtype, grade, TNM classification, localization, biopsy technique, tumour margins, number of operations, complications, radiation and dose, chemotherapy, survival, recrudescence, metastases and follow-up. Survivorship analysis was performed. We detected 56 (43%; 95%-CI 34.6–51.6%) atypical LS cases, 21 (16.2%; 95%-CI 9.8–22.5) dedifferentiated LS cases, 40 (30.8%; 95%-CI 22.8–38.7) myxoid LS cases and 12 (9.2%; 95%-CI 4.3–14.2) pleomorphic LS cases. G1 was the most common grade, which was followed by G3. Negative margins (R0) were detected in 67 cases (53.6%; 95%-CI 44.9–62.3) after surgical resection. Local recurrence was detected in 23.6% of cases. The presence of metastases and dedifferentiated LS subtype as well as negative margins, grade and tumour size are significant prognostic factors of the survival rates (p < 0.015). Grading, LS subtype, negative margins after surgery, metastases and tumour size are independently associated with disease-specific survival, and patients with local recurrence had lower survival rates. We hope our investigation may facilitate a further prospective study and clinical decision-making in LS.

Background: In patients with soft-tissue sarcomas of the extremities, the treatment decision is currently regularly based on tumor grading and size. The imaging-based analysis may pose an alternative way to stratify patients’ risk. In this work, we compared the value of MRI-based radiomics with expert-derived semantic imaging features for the prediction of overall survival (OS). Methods: Fat-saturated T2-weighted sequences (T2FS) and contrast-enhanced T1-weighted fat-saturated (T1FSGd) sequences were collected from two independent retrospective cohorts (training: 108 patients; testing: 71 patients). After preprocessing, 105 radiomic features were extracted. Semantic imaging features were determined by three independent radiologists. Three machine learning techniques (elastic net regression (ENR), least absolute shrinkage and selection operator, and random survival forest) were compared to predict OS. Results: ENR models achieved the best predictive performance. Histologies and clinical staging differed significantly between both cohorts. The semantic prognostic model achieved a predictive performance with a C-index of 0.58 within the test set. This was worse compared to a clinical staging system (C-index: 0.61) and the radiomic models (C-indices: T1FSGd: 0.64, T2FS: 0.63). Both radiomic models achieved significant patient stratification. Conclusions: T2FS and T1FSGd-based radiomic models outperformed semantic imaging features for prognostic assessment.

Even though tumors in children are rare, they cause the second most deaths under the age of 18 years. More often than in other age groups, underage patients suffer from malignancies of the bones, and these mostly occur in the area around the knee. One problem in the treatment is the early detection of bone tumors, especially on X-rays. The rarity and non-specific clinical symptoms further prolong the time to diagnosis. Nevertheless, an early diagnosis is crucial and can facilitate the treatment and therefore improve the prognosis of affected children. A new approach to evaluating X-ray images using artificial intelligence may facilitate the detection of suspicious lesions and, hence, accelerate the referral to a specialized center. We implemented a Vision Transformer model for image classification of healthy and pathological X-rays. To tackle the limited amount of data, we used a pretrained model and implemented extensive data augmentation. Discrete parameters were described by incidence and percentage ratio and continuous parameters by median, standard deviation and variance. For the evaluation of the model accuracy, sensitivity and specificity were computed. The two-entity classification of the healthy control group and the pathological group resulted in a cross-validated accuracy of 89.1%, a sensitivity of 82.2% and a specificity of 93.2% for test groups. Grad-CAMs were created to ensure the plausibility of the predictions. The proposed approach, using state-of-the-art deep learning methodology to detect bone tumors on knee X-rays of children has achieved very good results. With further improvement of the algorithm, enlargement of the dataset and removal of potential biases, this could become a useful additional tool, especially to support general practitioners for early, accurate and specific diagnosis of bone lesions in young patients.