Artificial intelligence-based radiomics in bone tumors: Technical advances and clinical application

Diffusion Tensor and Kurtosis MRI-Based Radiomics Analysis of Kidney Injury in Type 2 Diabetes

BACKGROUND

Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) can provide quantitative parameters that show promise for evaluation of diabetic kidney disease (DKD). The combination of radiomics with DTI and DKI may hold potential clinical value in detecting DKD.

PURPOSE

To investigate radiomics models of DKI and DTI for predicting DKD in type 2 diabetes mellitus (T2DM) and evaluate their performance in automated renal parenchyma segmentation.

STUDY TYPE

Prospective.

POPULATION

One hundred and sixty-three T2DM patients (87 DKD; 63 females; 27-80 years), randomly divided into training cohort (N = 114) and validation cohort (N = 49).

FIELD STRENGTH/SEQUENCE

1.5-T, diffusion spectrum imaging (DSI) with 9 different b-values.

ASSESSMENT

The images of DSI were processed to generate DKI and DTI parameter maps, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). The Swin UNETR model was trained with 5-fold cross-validation using 100 samples for renal parenchyma segmentation. Subsequently, radiomics features were automatically extracted from each parameter map. The performance of the radiomics models on the validation cohort was evaluated by utilizing the receiver operating characteristic (ROC) curve.

STATISTICAL TESTS

Mann-Whitney U test, Chi-squared test, Pearson correlation coefficient, least absolute shrinkage and selection operator (LASSO), dice similarity coefficient (DSC), decision curve analysis (DCA), area under the curve (AUC), and DeLong's test. The threshold for statistical significance was set at P < 0.05.

RESULTS

The DKI_MD achieved the best segmentation performance (DSC, 0.925 ± 0.011). A combined radiomics model (DTI_FA, DTI_MD, DKI_FA, DKI_MD, and DKI_RD) showed the best performance (AUC, 0.918; 95% confidence interval [CI]: 0.820-0.991). When the threshold probability was greater than 20%, the combined model provided the greatest net benefit. Among the single parameter maps, the DTI_FA exhibited superior diagnostic performance (AUC, 887; 95% CI: 0.779-0.972).

DATA CONCLUSION

The radiomics signature constructed based on DKI and DTI may be used as an accurate and non-invasive tool to identify T2DM and DKD.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 2.

Comparison of Different Fusion Radiomics for Predicting Benign and Malignant Sacral Tumors: A Pilot Study

Differentiating between benign and malignant sacral tumors is crucial for determining appropriate treatment options. This study aims to develop two benchmark fusion models and a deep learning radiomic nomogram (DLRN) capable of distinguishing between benign and malignant sacral tumors using multiple imaging modalities. We reviewed axial T2-weighted imaging (T2WI) and non-contrast computed tomography (NCCT) of 134 patients pathologically confirmed as sacral tumors. The two benchmark fusion models were developed using fusion deep learning (DL) features and fusion classical machine learning (CML) features from multiple imaging modalities, employing logistic regression, K-nearest neighbor classification, and extremely randomized trees. The two benchmark models exhibiting the most robust predictive performance were merged with clinical data to formulate the DLRN. Performance assessment involved computing the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, negative predictive value (NPV), and positive predictive value (PPV). The DL benchmark fusion model demonstrated superior performance compared to the CML fusion model. The DLRN, identified as the optimal model, exhibited the highest predictive performance, achieving an accuracy of 0.889 and an AUC of 0.961 in the test sets. Calibration curves were utilized to evaluate the predictive capability of the models, and decision curve analysis (DCA) was conducted to assess the clinical net benefit of the DLR model. The DLRN could serve as a practical predictive tool, capable of distinguishing between benign and malignant sacral tumors, offering valuable information for risk counseling, and aiding in clinical treatment decisions.

Intratumoral Heterogeneity Assessment of the Extracellular Bone Matrix and Immune Microenvironment in Osteosarcoma Using Digital Imaging to Predict Therapeutic Response

The assessment of chemotherapy response in osteosarcoma (OS) based on the average percentage of viable cells is limited, as it overlooks the spatial heterogeneity of tumor cell response (foci of resistant cells), immune microenvironment, and bone microarchitecture. Despite the resulting positive classification for response to chemotherapy, some patients experience early metastatic recurrence, demonstrating that our conventional tools for evaluating treatment response are insufficient. We studied the interactions between tumor cells, immune cells (lymphocytes, histiocytes, and osteoclasts), and bone extracellular matrix (ECM) in 18 surgical resection samples of OS using multiplex and conventional immunohistochemistry (IHC: CD8, CD163, CD68, and SATB2), combined with multiscale characterization approaches in territories of good and poor response (GRT/PRT) to treatment. GRT and PRT were defined as subregions with <10% and ≥10% of viable tumor cells, respectively. Local correlations between bone ECM porosity and density of immune cells were assessed in these territories. Immune cell density was then correlated to overall patient survival. Two patterns were identified for histiocytes and osteoclasts. In poor responder patients, CD68 osteoclast density exceeded that of CD163 histiocytes but was not related to bone ECM load. Conversely, in good responder patients, CD163 histiocytes were more numerous than CD68 osteoclasts. For both of them, a significant negative local correlation with bone ECM porosity was found (P < .01). Moreover, in PRT, multinucleated osteoclasts were rounded and intermingled with tumor cells, whereas in GRT, they were elongated and found in close contact with bone trabeculae. CD8 levels were always low in metastatic patients, and those initially considered good responders rapidly died from their disease. The specific recruitment of histiocytes and osteoclasts within the bone ECM, and the level of CD8 represent new features of OS response to treatment. The associated prognostic signatures should be integrated into the therapeutic stratification algorithm of patients after surgery.

Potential biomarkers for immune monitoring after renal transplantation

Renal transplantation represents the foremost efficacious approach for ameliorating end-stage renal disease. Despite the current state of advanced renal transplantation techniques and the established postoperative immunosuppression strategy, a subset of patients continues to experience immune rejection during both the early and late postoperative phases, ultimately leading to graft loss. Consequently, the identification of immunobiomarkers capable of predicting the onset of immune rejection becomes imperative in order to facilitate early intervention strategies and enhance long-term prognoses. Upon reviewing the pertinent literature, we identified several indicators that could potentially serve as immune biomarkers to varying extents. These include the T1/T2 ratio, Treg/Th17 ratio, IL-10/TNF-α ratio, IL-33, IL-34, IL-6, IL-4, other cytokines, and NOX2/4.

Fusion Radiomics-Based Prediction of Response to Neoadjuvant Chemotherapy for Osteosarcoma

RATIONALE AND OBJECTIVES

Neoadjuvant chemotherapy (NAC) is the most crucial prognostic factor for osteosarcoma (OS), it significantly prolongs progression-free survival and improves the quality of life. This study aims to develop a deep learning radiomics (DLR) model to accurately predict the response to NAC in patients diagnosed with OS using preoperative MR images.

METHODS

We reviewed axial T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted (T1CE) of 106 patients pathologically confirmed as OS. First, the Auto3DSeg framework was utilized for automated OS segmentation. Second, using three feature extraction methods, nine risk classification models were constructed based on three classifiers. The area under the receiver operating curve (AUC), sensitivity, specificity, accuracy, negative predictive value and positive predictive value were calculated for performance evaluation. Additionally, we developed a deep learning radiomics nomogram with clinical indicators.

RESULTS

The model for OS automatic segmentation achieved a Dice coefficient of 0.868 across datasets. To predict the response to NAC, the DLR model achieved the highest prediction performance with an accuracy of 93.8% and an AUC of 0.961 in the test sets. We used calibration curves to assess the predictive ability of the models and performed decision curve analysis to evaluate the clinical net benefit of the DLR model.

CONCLUSION

The DLR model can serve as a pragmatic prediction tool, capable of identifying patients with poor response to NAC, providing information for risk counseling, and assisting in making clinical treatment decisions. Poor responders are better advised to undergo immunotherapy and receive the best supportive care.

An integrated model incorporating deep learning, hand-crafted radiomics and clinical and US features to diagnose central lymph node metastasis in patients with papillary thyroid cancer

OBJECTIVE

To evaluate the value of an integrated model incorporating deep learning (DL), hand-crafted radiomics and clinical and US imaging features for diagnosing central lymph node metastasis (CLNM) in patients with papillary thyroid cancer (PTC).

METHODS

This retrospective study reviewed 613 patients with clinicopathologically confirmed PTC from two institutions. The DL model and hand-crafted radiomics model were developed using primary lesion images and then integrated with clinical and US features selected by multivariate analysis to generate an integrated model. The performance was compared with junior and senior radiologists on the independent test set. SHapley Additive exPlanations (SHAP) plot and Gradient-weighted Class Activation Mapping (Grad-CAM) were used for the visualized explanation of the model.

RESULTS

The integrated model yielded the best performance with an AUC of 0.841. surpassing that of the hand-crafted radiomics model (0.706, p < 0.001) and the DL model (0.819, p = 0.26). Compared to junior and senior radiologists, the integrated model reduced the missed CLNM rate from 57.89% and 44.74-27.63%, and decreased the rate of unnecessary central lymph node dissection (CLND) from 29.87% and 27.27-18.18%, respectively. SHAP analysis revealed that the DL features played a primary role in the diagnosis of CLNM, while clinical and US features (such as extrathyroidal extension, tumour size, age, gender, and multifocality) provided additional support. Grad-CAM indicated that the model exhibited a stronger focus on thyroid capsule in patients with CLNM.

CONCLUSION

Integrated model can effectively decrease the incidence of missed CLNM and unnecessary CLND. The application of the integrated model can help improve the acceptance of AI-assisted US diagnosis among radiologists.

Artificial Intelligence in Bone Metastasis Imaging: Recent Progresses from Diagnosis to Treatment - A Narrative Review

The introduction of artificial intelligence (AI) represents an actual revolution in the radiological field, including bone lesion imaging. Bone lesions are often detected both in healthy and oncological patients and the differential diagnosis can be challenging but decisive, because it affects the diagnostic and therapeutic process, especially in case of metastases. Several studies have already demonstrated how the integration of AI-based tools in the current clinical workflow could bring benefits to patients and to healthcare workers. AI technologies could help radiologists in early bone metastases detection, increasing the diagnostic accuracy and reducing the overdiagnosis and the number of unnecessary deeper investigations. In addition, radiomics and radiogenomics approaches could go beyond the qualitative features, visible to the human eyes, extrapolating cancer genomic and behavior information from imaging, in order to plan a targeted and personalized treatment. In this article, we want to provide a comprehensive summary of the most promising AI applications in bone metastasis imaging and their role from diagnosis to treatment and prognosis, including the analysis of future challenges and new perspectives.