Differentiation between adrenocortical carcinoma and lipid-poor adrenal adenoma using a multiparametric MRI-based diagnostic algorithm.

PURPOSE: The purpose of this study was to evaluate the capabilities of multiparametric magnetic resonance imaging (MRI) in differentiating between lipid-poor adrenal adenoma (LPAA) and adrenocortical carcinoma (ACC). MATERIALS AND METHODS: Patients of two centers who underwent surgical resection of LPAA or ACC after multiparametric MRI were retrospectively included. A training cohort was used to build a diagnostic algorithm obtained through recursive partitioning based on multiparametric MRI variables, including apparent diffusion coefficient and chemical shift signal ratio (i.e., tumor signal intensity index). The diagnostic performances of the multiparametric MRI-based algorithm were evaluated using a validation cohort, alone first and then in association with adrenal tumor size using a cut-off of 4 cm. Performances of the diagnostic algorithm for the diagnosis of ACC vs. LPAA were calculated using pathology as the reference standard. RESULTS: Fifty-four patients (27 with LPAA and 27 with ACC; 37 women; mean age, 48.5 ± 13.3 [standard deviation (SD)] years) were used as the training cohort and 61 patients (24 with LPAA and 37 with ACC; 47 women; mean age, 49 ± 11.7 [SD] years) were used as the validation cohort. In the validation cohort, the diagnostic algorithm yielded best accuracy for the diagnosis of ACC vs. LPAA (75%; 46/61; 95% CI: 55-88) when used without lesion size. Best sensitivity was obtained with the association of the diagnostic algorithm with tumor size (96%; 23/24; 95% CI: 80-99). Best specificity was obtained with the diagnostic algorithm used alone (76%; 28/37; 95% CI: 60-87). CONCLUSION: A multiparametric MRI-based diagnostic algorithm that includes apparent diffusion coefficient and tumor signal intensity index helps discriminate between ACC and LPAA with high degrees of specificity and accuracy. The association of the multiparametric MRI-based diagnostic algorithm with adrenal lesion size helps maximize the sensitivity of multiparametric MRI for the diagnosis of ACC.

Interventional Oncology: 2024 Update.

Interventional Oncology (IO) stands at the forefront of transformative cancer care, leveraging advanced imaging technologies and innovative interventions. This narrative review explores recent developments within IO, highlighting its potential impact facilitated by artificial intelligence (AI), personalized medicine and imaging innovations. The integration of AI in IO holds promise for accelerating tumour detection and characterization, guiding treatment strategies and refining predictive models. Imaging modalities, including functional MRI, PET and cone beam CT are reshaping imaging and precision. Navigation, fusion imaging, augmented reality and robotics have the potential to revolutionize procedural guidance and offer unparalleled accuracy. New developments are observed in embolization and ablative therapies. The pivotal role of genomics in treatment planning, targeted therapies and biomarkers for treatment response prediction underscore the personalization of IO. Quality of life assessment, minimizing side effects and long-term survivorship care emphasize patient-centred outcomes after IO treatment. The evolving landscape of IO training programs, simulation technologies and workforce competence ensures the field's adaptability. Despite barriers to adoption, synergy between interventional radiologists' proficiency and technological advancements hold promise in cancer care.

CT and MRI of Gastrointestinal Stromal Tumors: New Trends and Perspectives.

Gastrointestinal stromal tumors (GISTs) are defined as mesenchymal tumors of the gastrointestinal tract that express positivity for CD117, which is a c-KIT proto-oncogene antigen. Expression of the c-KIT protein, a tyrosine kinase growth factor receptor, allows the distinction between GISTs and other mesenchymal tumors such as leiomyoma, leiomyosarcoma, schwannoma and neurofibroma. GISTs can develop anywhere in the gastrointestinal tract, as well as in the mesentery and omentum. Over the years, the management of GISTs has improved due to a better knowledge of their behaviors and risk or recurrence, the identification of specific mutations and the use of targeted therapies. This has resulted in a better prognosis for patients with GISTs. In parallel, imaging of GISTs has been revolutionized by tremendous progress in the field of detection, characterization, survival prediction and monitoring during therapy. Recently, a particular attention has been given to radiomics for the characterization of GISTs using analysis of quantitative imaging features. In addition, radiomics has currently many applications that are developed in conjunction with artificial intelligence with the aim of better characterizing GISTs and providing a more precise assessment of tumor burden. This article sums up recent advances in computed tomography and magnetic resonance imaging of GISTs in the field of image/data acquisition, tumor detection, tumor characterization, treatment response evaluation, and preoperative planning.

CT and MRI of abdominal cancers: current trends and perspectives in the era of radiomics and artificial intelligence.

Abdominal cancers continue to pose daily challenges to clinicians, radiologists and researchers. These challenges are faced at each stage of abdominal cancer management, including early detection, accurate characterization, precise assessment of tumor spread, preoperative planning when surgery is anticipated, prediction of tumor aggressiveness, response to therapy, and detection of recurrence. Technical advances in medical imaging, often in combination with imaging biomarkers, show great promise in addressing such challenges. Information extracted from imaging datasets owing to the application of radiomics can be used to further improve the diagnostic capabilities of imaging. However, the analysis of the huge amount of data provided by these advances is a difficult task in daily practice. Artificial intelligence has the potential to help radiologists in all these challenges. Notably, the applications of AI in the field of abdominal cancers are expanding and now include diverse approaches for cancer detection, diagnosis and classification, genomics and detection of genetic alterations, analysis of tumor microenvironment, identification of predictive biomarkers and follow-up. However, AI currently has some limitations that need further refinement for implementation in the clinical setting. This review article sums up recent advances in imaging of abdominal cancers in the field of image/data acquisition, tumor detection, tumor characterization, prognosis, and treatment response evaluation.

Imaging of hepato-pancreato-biliary emergencies in patients with cancer.

Hepato-pancreato-biliary (HPB) emergencies in patients with cancer encompass an extensive array of various conditions, including primary malignancies that require prompt treatment, associated severe complications, and life-threatening consequences arising from treatment. In patients with cancer, the liver can be affected by chemotherapy-induced hepatotoxicity, veno-occlusive disease, Budd-Chiari syndrome, liver hemorrhage, and other complications arising from cancer therapy with all these complications requiring timely diagnosis and prompt treament. Cholecystitis induced by systemic anticancer therapies can result in severe conquences if not promptly identified and treated. The application of immunotherapy in cancer therapy is associated with cholangitis. Hemobilia, often caused by medical interventions, may require arterial embolization in patients with severe bleeding and hemodynamic instability. Malignant biliary obstruction in patients with biliary cancers may necessitate palliative strategies such as biliary stenting. In pancreatic cancer, patients often miss surgical treatment due to advanced disease stages or distant metastases, leading to potential emergencies at different treatment phases. This comprehensive review underscores the complexities of diagnostic and treatment roles of medical imaging in managing HPB emergencies in patients with cancer. It illustrates the crucial role of imaging techniques, including magnetic resonance imaging, computed tomography and ultrasound, in diagnosing and managing these conditions for timely intervention. It provides essential insights into the critical nature of early diagnosis and intervention in cancer-related HPB emergencies, ultimately impacting patient outcomes and survival rates.

Three-dimensional CT cinematic rendering of adrenal masses: Role in tumor analysis and management.

The adrenal gland is home to an array of complex physiological and neoplastic disease processes. While dedicated adrenal computed tomography (CT) is the gold standard imaging modality for adrenal lesions, there exists significant overlap among imaging features of adrenal pathology. This can often make radiological diagnosis and subsequent determination of the optimal surgical approach challenging. Cinematic rendering (CR) is a novel CT post-processing technique that utilizes advanced light modeling to generate highly photorealistic anatomic visualization. This generates unique prospects in the evaluation of adrenal masses. As one of the first large tertiary care centers to incorporate CR into routine diagnostic workup, our preliminary experience with using CR has been positive, and we have found CR to be a valuable adjunct during surgical planning. Herein, we highlight the unique utility of CR techniques in the workup of adrenal lesions and provide commentary on the opportunities and obstacles associated with the application of this novel display method in this setting.

Comparison Between Radial and Femoral Artery Access for Transarterial Chemoembolisation in Patients With Hepatocellular Carcinoma.

PURPOSE: The purpose of this study was to compare the technical success rate, the selectivity of transarterial chemoembolisation (TACE), the complication rate, the radiation dose given to the patients and the hospitalization stay between TACE performed using femoral artery approach (FAA) and TACE performed using radial artery approach (RAA) in patients with hepatocellular carcinoma (HCC). METHODS: Between June 2020 and April 2022, 49 patients with HCC who underwent 116 TACEs (75 using FAA and 41 using RAA) were included. Differences in technical success rate, selectivity of micro-catheterization, radiation dose given to the patients, fluoroscopy time, hospitalization stay duration, and complication rate were compared between FAA and RAA using Fisher exact or Student t tests. RESULTS: No differences in technical success rates were found between RAA (93%; 39/41 TACEs) and FAA (100%; 75/75 TACEs) (P = .12). There were no differences between the two groups in terms of selectivity of catheterization, radiation dose, fluoroscopy time and hospitalization stay duration. Five patients had Grade 2 complications (hematoma) after FAA vs. one patient with one Grade 1 complication (radial artery occlusion) after RAA (5/75 [7%] vs. 1/41 [2%], respectively; P = .42). No major arterial access site complications occurred with FAA or RAA. CONCLUSIONS: This study confirms that RAA is a safe approach that does not compromise the technical efficacy and the selectivity of TACE compared to FAA in patients with HCC.

Treatment of adenomyosis, abdominal wall endometriosis and uterine leiomyoma with interventional radiology: A review of current evidences.

Interventional radiology shows promises in the field of women's health, particularly in pelvic interventions. This review article discusses the latest advancements in interventional radiology techniques for pelvic conditions affecting women including adenomyosis, abdominal wall endometriosis and uterine leiomyoma. Extraperitoneal endometriosis involving the abdominal wall may be treated by percutaneous thermal ablation, such as cryoablation, whereas uterine leiomyoma and adenomyosis can be managed either using percutaneous thermal ablation or using uterine artery embolization. Continued research and development in interventional radiology will further enhance the minimally-invasive interventions available for women's health, improving outcomes and quality of life for this large patient population of women.

Cinematic Rendering of Gastrointestinal Stromal Tumors: A Review of Current Possibilities and Future Developments.

Gastrointestinal stromal tumors (GISTs) are defined as CD117-positive primary, spindled or epithelioid, mesenchymal tumors of the gastrointestinal tract, omentum, or mesentery. While computed tomography (CT) is the recommended imaging modality for GISTs, overlap in imaging features between GISTs and other gastrointestinal tumors often make radiological diagnosis and subsequent selection of the optimal therapeutic approach challenging. Cinematic rendering is a novel CT post-processing technique that generates highly photorealistic anatomic images based on a unique lighting model. The global lighting model produces high degrees of surface detail and shadowing effects that generate depth in the final three-dimensional display. Early studies have shown that cinematic rendering produces high-quality images with enhanced detail by comparison with other three-dimensional visualization techniques. Cinematic rendering shows promise in improving the visualization of enhancement patterns and internal architecture of abdominal lesions, local tumor extension, and global disease burden, which may be helpful for lesion characterization and pretreatment planning. This article discusses and illustrates the application of cinematic rendering in the evaluation of GISTs and the unique benefit of using cinematic rendering in the workup of GIST with a specific emphasis on tumor characterization and preoperative planning.

A preoperative computed tomography radiomics model to predict disease-free survival in patients with pancreatic neuroendocrine tumors.

IMPORTANCE: Imaging has demonstrated capabilities in the diagnosis of pancreatic neuroendocrine tumors (pNETs), but its utility for prognostic prediction has not been elucidated yet. OBJECTIVE: The aim of this study was to build a radiomics model using preoperative computed tomography (CT) data that may help predict recurrence-free survival (RFS) or OS in patients with pNET. DESIGN: We performed a retrospective observational study in a cohort of French patients with pNETs. PARTICIPANTS: Patients with surgically resected pNET and available CT examinations were included. INTERVENTIONS: Radiomics features of preoperative CT data were extracted using 3D-Slicer® software with manual segmentation. Discriminant features were selected with penalized regression using least absolute shrinkage and selection operator method with training on the tumor Ki67 rate (≤2 or >2). Selected features were used to build a radiomics index ranging from 0 to 1. OUTCOME AND MEASURE: A receiving operator curve was built to select an optimal cutoff value of the radiomics index to predict patient RFS and OS. Recurrence-free survival and OS were assessed using Kaplan-Meier analysis. RESULTS: Thirty-seven patients (median age, 61 years; 20 men) with 37 pNETs (grade 1, 21/37 [57%]; grade 2, 12/37 [32%]; grade 3, 4/37 [11%]) were included. Patients with a radiomics index >0.4 had a shorter median RFS (36 months; range: 1-133) than those with a radiomics index ≤0.4 (84 months; range: 9-148; P = .013). No associations were found between the radiomics index and OS (P = .86).

Radiomic analysis of abdominal organs during sepsis of digestive origin in a French intensive care unit.

BACKGROUND: Sepsis is a severe and common cause of admission to the intensive care unit (ICU). Radiomic analysis (RA) may predict organ failure and patient outcomes. The objective of this study was to assess a model of RA and to evaluate its performance in predicting in-ICU mortality and acute kidney injury (AKI) during abdominal sepsis. METHODS: This single-center, retrospective study included patients admitted to the ICU for abdominal sepsis. To predict in-ICU mortality or AKI, elastic net regularized logistic regression and the random forest algorithm were used in a five-fold cross-validation set repeated 10 times. RESULTS: Fifty-five patients were included. In-ICU mortality was 25.5%, and 76.4% of patients developed AKI. To predict in-ICU mortality, elastic net and random forest models, respectively, achieved areas under the curve (AUCs) of 0.48 (95% confidence interval [CI], 0.43-0.54) and 0.51 (95% CI, 0.46-0.57) and were not improved combined with Simplified Acute Physiology Score (SAPS) II. To predict AKI with RA, the AUC was 0.71 (95% CI, 0.66-0.77) for elastic net and 0.69 (95% CI, 0.64-0.74) for random forest, and these were improved combined with SAPS II, respectively; AUC of 0.94 (95% CI, 0.91-0.96) and 0.75 (95% CI, 0.70-0.80) for elastic net and random forest, respectively. CONCLUSIONS: This study suggests that RA has poor predictive performance for in-ICU mortality but good predictive performance for AKI in patients with abdominal sepsis. A secondary validation cohort is needed to confirm these results and the assessed model.

Pretreatment CT Texture Parameters as Predictive Biomarkers of Progression-Free Survival in Follicular Lymphoma Treated with Immunochemotherapy and Rituximab Maintenance.

The purpose of this study was to determine whether texture analysis features present on pretreatment unenhanced computed tomography (CT) images, derived from 18F-fluorodeoxyglucose positron emission/computed tomography (18-FDG PET/CT), can predict progression-free survival (PFS), progression-free survival at 24 months (PFS 24), time to next treatment (TTNT), and overall survival in patients with high-tumor-burden follicular lymphoma treated with immunochemotherapy and rituximab maintenance. Seventy-two patients with follicular lymphoma were retrospectively included. Texture analysis was performed on unenhanced CT images extracted from 18-FDG PET/CT examinations that were obtained within one month before treatment. Skewness at a fine texture scale (SSF = 2) was an independent predictor of PFS (hazard ratio = 3.72 (95% CI: 1.15, 12.11), p = 0.028), PFS 24 (hazard ratio = 13.38; 95% CI: 1.29, 138.13; p = 0.029), and TTNT (hazard ratio = 5.11; 95% CI: 1.18, 22.13; p = 0.029). Skewness values above -0.015 at SSF = 2 were significantly associated with lower PFS, PFS 24, and TTNT. Kurtosis without filtration was an independent predictor of PFS (SSF = 0; HR = 1.22 (95% CI: 1.04, 1.44), p = 0.013), and TTNT (SSF = 0; hazard ratio = 1.23; 95% CI: 1.04, 1.46; p = 0.013). This study shows that pretreatment unenhanced CT texture analysis-derived tumor skewness and kurtosis may be used as predictive biomarkers of PFS and TTNT in patients with high-tumor-burden follicular lymphoma treated with immunochemotherapy and rituximab maintenance.