Radiomics and machine learning for renal tumor subtype assessment using multiphase computed tomography in a multicenter setting.

OBJECTIVES: To distinguish histological subtypes of renal tumors using radiomic features and machine learning (ML) based on multiphase computed tomography (CT). MATERIAL AND METHODS: Patients who underwent surgical treatment for renal tumors at two tertiary centers from 2012 to 2022 were included retrospectively. Preoperative arterial (corticomedullary) and venous (nephrogenic) phase CT scans from these centers, as well as from external imaging facilities, were manually segmented, and standardized radiomic features were extracted. Following preprocessing and addressing the class imbalance, a ML algorithm based on extreme gradient boosting trees (XGB) was employed to predict renal tumor subtypes using 10-fold cross-validation. The evaluation was conducted using the multiclass area under the receiver operating characteristic curve (AUC). Algorithms were trained on data from one center and independently tested on data from the other center. RESULTS: The training cohort comprised n = 297 patients (64.3% clear cell renal cell cancer [RCC], 13.5% papillary renal cell carcinoma (pRCC), 7.4% chromophobe RCC, 9.4% oncocytomas, and 5.4% angiomyolipomas (AML)), and the testing cohort n = 121 patients (56.2%/16.5%/3.3%/21.5%/2.5%). The XGB algorithm demonstrated a diagnostic performance of AUC = 0.81/0.64/0.8 for venous/arterial/combined contrast phase CT in the training cohort, and AUC = 0.75/0.67/0.75 in the independent testing cohort. In pairwise comparisons, the lowest diagnostic accuracy was evident for the identification of oncocytomas (AUC = 0.57-0.69), and the highest for the identification of AMLs (AUC = 0.9-0.94) CONCLUSION: Radiomic feature analyses can distinguish renal tumor subtypes on routinely acquired CTs, with oncocytomas being the hardest subtype to identify. CLINICAL RELEVANCE STATEMENT: Radiomic feature analyses yield robust results for renal tumor assessment on routine CTs. Although radiologists routinely rely on arterial phase CT for renal tumor assessment and operative planning, radiomic features derived from arterial phase did not improve the accuracy of renal tumor subtype identification in our cohort.

Renal tumor segmentation, visualization, and segmentation confidence using ensembles of neural networks in patients undergoing surgical resection.

OBJECTIVES: To develop an automatic segmentation model for solid renal tumors on contrast-enhanced CTs and to visualize segmentation with associated confidence to promote clinical applicability. MATERIALS AND METHODS: The training dataset included solid renal tumor patients from two tertiary centers undergoing surgical resection and receiving CT in the corticomedullary or nephrogenic contrast media (CM) phase. Manual tumor segmentation was performed on all axial CT slices serving as reference standard for automatic segmentations. Independent testing was performed on the publicly available KiTS 2019 dataset. Ensembles of neural networks (ENN, DeepLabV3) were used for automatic renal tumor segmentation, and their performance was quantified with DICE score. ENN average foreground entropy measured segmentation confidence (binary: successful segmentation with DICE score > 0.8 versus inadequate segmentation ≤ 0.8). RESULTS: N = 639/n = 210 patients were included in the training and independent test dataset. Datasets were comparable regarding age and sex (p > 0.05), while renal tumors in the training dataset were larger and more frequently benign (p < 0.01). In the internal test dataset, the ENN model yielded a median DICE score = 0.84 (IQR: 0.62-0.97, corticomedullary) and 0.86 (IQR: 0.77-0.96, nephrogenic CM phase), and the segmentation confidence an AUC = 0.89 (sensitivity = 0.86; specificity = 0.77). In the independent test dataset, the ENN model achieved a median DICE score = 0.84 (IQR: 0.71-0.97, corticomedullary CM phase); and segmentation confidence an accuracy = 0.84 (sensitivity = 0.86 and specificity = 0.81). ENN segmentations were visualized with color-coded voxelwise tumor probabilities and thresholds superimposed on clinical CT images. CONCLUSIONS: ENN-based renal tumor segmentation robustly performs in external test data and might aid in renal tumor classification and treatment planning. CLINICAL RELEVANCE STATEMENT: Ensembles of neural networks (ENN) models could automatically segment renal tumors on routine CTs, enabling and standardizing downstream image analyses and treatment planning. Providing confidence measures and segmentation overlays on images can lower the threshold for clinical ENN implementation. KEY POINTS: Ensembles of neural networks (ENN) segmentation is visualized by color-coded voxelwise tumor probabilities and thresholds. ENN provided a high segmentation accuracy in internal testing and in an independent external test dataset. ENN models provide measures of segmentation confidence which can robustly discriminate between successful and inadequate segmentations.

[Staging and Diagnostics of Rectal Cancer]. / Modalitäten des Stagings und der Diagnostik bei Rektumkarzinom.

The review titled "Staging and Diagnostics of Rectal Cancer" aims to provide insight to imaging techniques in patients with rectal cancer.Rectal cancer is among the most common malignancies, with one of the highest mortality rates worldwide. Timely diagnosis and therapy of this cancer therefore has important socio-economic implications.Radiological imaging plays a major role in the planning of subsequent therapy. Modern tomographic imaging is used not only for initial diagnosis, but also for staging.The individual role of different imaging techniques in diagnosis of rectal cancer will be explained in detail, and their function in general. Furthermore, we will present relevant radiological research related.The increasing role of MRI-based local staging will be presented in detail in this review. Defined diagnostic criteria, based on common recommendations, will be explained. We will show how MRI-based local staging can support the initial diagnosis and follow-up examinations in collaboration with other medical specialties in therapeutic planning. In particular, we describe how MRI is capable of substantially influencing the determination of surgical procedures in rectal cancer.

[Management of Injuries to the Parenchymal Abdominal Organs]. / Management von Verletzungen parenchymatöser Abdominalorgane.

The most common organs affected by abdominal trauma are the spleen and the liver, often in combination. Pancreatic injuries are rare. In the case of blunt abdominal trauma, which is much more common, a clinical and laboratory examination as well as sonography should be performed. In the initial assessment, the circulatory situation must be screened. If there is haemodynamic instability and presentation of free fluid, an emergency laparotomy is indicated. If the situation is stable or stabilised and a pathological sonography is present, it is essential to perform triphasic contrast enhanced computed tomography, which is also mandatory in polytraumatised patients. If a renal injury is suspected, a late venous phase should be attached. In addition to the classification of the injury, attention should be paid to possible vascular injury or active bleeding. In this case, angiography with the possibility of intervention should be performed. Endoscopic treatment is possible for injuries of the pancreatic duct. If the imaging does not reveal any intervention target and a circulation is stable, a conservative approach is possible with continuous monitoring using clinical, laboratory and sonographic controls. Most injuries can be successfully treated by non-operative management (NOM).There are various surgical options for treating the injury, such as local and resecting procedures. There is also the option of "damage control surgery" with acute bleeding control and second look. Complex surgical procedures should be performed at centres. Postoperative complications arise out of elective surgery.In the less common case of penetrating abdominal trauma, the actual extent of the injury cannot be estimated from the visible wound. Here again, the circulatory situation determines the next steps. An emergency laparotomy should be carried out in case of instability. If the condition is stable, further diagnostics should be performed using contrast enhanced computed tomography. If penetration through the peritoneum cannot be clearly excluded, diagnostic laparoscopy should be performed.

Time to Deliver on Promises: The Role of ERBB2 Alterations as Treatment Options for Colorectal Cancer Patients in the Era of Precision Oncology.

Receptor tyrosine kinase erythroblastic oncogene B2 (ERBB2), also known as human epidermal growth factor receptor 2 (HER2), represents an oncogenic driver and has been effectively targeted in breast and gastric cancer. Recently, next-generation sequencing (NGS) discovered ERBB2 as a promising therapeutic target in metastatic colorectal cancer (mCRC), where it is altered in 3-5% of patients, but no therapies are currently approved for this use. Herein, we present the experience of a single center in diagnosing actionable genetic ERBB2 alterations using NGS and utilizing the latest therapeutic options. Between October 2019 and December 2022, a total of 107 patients with advanced CRC underwent molecular analysis, revealing actionable ERBB2 mutations in two patients and ERBB2 amplifications in two other patients. These findings correlated with immunohistochemical (IHC) staining. Of these four patients, two were treated with trastuzumab-deruxtecan (T-DXd). We present two exemplary cases of patients with actionable ERBB2 alterations to demonstrate the effectiveness of T-DXd in heavily pretreated ERBB2-positive mCRC patients and the need for early molecular profiling. To fully exploit the potential of this promising treatment, earlier molecular profiling and the initiation of targeted therapies are essential.

Visualization of deglutition and gastroesophageal reflux using real-time MRI: a standardized approach to image acquisition and assessment.

This study aims to develop a standardized algorithm for gastroesophageal image acquisition and diagnostic assessment using real-time MRI. Patients with GERD symptoms undergoing real-time MRI of the esophagus and esophagogastric junction between 2015 and 2018 were included. A 10 ml bolus of pineapple juice served as an oral contrast agent. Patients performed Valsalva maneuver to provoke reflux and hiatal hernia. Systematic MRI assessment included visual presence of achalasia, fundoplication failure in patients with previous surgical fundoplication, gastroesophageal reflux, and hiatal hernia. A total of 184 patients (n = 92 female [50%], mean age 52.7 ± 15.8 years) completed MRI studies without adverse events at a mean examination time of 15 min. Gastroesophageal reflux was evident in n = 117 (63.6%), hiatal hernia in n = 95 (52.5%), and achalasia in 4 patients (2.2%). Hiatal hernia was observed more frequently in patients with reflux at rest (n = 67 vs. n = 6, p < 0.01) and during Valsalva maneuver (n = 87 vs. n = 8, p < 0.01). Real-time MRI visualized a morphologic correlate for recurring GERD symptoms in 20/22 patients (90%) after fundoplication procedure. In a large-scale single-center cohort of patients with GERD symptoms undergoing real-time MRI, visual correlates for clinical symptoms were evident in most cases. The proposed assessment algorithm could aid in wider-spread utilization of real-time MRI and provides a comprehensive approach to this novel imaging modality.