Stroma AReactive Invasion Front Areas (SARIFA)-A New Easily to Determine Biomarker in Colon Cancer-Results of a Retrospective Study.

Many studies have used histomorphological features to more precisely predict the prognosis of patients with colon cancer, focusing on tumor budding, poorly differentiated clusters, and the tumor-stroma ratio. Here, we introduce SARIFA: Stroma AReactive Invasion Front Area(s). We defined SARIFA as the direct contact between a tumor gland/tumor cell cluster (≥5 cells) and inconspicuous surrounding adipose tissue in the invasion front. In this retrospective, single-center study, we classified 449 adipose-infiltrative adenocarcinomas (not otherwise specified) from two groups based on SARIFA and found 25% of all tumors to be SARIFA-positive. Kappa values between the two pathologists were good/very good: 0.77 and 0.87. Patients with SARIFA-positive tumors had a significantly shorter colon-cancer-specific survival (p = 0.008, group A), absence of metastasis, and overall survival (p < 0.001, p = 0.003, group B). SARIFA was significantly associated with adverse features such as pT4 stage, lymph node metastasis, tumor budding, and higher tumor grade. Moreover, SARIFA was confirmed as an independent prognostic indicator for colon-cancer-specific survival (p = 0.011, group A). SARIFA assessment was very quick (<1 min). Because of low interobserver variability and good prognostic significance, SARIFA seems to be a promising histomorphological prognostic indicator in adipose-infiltrative adenocarcinomas of the colon. Further studies should validate our results and also determine whether SARIFA is a universal prognostic indicator in solid cancers.

Deep Learning Prediction of Metastasis in Locally Advanced Colon Cancer Using Binary Histologic Tumor Images.

In this study, we developed the Binary ImaGe Colon Metastasis classifier (BIg-CoMet), a semi-guided approach for the stratification of colon cancer patients into two risk groups for the occurrence of distant metastasis, using an InceptionResNetV2-based deep learning model trained on binary images. We enrolled 291 colon cancer patients with pT3 and pT4 adenocarcinomas and converted one cytokeratin-stained representative tumor section per case into a binary image. Image augmentation and dropout layers were incorporated to avoid overfitting. In a validation collective (n = 128), BIg-CoMet was able to discriminate well between patients with and without metastasis (AUC: 0.842, 95% CI: 0.774-0.911). Further, the Kaplan-Meier curves of the metastasis-free survival showed a highly significant worse clinical course for the high-risk group (log-rank test: p < 0.001), and we demonstrated superiority over other established risk factors. A multivariable Cox regression analysis adjusted for confounders supported the use of risk groups as a prognostic factor for the occurrence of metastasis (hazard ratio (HR): 5.4, 95% CI: 2.5-11.7, p < 0.001). BIg-CoMet achieved good performance for both UICC subgroups, especially for UICC III (n = 53), with a positive predictive value of 80%. Our study demonstrates the ability to stratify colon cancer patients via a semi-guided process on images that primarily reflect tumor architecture.

FDG PET Data is Associated with Cognitive Performance in Patients from a Memory Clinic.

BACKGROUND: Various reasons may lead to cognitive symptoms in elderly, including the development of cognitive decline and dementia. Often, mixed pathologies such as neurodegeneration and cerebrovascular disease co-exist in these patients. Diagnostic work-up commonly includes imaging modalities such as FDG PET, MRI, and CT, each delivering specific information. OBJECTIVE: To study the informative value of neuroimaging-based data supposed to reflect neurodegeneration (FDG PET), cerebral small vessel disease (MRI), and cerebral large vessel atherosclerosis (CT) with regard to cognitive performance in patients presenting to our memory clinic. METHODS: Non-parametric partial correlations and an ordinal logistic regression model were run to determine relationships between scores for cortical hypometabolism, white matter hyperintensities, calcified plaque burden, and results from Mini-Mental State Examination (MMSE). The final study group consisted of 162 patients (female: 94; MMSE: 6-30). RESULTS: Only FDG PET data was linked to and predicted cognitive performance (r(157) = -0.388, p < 0.001). Overall, parameters linked to cerebral small and large vessel disease showed no significant association with cognition. Further findings demonstrated a relationship between white matter hyperintensities and FDG PET data (r(157) = 0.230, p = 0.004). CONCLUSION: Only FDG PET imaging mirrors cognitive performance, presumably due to the examination's ability to reflect neurodegeneration and vascular dysfunction, thus capturing a broader spectrum of pathologies. This makes the examination a useful imaging-based diagnostic tool in the work-up of patients presenting to a memory clinic. Parameters of vascular dysfunction alone as depicted by conventional MRI and CT are less adequate in such a situation, most likely because they reflect one pathology complex only.

Clinical Routine FDG-PET Imaging of Suspected Progressive Supranuclear Palsy and Corticobasal Degeneration: A Gatekeeper for Subsequent Tau-PET Imaging?

Background: F-18-fluordeoxyglucose positron emission tomography (FDG-PET) is widely used for discriminative diagnosis of tau-positive atypical parkinsonian syndromes (T+APS). This approach now stands to be augmented with more specific tau tracers. Therefore, we retrospectively analyzed a large clinical routine dataset of FDG-PET images for evaluation of the strengths and limitations of stand-alone FDG-PET. Methods: A total of 117 patients (age 68.4 ± 11.1 y) underwent an FDG-PET exam. Patients were followed clinically for a minimum of one year and their final clinical diagnosis was recorded. FDG-PET was rated visually (positive/negative) and categorized as high, moderate or low likelihood of T+APS and other neurodegenerative disorders. We then calculated positive and negative predictive values (PPV/NPV) of FDG-PET readings for the different subgroups relative to their final clinical diagnosis. Results: Suspected diagnoses were confirmed by clinical follow-up (≥1 y) for 62 out of 117 (53%) patients. PPV was excellent when FDG-PET indicated a high likelihood of T+APS in combination with low to moderate likelihood of another neurodegenerative disorder. PPV was distinctly lower when FDG-PET indicated only a moderate likelihood of T+APS or when there was deemed equal likelihood of other neurodegenerative disorder. NPV of FDG-PET with a low likelihood for T+APS was high. Conclusions: FDG-PET has high value in clinical routine evaluation of suspected T+APS, gaining satisfactory differential diagnosis in two thirds of the patients. One third of patients would potentially profit from further evaluation by more specific radioligands, with FDG-PET serving gatekeeper function for the more expensive methods.

Additive value of amyloid-PET in routine cases of clinical dementia work-up after FDG-PET.

PURPOSE: In recent years, several [18F]-labeled amyloid-PET tracers have been developed and have obtained clinical approval. Despite their widespread scientific use, studies in routine clinical settings are limited. We therefore investigated the impact of [18F]-florbetaben (FBB)-PET on the diagnostic management of patients with suspected dementia that was still unclarified after [18F]-fluordeoxyglucose (FDG)-PET. METHODS: All subjects were referred in-house with a suspected dementia syndrome due to neurodegenerative disease. After undergoing an FDG-PET exam, the cases were discussed by the interdisciplinary dementia board, where the most likely diagnosis as well as potential differential diagnoses were documented. Because of persistent diagnostic uncertainty, the patients received an additional FBB-PET exam. Results were interpreted visually and classified as amyloid-positive or amyloid-negative, and we then compared the individual clinical diagnoses before and after additional FBB-PET. RESULTS: A total of 107 patients (mean age 69.4 ± 9.7y) were included in the study. The FBB-PET was rated as amyloid-positive in 65/107. In 83% of the formerly unclear cases, a final diagnosis was reached through FBB-PET, and the most likely prior diagnosis was changed in 28% of cases. The highest impact was observed for distinguishing Alzheimer's dementia (AD) from fronto-temporal dementia (FTLD), where FBB-PET altered the most likely diagnosis in 41% of cases. CONCLUSIONS: FBB-PET has a high additive value in establishing a final diagnosis in suspected dementia cases when prior investigations such as FDG-PET are inconclusive. The differentiation between AD and FTLD was particularly facilitated by amyloid-PET, predicting a considerable impact on patient management, especially in the light of upcoming disease-modifying therapies.