[SARIFA-a new multi-entity biomarker]. / SARIFA ­ ein neuer entitätenübergreifender Biomarker.

A stroma a­reactive invasion front area (SARIFA) is a new prognostic biomarker in carcinomas. Essentially, SARIFA describes the occurrence of direct contact between at least five tumor cells and adipocytes. This phenomenon is extremely easy and quick to identify, shows an extremely low interobserver variability, and does not require any additional staining as it can be identified on standard HE sections. The prognostic efficiency has now been demonstrated in gastric, colorectal, pancreatic, and prostate carcinoma.

C-X-C Motif Chemokine Receptor 4-Directed Scintigraphy Using [99mTc]Tc-Pentixatec in Primary Aldosteronism: A Proof-of-Concept Study.

C-X-C motif chemokine receptor 4 (CXCR4)-directed imaging has gained clinical interest in aiding clinical diagnostics in primary aldosteronism (PA). We retrospectively evaluated the feasibility of CXCR4-directed scintigraphy using the novel CXCR-4 ligand [99mTc]Tc-pentixatec in patients with PA. Methods: Six patients (mean age ± SD, 49 ± 15 y) underwent CXCR4-directed scintigraphy (including planar imaging and SPECT/CT) 30, 120, and 240 min after injection of 435 ± 50 MBq of [99mTc]Tc-pentixatec. Adrenal CXCR4 expression was analyzed by calculating lesion-to-contralateral ratios (LCRs). Imaging results were correlated to clinical information. Histopathology and clinical follow-up served as the standard of reference. Results: Three subjects showed lateralization of adrenal tracer accumulation, with a mean maximum lesion-to-contralateral ratio of 1.65 (range, 1.52-1.70), which correlated with morphologic findings on CT. One individual underwent adrenalectomy and presented with complete biochemical and clinical remission at follow-up. Histopathologic workup confirmed unilateral aldosterone-producing adenoma. Conclusion: [99mTc]Tc-pentixatec scintigraphy with SPECT in patients with PA is feasible and might offer a valuable alternative to CXCR4-directed imaging with [68Ga]Ga-pentixafor PET.

New PET Tracers for Lymphoma.

While functional imaging with [18F]Fluoro-deoxy-glucose positron emission tomography (PET)/computed tomography is a well-established imaging modality in most lymphoma entities, novel tracers addressing cell surface receptors, tumor biology, and the microenvironment are being developed. Especially, with the emergence of immuno-PET targeting surface markers of lymphoma cells, a new imaging modality of immunotherapies is evolving, which might especially aid in relapsed and refractory disease stages. This review highlights different new PET tracers in indolent and aggressive lymphoma subtypes and summarizes the current state of immuno-PET imaging in lymphoma.

Artificial Intelligence in Colorectal Cancer: From Patient Screening over Tailoring Treatment Decisions to Identification of Novel Biomarkers.

BACKGROUND: Artificial intelligence (AI) is increasingly entering and transforming not only medical research but also clinical practice. In the last 10 years, new AI methods have enabled computers to perform visual tasks, reaching high performance and thereby potentially supporting and even outperforming human experts. This is in particular relevant for colorectal cancer (CRC), which is the 3rd most common cancer type in general, as along the CRC patient journey many complex visual tasks need to be performed: from endoscopy over imaging to histopathology; the screening, diagnosis, and treatment of CRC involve visual image analysis tasks. SUMMARY: In all these clinical areas, AI models have shown promising results by supporting physicians, improving accuracy, and providing new biological insights and biomarkers. By predicting prognostic and predictive biomarkers from routine images/slides, AI models could lead to an improved patient stratification for precision oncology approaches in the near future. Moreover, it is conceivable that AI models, in particular together with innovative techniques such as single-cell or spatial profiling, could help identify novel clinically as well as biologically meaningful biomarkers that could pave the way to new therapeutic approaches. KEY MESSAGES: Here, we give a comprehensive overview of AI in colorectal cancer, describing and discussing these developments as well as the next steps which need to be taken to incorporate AI methods more broadly into the clinical care of CRC.

SARIFA as a new histopathological biomarker is associated with adverse clinicopathological characteristics, tumor-promoting fatty-acid metabolism, and might predict a metastatic pattern in pT3a prostate cancer.

BACKGROUND: Recently, we introduced Stroma-AReactive-Invasion-Front-Areas (SARIFA) as a novel hematoxylin-eosin (H&E)-based histopathologic prognostic biomarker for various gastrointestinal cancers, closely related to lipid metabolism. To date, no studies on SARIFA, which is defined as direct tumor-adipocyte-interaction, beyond the alimentary tract exist. Hence, the objective of our current investigation was to study the significance of SARIFA in pT3a prostate cancer (PCa) and explore its association with lipid metabolism in PCa as lipid metabolism plays a key role in PCa development and progression. METHODS: To this end, we evaluated SARIFA-status in 301 radical prostatectomy specimens and examined the relationship between SARIFA-status, clinicopathological characteristics, overall survival, and immunohistochemical expression of FABP4 and CD36 (proteins closely involved in fatty-acid metabolism). Additionally, we investigated the correlation between SARIFA and biochemical recurrence-free survival (BRFS) and PSMA-positive recurrences in PET/CT imaging in a patient subgroup. Moreover, a quantitative SARIFA cut-off was established to further understand the underlying tumor biology. RESULTS: SARIFA positivity occurred in 59.1% (n = 178) of pT3a PCas. Our analysis demonstrated that SARIFA positivity is strongly associated with established high-risk features, such as R1 status, extraprostatic extension, and higher initial PSA values. Additionally, we observed an upregulation of immunohistochemical CD36 expression specifically at SARIFAs (p = 0.00014). Kaplan-Meier analyses revealed a trend toward poorer outcomes, particularly in terms of BRFS (p = 0.1). More extensive tumor-adipocyte interaction, assessed as quantity-dependent SARIFA-status on H&E slides, is also significantly associated with high-risk features, such as lymph node metastasis, and seems to be associated with worse survival outcomes (p = 0.16). Moreover, SARIFA positivity appeared to be linked to more distant lymph node and bone metastasis, although statistical significance was slightly not achieved (both p > 0.05). CONCLUSIONS: This is the first study to introduce SARIFA as easy-and-fast-to-assess H&E-based biomarker in locally advanced PCa. SARIFA as the histopathologic correlate of a distinct tumor biology, closely related to lipid metabolism, could pave the way to a more detailed patient stratification and to the development of novel drugs targeting lipid metabolism in pT3a PCa. On the basis of this biomarker discovery study, further research efforts on the prognostic and predictive role of SARIFA in PCa can be designed.

First Safety and Efficacy Data with the Radiohybrid 177Lu-rhPSMA-10.1 for the Treatment of Metastatic Prostate Cancer.

We recently published the first dosimetry data, to our knowledge, for the radioligand therapy agent 177Lu-rhPSMA-10.1, providing an intrapatient comparison with 177Lu-PSMA-I&T in patients with metastatic prostate cancer. Here, we report efficacy and safety findings from these patients. Methods: Four consecutive patients with prostate-specific membrane antigen (PSMA)-positive metastatic prostate cancer received up to 6 cycles of 177Lu-rhPSMA-10.1 (7.4-7.7 GBq per cycle). Efficacy (prostate-specific antigen response according to Prostate Cancer Working Group 3 criteria and the Response Evaluation Criteria in PSMA PET/CT), progression-free survival, and overall survival were evaluated. Adverse events were recorded from the first dose until 16-24 mo after treatment. Results: The patients received a total activity of 29.6-59.4 GBq (4-6 cycles). Prostate-specific antigen was reduced by 100%, 99%, 88%, and 35%. Progression-free survival was not reached for 2 patients at 24 and 18 mo of follow-up and was 15 and 12 mo for the other 2 patients. One patient had a sustained complete response with 2 y of follow up. All patients were alive at the last time point of data collection. No serious adverse events were reported. Conclusion: 177Lu-rhPSMA-10.1 demonstrated encouraging preliminary efficacy and was well tolerated. Formal clinical trials are now under way to evaluate its potential prospectively (NCT05413850).

Radiomics Features of the Spleen as Surrogates for CT-Based Lymphoma Diagnosis and Subtype Differentiation.

The spleen is often involved in malignant lymphoma, which manifests on CT as either splenomegaly or focal, hypodense lymphoma lesions. This study aimed to investigate the diagnostic value of radiomics features of the spleen in classifying malignant lymphoma against non-lymphoma as well as the determination of malignant lymphoma subtypes in the case of disease presence-in particular Hodgkin lymphoma (HL), diffuse large B-cell lymphoma (DLBCL), mantle-cell lymphoma (MCL), and follicular lymphoma (FL). Spleen segmentations of 326 patients (139 female, median age 54.1 +/- 18.7 years) were generated and 1317 radiomics features per patient were extracted. For subtype classification, we created four different binary differentiation tasks and addressed them with a Random Forest classifier using 10-fold cross-validation. To detect the most relevant features, permutation importance was analyzed. Classifier results using all features were: malignant lymphoma vs. non-lymphoma AUC = 0.86 (p < 0.01); HL vs. NHL AUC = 0.75 (p < 0.01); DLBCL vs. other NHL AUC = 0.65 (p < 0.01); MCL vs. FL AUC = 0.67 (p < 0.01). Classifying malignant lymphoma vs. non-lymphoma was also possible using only shape features AUC = 0.77 (p < 0.01), with the most important feature being sphericity. Based on only shape features, a significant AUC could be achieved for all tasks, however, best results were achieved combining shape and textural features. This study demonstrates the value of splenic imaging and radiomic analysis in the diagnostic process in malignant lymphoma detection and subtype classification.

Quantitative Imaging Biomarkers of the Whole Liver Tumor Burden Improve Survival Prediction in Metastatic Pancreatic Cancer.

Finding prognostic biomarkers with high accuracy in patients with pancreatic cancer (PC) remains a challenging problem. To improve the prediction of survival and to investigate the relevance of quantitative imaging biomarkers (QIB) we combined QIB with established clinical parameters. In this retrospective study a total of 75 patients with metastatic PC and liver metastases were analyzed. Segmentations of whole liver tumor burden (WLTB) from baseline contrast-enhanced CT images were used to derive QIBs. The benefits of QIBs in multivariable Cox models were analyzed in comparison with two clinical prognostic models from the literature. To discriminate survival, the two clinical models had concordance indices of 0.61 and 0.62 in a statistical setting. Combined clinical and imaging-based models achieved concordance indices of 0.74 and 0.70 with WLTB volume, tumor burden score (TBS), and bilobar disease being the three WLTB parameters that were kept by backward elimination. These combined clinical and imaging-based models have significantly higher predictive performance in discriminating survival than the underlying clinical models alone (p < 0.003). Radiomics and geometric WLTB analysis of patients with metastatic PC with liver metastases enhances the modeling of survival compared with models based on clinical parameters alone.