Histology-Based Radiomics for [18F]FDG PET Identifies Tissue Heterogeneity in Pancreatic Cancer.

Radiomics features can reveal hidden patterns in a tumor but usually lack an underlying biologic rationale. In this work, we aimed to investigate whether there is a correlation between radiomics features extracted from [18F]FDG PET images and histologic expression patterns of a glycolytic marker, monocarboxylate transporter-4 (MCT4), in pancreatic cancer. Methods: A cohort of pancreatic ductal adenocarcinoma patients (n = 29) for whom both tumor cross sections and [18F]FDG PET/CT scans were available was used to develop an [18F]FDG PET radiomics signature. By using immunohistochemistry for MCT4, we computed density maps of MCT4 expression and extracted pathomics features. Cluster analysis identified 2 subgroups with distinct MCT4 expression patterns. From corresponding [18F]FDG PET scans, radiomics features that associate with the predefined MCT4 subgroups were identified. Results: Complex heat map visualization showed that the MCT4-high/heterogeneous subgroup was correlating with a higher MCT4 expression level and local variation. This pattern linked to a specific [18F]FDG PET signature, characterized by a higher SUVmean and SUVmax and second-order radiomics features, correlating with local variation. This MCT4-based [18F]FDG PET signature of 7 radiomics features demonstrated prognostic value in an independent cohort of pancreatic cancer patients (n = 71) and identified patients with worse survival. Conclusion: Our cross-modal pipeline allows the development of PET scan signatures based on immunohistochemical analysis of markers of a particular biologic feature, here demonstrated on pancreatic cancer using intratumoral MCT4 expression levels to select [18F]FDG PET radiomics features. This study demonstrated the potential of radiomics scores to noninvasively capture intratumoral marker heterogeneity and identify a subset of pancreatic ductal adenocarcinoma patients with a poor prognosis.

Detection and subtyping of basal cell carcinoma in whole-slide histopathology using weakly-supervised learning.

The frequency of basal cell carcinoma (BCC) cases is putting an increasing strain on dermatopathologists. BCC is the most common type of skin cancer, and its incidence is increasing rapidly worldwide. AI can play a significant role in reducing the time and effort required for BCC diagnostics and thus improve the overall efficiency of the process. To train such an AI system in a fully-supervised fashion however, would require a large amount of pixel-level annotation by already strained dermatopathologists. Therefore, in this study, our primary objective was to develop a weakly-supervised for the identification of basal cell carcinoma (BCC) and the stratification of BCC into low-risk and high-risk categories within histopathology whole-slide images (WSI). We compared Clustering-constrained Attention Multiple instance learning (CLAM) with StreamingCLAM and hypothesized that the latter would be the superior approach. A total of 5147 images were used to train and validate the models, which were subsequently tested on an internal set of 949 images and an external set of 183 images. The labels for training were automatically extracted from free-text pathology reports using a rule-based approach. All data has been made available through the COBRA dataset. The results showed that both the CLAM and StreamingCLAM models achieved high performance for the detection of BCC, with an area under the ROC curve (AUC) of 0.994 and 0.997, respectively, on the internal test set and 0.983 and 0.993 on the external dataset. Furthermore, the models performed well on risk stratification, with AUC values of 0.912 and 0.931, respectively, on the internal set, and 0.851 and 0.883 on the external set. In every single metric the StreamingCLAM model outperformed the CLAM model or is on par. The performance of both models was comparable to that of two pathologists who scored 240 BCC positive slides. Additionally, in the public test set, StreamingCLAM demonstrated a comparable AUC of 0.958, markedly superior to CLAM's 0.803. This difference was statistically significant and emphasized the strength and better adaptability of the StreamingCLAM approach.

Fibroblast Activation Protein-Targeted Photodynamic Therapy of Cancer-Associated Fibroblasts in Murine Models for Pancreatic Ductal Adenocarcinoma.

Patients with pancreatic ductal adenocarcinoma (PDAC) have a dismal 5 year survival of 9%. One important limiting factor for treatment efficacy is the dense tumor-supporting stroma. The cancer-associated fibroblasts in this stroma deposit excessive amounts of extracellular matrix components and anti-inflammatory mediators, which hampers the efficacy of chemo- and immunotherapies. Systemic depletion of all activated fibroblasts is, however, not feasible nor desirable and therefore a local approach should be pursued. Here, we provide a proof-of-principle of using fibroblast activation protein (FAP)-targeted photodynamic therapy (tPDT) to treat PDAC. FAP-targeting antibody 28H1 and irrelevant control antibody DP47GS were conjugated to the photosensitizer IRDye700DX (700DX) and the chelator diethylenetriaminepentaacetic acid. In vitro binding and cytotoxicity were evaluated using the fibroblast cell-line NIH-3T3 stably transfected with FAP. Biodistribution of 111In-labeled antibody-700DX constructs was determined in mice carrying syngeneic tumors of the murine PDAC cell line PDAC299, and in a genetically engineered PDAC mouse model (CKP). Then, tPDT was performed by exposing the subcutaneous or the spontaneous PDAC tumors to 690 nm light. Induction of apoptosis after treatment was assessed using automated analyses of immunohistochemistry for cleaved caspase-3. 28H1-700DX effectively bound to 3T3-FAP cells and induced cytotoxicity upon exposure to 690 nm light, whereas no binding or cytotoxic effects were observed for DP47GS-700DX. Although both 28H1-700DX and DP47GS-700DX accumulated in subcutaneous PDAC299 tumors, autoradiography demonstrated that only 28H1-700DX reached the tumor core. On the contrary, control antibody DP47GS-700DX was only present at the tumor rim. In CKP mice, both antibodies accumulated in the tumor, but tumor-to-blood ratios of 28H1-700DX were higher than that of the control. Notably, in vivo FAP-tPDT caused upregulation of cleaved caspase-3 staining in both subcutaneous and in spontaneous tumors. In conclusion, we have shown that tPDT is a feasible approach for local depletion of FAP-expressing stromal cells in murine models for PDAC.

Quantitative assessment of inflammatory infiltrates in kidney transplant biopsies using multiplex tyramide signal amplification and deep learning.

Delayed graft function (DGF) is a strong risk factor for development of interstitial fibrosis and tubular atrophy (IFTA) in kidney transplants. Quantitative assessment of inflammatory infiltrates in kidney biopsies of DGF patients can reveal predictive markers for IFTA development. In this study, we combined multiplex tyramide signal amplification (mTSA) and convolutional neural networks (CNNs) to assess the inflammatory microenvironment in kidney biopsies of DGF patients (n = 22) taken at 6 weeks post-transplantation. Patients were stratified for IFTA development (<10% versus ≥10%) from 6 weeks to 6 months post-transplantation, based on histopathological assessment by three kidney pathologists. One mTSA panel was developed for visualization of capillaries, T- and B-lymphocytes and macrophages and a second mTSA panel for T-helper cell and macrophage subsets. The slides were multi spectrally imaged and custom-made python scripts enabled conversion to artificial brightfield whole-slide images (WSI). We used an existing CNN for the detection of lymphocytes with cytoplasmatic staining patterns in immunohistochemistry and developed two new CNNs for the detection of macrophages and nuclear-stained lymphocytes. F1-scores were 0.77 (nuclear-stained lymphocytes), 0.81 (cytoplasmatic-stained lymphocytes), and 0.82 (macrophages) on a test set of artificial brightfield WSI. The CNNs were used to detect inflammatory cells, after which we assessed the peritubular capillary extent, cell density, cell ratios, and cell distance in the two patient groups. In this cohort, distance of macrophages to other immune cells and peritubular capillary extent did not vary significantly at 6 weeks post-transplantation between patient groups. CD163+ cell density was higher in patients with ≥10% IFTA development 6 months post-transplantation (p < 0.05). CD3+CD8-/CD3+CD8+ ratios were higher in patients with <10% IFTA development (p < 0.05). We observed a high correlation between CD163+ and CD4+GATA3+ cell density (R = 0.74, p < 0.001). Our study demonstrates that CNNs can be used to leverage reliable, quantitative results from mTSA-stained, multi spectrally imaged slides of kidney transplant biopsies.

Optimized tumour infiltrating lymphocyte assessment for triple negative breast cancer prognostics.

The tumour microenvironment has been shown to be a valuable source of prognostic information for different cancer types. This holds in particular for triple negative breast cancer (TNBC), a breast cancer subtype for which currently no prognostic biomarkers are established. Although different methods to assess tumour infiltrating lymphocytes (TILs) have been published, it remains unclear which method (marker, region) yields the most optimal prognostic information. In addition, to date, no objective TILs assessment methods are available. For this proof of concept study, a subset of our previously described TNBC cohort (n = 94) was stained for CD3, CD8 and FOXP3 using multiplex immunohistochemistry and subsequently imaged by a multispectral imaging system. Advanced whole-slide image analysis algorithms, including convolutional neural networks (CNN) were used to register unmixed multispectral images and corresponding H&E sections, to segment the different tissue compartments (tumour, stroma) and to detect all individual positive lymphocytes. Densities of positive lymphocytes were analysed in different regions within the tumour and its neighbouring environment and correlated to relapse free survival (RFS) and overall survival (OS). We found that for all TILs markers the presence of a high density of positive cells correlated with an improved survival. None of the TILs markers was superior to the others. The results of TILs assessment in the various regions did not show marked differences between each other. The negative correlation between TILs and survival in our cohort are in line with previous studies. Our results provide directions for optimizing TILs assessment methodology.

Spermometer: electrical characterization of single boar sperm motility.

OBJECTIVE: To study single sperm boar motility using electrical impedance measurements in a microfluidic system. DESIGN: Comparison of the optical data and electrical impedance data. SETTING: Research laboratory at a university. ANIMAL(S): Boar semen sample were used. INTERVENTION(S): A microfluidic system is developed that is able to spatially confine single boar sperm cells and allows noninvasive analysis of their motility on the single cell level. Using this system, the single sperm motility was affected by changing the temperature or adding chemical stimuli (caffeine). The retrieved electrical impedance and video data were processed using Matlab. MAIN OUTCOME MEASURE(S): The sperm beat frequency and amplitude determined from the electrical impedance and video data. RESULT(S): The electrically measured sperm beat frequency was verified by optical analysis and in correspondence. Furthermore the microfluidic platform allowed single sperm analysis by altering the sperm by temperature and chemical stimuli. CONCLUSION(S): This platform could be exploited as a potential tool to study sperm cells on the single cell level and to perform advanced sperm selection for intracytoplasmic sperm injection (ICSI) applications.