Deep learning for diabetic retinopathy detection and classification based on fundus images: A review.

Diabetic Retinopathy is a retina disease caused by diabetes mellitus and it is the leading cause of blindness globally. Early detection and treatment are necessary in order to delay or avoid vision deterioration and vision loss. To that end, many artificial-intelligence-powered methods have been proposed by the research community for the detection and classification of diabetic retinopathy on fundus retina images. This review article provides a thorough analysis of the use of deep learning methods at the various steps of the diabetic retinopathy detection pipeline based on fundus images. We discuss several aspects of that pipeline, ranging from the datasets that are widely used by the research community, the preprocessing techniques employed and how these accelerate and improve the models' performance, to the development of such deep learning models for the diagnosis and grading of the disease as well as the localization of the disease's lesions. We also discuss certain models that have been applied in real clinical settings. Finally, we conclude with some important insights and provide future research directions

Perfusion Magnetic Resonance as a Biomarker for Sorafenib-Treated Advanced Hepatocellular Carcinoma: A Pilot Study

Background: Sorafenib is the currently recommended therapy in patients with advanced hepatocellular carcinoma (HCC). Among the several biomarkers available for the evaluation of the therapeutic response and prognosis, there is perfusion magnetic resonance imaging (p-MRI) that, through measurement of the vascular permeability unit (ktrans), may retrieve useful information regarding the microvascular properties of focal liver lesions. The aim of this study was to evaluate the impact of sorafenib therapy in patients with advanced HCC using the p-MRI technique. Materials and Methods: In this retrospective study, 27 patients with the diagnosis of advanced HCC were included for palliative therapy using sorafenib. MRI of the liver was performed before the beginning of the oral therapy (T0), after 3 (T3), and after 6 months (T6). Dynamic acquisitions of the tumor (n = 50, during the first 2 min after contrast injection) were obtained in the coronal plane and were used to compute the parametric perfusion maps, acquiring the ktrans value using the extended Tofts pharmacokinetic model. Results: The value of ktrans obtained at T0 was significantly different from the value of ktrans obtained at T6 (p = 0.028). There were no significant differences between T0 and T3 (p = 0.115) or a correlation between ktrans at T0 and the size of the lesion (p = 0.376). The ktrans value at T0 in patients with progression-free survival (PFS) > 6 months was not significantly different from the ktrans value in patients with PFS ≤6 months (p = 0.113). The ktrans value at T0 was not significantly different between patients who were previously submitted to chemoembolization and those who were not submitted (p = 0.587). Conclusion: In this pilot study, the ktrans value may serve as a biomarker of tumor response to antiangiogenic therapy, but only 6 months after its initiation. Clinical outcomes such as PFS were not predicted before the initiation of treatment

Diffusion Weighted Imaging in the Assessment of Tumor Grade in Endometrial Cancer Based on Intravoxel Incoherent Motion MRI

The aim of this study is to investigate the possibility of predicting histological grade in patients with endometrial cancer on the basis of intravoxel incoherent motion (IVIM)-related histogram analysis parameters. This prospective study included 52 women with endometrial cancer (EC) who underwent MR imaging as initial staging in our hospital, allocated into low-grade (G1 and G2) and high-grade (G3) tumors according to the pathology reports. Regions of interest (ROIs) were drawn on the diffusion weighted images and apparent diffusion coefficient (ADC), true diffusivity (D), and perfusion fraction (f) using diffusion models were computed. Mean, median, skewness, kurtosis, and interquartile range (IQR) were calculated from the whole-tumor histogram. The IQR of the diffusion coefficient (D) was significantly lower in the low-grade tumors from that of the high-grade group with an adjusted p-value of less than 5% (0.048). The ROC curve analysis results of the statistically significant IQR of the D yielded an accuracy, sensitivity, and specificity of 74.5%, 70.1%, and 76.5% respectively, for discriminating low from high-grade tumors, with an optimal cutoff of 0.206 (×10−3 mm2/s) and an AUC of 75.4% (95% CI: 62.1 to 88.8). The IVIM modeling coupled with histogram analysis techniques is promising for preoperative differentiation between low- and high-grade EC tumors

Extended perfusion protocol for MS lesion quantification

This study aims to examine a time-extended dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) protocol and report a comparative study with three different pharmacokinetic (PK) models, for accurate determination of subtle blood–brain barrier (BBB) disruption in patients with multiple sclerosis (MS). This time-extended DCE-MRI perfusion protocol, called Snaps, was applied on 24 active demyelinating lesions of 12 MS patients. Statistical analysis was performed for both protocols through three different PK models. The Snaps protocol achieved triple the window time of perfusion observation by extending the magnetic resonance acquisition time by less than 2 min on average for all patients. In addition, the statistical analysis in terms of adj-R2 goodness of fit demonstrated that the Snaps protocol outperformed the conventional DCE-MRI protocol by detecting 49% more pixels on average. The exclusive pixels identified from the Snaps protocol lie in the low ktrans range, potentially reflecting areas with subtle BBB disruption. Finally, the extended Tofts model was found to have the highest fitting accuracy for both analyzed protocols. The previously proposed time-extended DCE protocol, called Snaps, provides additional temporal perfusion information at the expense of a minimal extension of the conventional DCE acquisition time

Pollen Grain Classification Based on Ensemble Transfer Learning on the Cretan Pollen Dataset

Pollen identification is an important task for the botanical certification of honey. It is performed via thorough microscopic examination of the pollen present in honey; a process called melissopalynology. However, manual examination of the images is hard, time-consuming and subject to inter- and intra-observer variability. In this study, we investigated the applicability of deep learning models for the classification of pollen-grain images into 20 pollen types, based on the Cretan Pollen Dataset. In particular, we applied transfer and ensemble learning methods to achieve an accuracy of 97.5%, a sensitivity of 96.9%, a precision of 97%, an F1 score of 96.89% and an AUC of 0.9995. However, in a preliminary case study, when we applied the best-performing model on honey-based pollen-grain images, we found that it performed poorly; only 0.02 better than random guessing (i.e., an AUC of 0.52). This indicates that the model should be further fine-tuned on honey-based pollen-grain images to increase its effectiveness on such data

Dissecting Tumor-Immune Microenvironment in Breast Cancer at a Spatial and Multiplex Resolution

The evaluation of breast cancer immune microenvironment has been increasingly used in clinical practice, either by counting tumor infiltrating lymphocytes or assessing programmed death ligand 1 expression. However, the spatiotemporal organization of anti-breast cancer immune response has yet to be fully explored. Multiplex in situ methods with spectral imaging have emerged to deconvolute the different elements of tumor immune microenvironment. In this narrative review, we provide an overview of the impact that those methods have, to characterize spatiotemporal heterogeneity of breast cancer microenvironment at neoadjuvant, adjuvant and metastatic setting. Multiplexing in situ can then be useful for new classifications of tumor microenvironment and discovery of immune-related biomarkers within their spatial niche. The tumor immune microenvironment (TIME) is an important player in breast cancer pathophysiology. Surrogates for antitumor immune response have been explored as predictive biomarkers to immunotherapy, though with several limitations. Immunohistochemistry for programmed death ligand 1 suffers from analytical problems, immune signatures are devoid of spatial information and histopathological evaluation of tumor infiltrating lymphocytes exhibits interobserver variability. Towards improved understanding of the complex interactions in TIME, several emerging multiplex in situ methods are being developed and gaining much attention for protein detection. They enable the simultaneous evaluation of multiple targets in situ, detection of cell densities/subpopulations as well as estimations of functional states of immune infiltrate. Furthermore, they can characterize spatial organization of TIME-by cell-to-cell interaction analyses and the evaluation of distribution within different regions of interest and tissue compartments-while digital imaging and image analysis software allow for reproducibility of the various assays. In this review, we aim to provide an overview of the different multiplex in situ methods used in cancer research with special focus on breast cancer TIME at the neoadjuvant, adjuvant and metastatic setting. Spatial heterogeneity of TIME and importance of longitudinal evaluation of TIME changes under the pressure of therapy and metastatic progression are also addressed