Histopathological Slices Research Articles

The Effects of Almond Oil, Walnut Oil and Corn Oil on Histopathology of the Heart, Aorta and Testes in L-NAME-induced Hypertensive Rats

Abstract Introduction: Chronic hypertension is a serious physiological condition that develops histopathological abnormalities such as remodelling, hypertrophy in heart muscle and vasoconstriction of blood vessels. The aim of the study was to determine the impact of almond, walnut and corn oils on remodelling and hypertrophy of the heart, aorta and testicles in high blood pressure (BP)-bearing rats. Materials and Methods: Twenty-five adult male albino rats (Rattus norvegicus) weighing 200–250 g were used. Rats were divided into 5 groups, with 5 rats in each group, as the following: Group first: normal control: standard rat chow and tap water ad libitum; Group 2: hypertension induced by L-NAME (model): This group received L-NAME (40 mg/kg/day) orally; Group 3: model + walnut oil, received almond oil (3 ml/kg) for 3 weeks; Group 4: model + almond oil, received walnut oil (3 ml/kg) for 3 weeks and Group 5: mode + corn oil, received corn oil (3 ml/kg) for 3 weeks. The experiment took 28 days, BP was recorded each week and then the animals were sacrificed under the effects of ketamin-xylazine anaesthesia by cervical dislocation. Histopathological slices were taken from the whole heart, aorta and testes using the standard method. The mean BP for each group was presented in the table, and t-test was used for statistical analysis. Histopathological results for each group are shown in the figures. Results: In contrast to almond oil, walnut oil and corn oil have significantly reduced BP (157.9 ± 4.490 mmHg) to (140.1 ± 3.068 mmHg) but not to the control level (130.7 ± 2.605). Walnut and corn oil could prevent tunica media thickness in the aortic, remodelling and hypertrophy in cardiac muscle and protect the architecture of spermatogenesis tissue in seminiferous tubules of testicle. Almond-treated groups showed no improvement in histopathological changes when compared with the model in aorta, cardiac muscle and testicle. Conclusion: Unlike almond oil, corn and walnut oil can reduce high BP induced by L-NAME and alleviate the histopathological changes that occur as the result of hypertension, such as aortic wall thickness and myocadiac hypertrophy.

A Pilot Study of PSMA PET/CT and MRI Fusion for Prostate Cancer: Software to Replace PET/MRI Hardware.

Introduction: Prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT), in combination with magnetic resonance imaging (MRI), may enhance the diagnosis and staging of prostate cancer. Image fusion of separately acquired PET/CT and MRI images serve to facilitate clinical integration and treatment planning. This study aimed to investigate different PSMA PET/CT and MRI image fusion workflows for prostate cancer visualisation. Methods: Eighteen patients with prostate cancer who underwent PSMA PET/CT and MRI prior to radical prostatectomy were retrospectively selected. Alignment of the prostate was performed between PET/CT and MRI via three techniques: semi-automatic rigid, automatic rigid, and automatic non-rigid. Image fusion accuracy was evaluated through boundary and volume agreement, quantified by the Dice Similarity Coefficient (DSC), 95% Hausdorff Distance (HD), and Mean Surface Distance (MSD), with comparison against reconstructed histopathology slices. Results: Image fusion using all techniques resulted in clear lesion visualisation from PSMA PET/CT overlay and anatomical detail afforded by the MRI base and was consistent with histopathology tumour location. Image fusion accuracy was within the recommended range based on a DSC of 0.8-0.9. The automatic non-rigid registration method had the highest volume agreement (DSC: 0.96 ± <0.01) and boundary agreement (HD: 1.17 ± 0.35 mm) when compared to automatic rigid (DSC 0.88 ± 0.02, HD 3.18 ± 0.29 mm) and semi-automatic rigid (DSC 0.80 ± 0.06, HD 5.25 ± 1.68 mm). Conclusions: Image fusion of clinically obtained PET/CT and MRI is feasible and clinically acceptable for use in prostate cancer diagnosis and surgical management. While the best accuracy was observed with the automatic non-rigid technique, which requires further validation, image fusion with clinically accessible methods (semi-automatic rigid) may currently aid patient education, pre-operative planning, and intra-operative guidance.

Human dorsal root ganglia are either preserved or completely lost after deafferentation by brachial plexus injury

BackgroundPlexus injury results in lifelong suffering from flaccid paralysis, sensory loss, and intractable pain. For this clinical problem, regenerative medicine concepts set high expectations. However, it is largely unknown how dorsal root ganglia (DRG) are affected by accidental deafferentation. MethodsHere, we phenotyped DRG of a clinically and MRI-characterised cohort of 13 patients with plexus injury. Avulsed DRG were collected during reconstructive nerve surgery. For control, we used DRG from forensic autopsy. The cellular composition of the DRG was analysed in histopathological slices with multicolour high-resolution immunohistochemistry, tile microscopy, and deep-learning-based bioimage analysis. We then sequenced the bulk RNA of corresponding DRG slices. ResultsIn about half of the patients we found loss of the typical DRG units consisting of neurones and satellite glial cells. The DRG cells were replaced by mesodermal/connective tissue. In the remaining patients, the cellular units were well preserved. Preoperative plexus MRI neurography was not able to distinguish the two types. Patients with ‘neuronal preservation’ had less maximum pain than patients with ‘neuronal loss’. Arm function improved after nerve reconstruction, but severe pain persisted. Transcriptome analysis of preserved DRGs revealed expression of subtype-specific sensory neurone marker genes, but downregulation of neuronal attributes. Furthermore, they showed signs of ongoing inflammation and connective tissue remodelling. ConclusionsPatients with plexus injury separate into two groups with either neuronal preservation or neuronal loss. The former could benefit from anti-inflammatory therapy. For the latter, studies should explore mechanisms of neuronal loss especially for regenerative approaches. Clinical trial registrationDRKS00017266.

Influence of wall thickness on the rupture risk of a patient-specific cerebral aneurysm: A fluid–structure interaction study

Cerebral aneurysms are the bulges in arteries that have the potential to rupture, as thin-walled regions of an aneurysm are more vulnerable. Understanding the correlation between the wall thickness and the corresponding wall stresses can facilitate better prediction using fluid–structure interaction tools. However, obtaining the actual in vivo wall thickness variation of the aneurysm dome and neck is vital for an accurate prediction of wall stresses. Invasive methods of obtaining wall thickness variation of an abnormal artery may further aggravate the rupture risk of these aneurysms. Modeling aneurysmal wall thickness reconstruction, closer to the in vivo conditions from the histopathological slices, is an apt approach to follow. To this end, the present study performs a comparative assessment of uniform, variable, and patient-specific wall thickness on the hemodynamic and biomechanical wall stresses. Simulations show that maximum wall stresses for the uniform, variable, and patient-specific wall thickness are 13.6, 27.6, and 48.4 kPa, respectively. The maximum wall displacements for the uniform, variable, and patient-specific wall thickness were observed to be 58.5, 126, and 162 μm, respectively. It is observed that the uniform wall thickness model is conservative and underestimates the risk in the prediction of biomechanical stresses and wall displacements. Thinner wall regions experience higher stress for the same internal pressure than thicker wall regions, indicating regions that are more susceptible to rupture. The generation of a variable wall thickness model was observed to be an apt approach, as patient-specific wall thickness information can only be retrospective in the current scientific scenario.

What can machine vision do for lymphatic histopathology image analysis: a comprehensive review

Over the past 10 years, machine vision (MV) algorithms for image analysis have been developing rapidly with computing power. At the same time, histopathological slices can be stored as digital images. Therefore, MV algorithms can provide diagnostic references to doctors. In particular, the continuous improvement of deep learning algorithms has further improved the accuracy of MV in disease detection and diagnosis. This paper reviews the application of image processing techniques based on MV in lymphoma histopathological images in recent years, including segmentation, classification and detection. Finally, the current methods are analyzed, some potential methods are proposed, and further prospects are made.

A semi-supervised learning approach with consistency regularization for tumor histopathological images analysis.

Manual inspection of histopathological images is important in clinical cancer diagnosis. Pathologists implement pathological diagnosis and prognostic evaluation through the microscopic examination of histopathological slices. This entire process is time-consuming, laborious, and challenging for pathologists. The modern use of whole-slide imaging, which scans histopathology slides to digital slices, and analysis using computer-aided diagnosis is an essential problem. To solve the problem of difficult labeling of histopathological data, and improve the flexibility of histopathological analysis in clinical applications, we herein propose a semi-supervised learning algorithm coupled with consistency regularization strategy, called"Semi- supervised Histopathology Analysis Network"(Semi-His-Net), for automated normal-versus-tumor and subtype classifications. Specifically, when inputted disturbing versions of the same image, the model should predict similar outputs. Based on this, the model itself can assign artificial labels to unlabeled data for subsequent model training, thereby effectively reducing the labeled data required for training. Our Semi-His-Net is able to classify patches from breast cancer histopathological images into normal tissue and three other different tumor subtypes, achieving an accuracy was 90%. The average AUC of cross-classification between tumors reached 0.893. To overcome the limitations of visual inspection by pathologists for histopathology images, such as long time and low repeatability, we have developed a deep learning-based framework (Semi-His-Net) for automatic classification subdivision of the subtypes contained in the whole pathological images. This learning-based framework has great potential to improve the efficiency and repeatability of histopathological image diagnosis.

Abstract 12487: Ex Vivo Assessments of Human Nodular Calcification: Optical Frequency Domain Imaging and Histopathology

Introduction: It has been reported that a nodular calcification is associated with a calcified nodule and the reappearance of in-stent failure after percutaneous coronary intervention. Hypothesis: This study evaluated characteristics of nodular calcification on optical frequency domain imaging (OFDI) image in comparison to histopathology. Methods: One hundred and seven matched OFDI and histopathological cross-sections from 13 coronary arteries of 5 human autopsy hearts were evaluated. In histopathological assessment, a nodular calcification was defined as a fragmented calcification with accumulated fibrin. In OFDI analysis, calcification arc, shape of luminal surface, lumen side shape of calcification, irregularity of lumen surface, and irregularity of calcification surface of lumen side were analyzed in each OFDI image. Results: There were 10 nodular calcifications and 97 non-nodular calcifications in histopathological slices. The calcification arc was significantly smaller in nodular calcification than in non-nodular calcification [89.5° (56.4-108) vs. 126° (82.0-190), p =0.02]. OFDI features of a nodular calcification were characterized by a convex shape of the luminal surface (90.0% in nodular calcifications vs. 6.2% in non-nodular calcifications, p &lt;0.01), a convex shape of calcification (90.0% vs 18.6%, p &lt;0.01), an irregular luminal surface (50.0% vs 0%, p &lt;0.01), and an irregular calcification surface of lumen side (60.0% vs 3.1%, p &lt;0.01). Conclusion: This study suggests that OFDI has a potential capability to distinguish between nodular calcification and non-nodular calcification based on angle of calcification, shape and irregularity of luminal surface and those of calcification.

GCLDNet: Gastric cancer lesion detection network combining level feature aggregation and attention feature fusion.

BackgroundAnalysis of histopathological slices of gastric cancer is the gold standard for diagnosing gastric cancer, while manual identification is time-consuming and highly relies on the experience of pathologists. Artificial intelligence methods, particularly deep learning, can assist pathologists in finding cancerous tissues and realizing automated detection. However, due to the variety of shapes and sizes of gastric cancer lesions, as well as many interfering factors, GCHIs have a high level of complexity and difficulty in accurately finding the lesion region. Traditional deep learning methods cannot effectively extract discriminative features because of their simple decoding method so they cannot detect lesions accurately, and there is less research dedicated to detecting gastric cancer lesions.MethodsWe propose a gastric cancer lesion detection network (GCLDNet). At first, GCLDNet designs a level feature aggregation structure in decoder, which can effectively fuse deep and shallow features of GCHIs. Second, an attention feature fusion module is introduced to accurately locate the lesion area, which merges attention features of different scales and obtains rich discriminative information focusing on lesion. Finally, focal Tversky loss (FTL) is employed as a loss function to depress false-negative predictions and mine difficult samples.ResultsExperimental results on two GCHI datasets of SEED and BOT show that DSCs of the GCLDNet are 0.8265 and 0.8991, ACCs are 0.8827 and 0.8949, JIs are 0.7092 and 0.8182, and PREs are 0.7820 and 0.8763, respectively.ConclusionsExperimental results demonstrate the effectiveness of GCLDNet in the detection of gastric cancer lesions. Compared with other state-of-the-art (SOTA) detection methods, the GCLDNet obtains a more satisfactory performance. This research can provide good auxiliary support for pathologists in clinical diagnosis.

Black Line Sign in Focal Cortical Dysplasia IIB: A 7T MRI and Electroclinicopathologic Study.

We aim to provide detailed imaging-electroclinicopathologic characterization of the black line sign, a novel MRI marker for focal cortical dysplasia (FCD) IIB. 7T T2*-weighted gradient-echo (T2*w-GRE) images were retrospectively reviewed in a consecutive cohort of patients with medically intractable epilepsy with pathology-proven FCD II, for the occurrence of the black line sign. We examined the overlap between the black line region and the seizure-onset zone (SOZ) defined by intracranial EEG (ICEEG) and additionally assessed whether complete inclusion of the black line region in the surgical resection was associated with postoperative seizure freedom. The histopathologic specimen was aligned with the MRI to investigate the pathologic underpinning of the black line sign. Region-of-interest-based quantitative MRI (qMRI) analysis on the 7T T1 map was performed in the black line region, entire lesional gray matter (GM), and contralateral/ipsilateral normal gray and white matter (WM). We included 20 patients with FCD II (14 IIB and 6 IIA). The black line sign was identified in 12/14 (85.7%) of FCD IIB and 0/6 of FCD IIA on 7T T2*w-GRE. The black line region was highly concordant with the ICEEG-defined SOZ (5/7 complete and 2/7 partial overlap). Seizure freedom was seen in 8/8 patients whose black line region was completely included in the surgical resection; in the 2 patients whose resection did not completely include the black line region, both had recurring seizures. Inclusion of the black line region in the surgical resection was significantly associated with seizure freedom (p = 0.02). QMRI analyses showed that the T1 mean value of the black line region was significantly different from the WM (p < 0.001), but similar to the GM. Well-matched histopathologic slices in one case revealed accumulated dysmorphic neurons and balloon cells in the black line region. The black line sign may serve as a noninvasive marker for FCD IIB. Both MRI-pathology and qMRI analyses suggest that the black line region was an abnormal GM component within the FCD. Being highly concordant with ICEEG-defined SOZ and significantly associated with seizure freedom when included in resection, the black line sign may contribute to the planning of ICEEG/surgery of patients with medically intractable epilepsy with FCD IIB. This study provides Class II evidence that in individuals with intractable focal epilepsy undergoing resection who have a 7T MRI with adequate image quality, the presence of the black line sign may suggest FCD IIB, be concordant with SOZ from ICEEG, and be associated with more seizure freedom if fully included in resection.

Optimization of a flexible fiber-optic probe for epi-mode quantitative phase imaging.

Quantitative oblique back-illumination microscopy (qOBM) is an emerging label-free optical imaging technology that enables 3D, tomographic quantitative phase imaging (QPI) with epi-illumination in thick scattering samples. In this work, we present a robust optimization of a flexible, fiber-optic-based qOBM system. Our approach enables in silico optimization of the phase signal-to-noise-ratio over a wide parameter space and obviates the need for tedious experimental optimization which could easily miss optimal conditions. Experimental validations of the simulations are also presented and sensitivity limits for the probe are assessed. The optimized probe is light-weight (∼40g) and compact (8mm in diameter) and achieves a 2µm lateral resolution, 6µm axial resolution, and a 300µm field of view, with near video-rate operation (10Hz, limited by the camera). The phase sensitivity is <20nm for a single qOBM acquisition (at 10Hz) and a lower limit of ∼3 nm via multi-frame averaging. Finally, to demonstrate the utility of the optimized probe, we image (1) thick, fixed rat brain samples from a 9L gliosarcoma tumor model and (2) freshly excised human brain tissues from neurosurgery. Acquired qOBM images using the flexible fiber-optic probe are in excellent agreement with those from a free-space qOBM system (both in-situ), as well as with gold-standard histopathology slices (after tissue processing).

Clinical impact of primary tumour 123ImIBG response to induction chemotherapy in children with high-risk neuroblastoma.

More than 50% children with high-risk neuroblastoma (HR-NBL) experience disease progression, which we hypothesise is due to non-response of primary tumour to treatment. Current imaging techniques are unable to characterise response in primary tumour (necrotic versus viable tissue) at diagnosis or follow-up. Compare clinico-histological characteristics between primary 123ImIBG-avid tumours that became entirely 123ImIBG-non-avid (responders) after induction chemotherapy (IC) versus primary 123ImIBG-avid tumour that remained 123ImIBG-avid (non-responders). Retrospective review of clinico-radiological data of children diagnosed with 123ImIBG-avid HR-NBL at our centre (2005-2016). Patients received Rapid COJEC IC and two additional courses of TVD if metastatic response was inadequate. Primary tumour 123ImIBG response was assessed qualitatively as positive, negative or intermediate at diagnosis and after IC. Post-surgical histopathology slices were marked considering percentage of viable tissue. Sixteen of 61 patients showed complete primary tumour 123ImIBG response, 20 partial response, while 25 no response. There was no statistically significant difference between clinical demographics of complete responders and group of non- or partial responders. Mean percentage of viable tumour cells was higher in non-responders than in complete responders (44.6% vs 20.6%; p = 0.05). Five-year EFS was significantly higher in complete responders than non-responders (43 ± 15% vs 7 ± 6%; p < 0.005). 123ImIBG response in primary HR-NBL correlates with amount of necrotic tissue, skeletal metastatic 123ImIBG response and outcome. An entirely 123ImIBG non-avid tumour can still harbour viable tumour cells. Therefore, our findings do not support utility of primary tumour 123ImIBG response in decision making regarding residual tumour surgery. Combining both, primary and metastatic 123ImIBG response will improve interpretability of clinical trial results.

Establishing a survival prediction model for esophageal squamous cell carcinoma based on CT and histopathological images

Currently, the incidence of esophageal squamous cell carcinoma (ESCC) in China is high and its prognosis is poor. To evaluate the prognosis of patients with ESCC, we performed computerized quantitative analyses on diagnostic computed tomography (CT) and digital histopathological slices. A retrospective study was conducted to assess the prognosis of ESCC in 153 patients who underwent esophagectomy, and the cohort was selected based on strict clinical criteria. Each patient had an enhanced CT image, and there were two imaging protocols for CT images of all patients. Each patient in the cohort also had a histopathological tissue slide after hematoxylin–eosin staining. Under an electron microscope, the tissue slide was scanned as an image of large size. We then performed quantitative analyses to identify factors related to the prognosis of ESCC on digital histological images and diagnostic CT images. For CT images, we used the radiomics method. For histological images, we designed a set of quantitative features based on machine learning algorithms, such as K-means and principal component analysis. These features describe the patterns of different cell types in histopathological images. Subsequently, we used the survival analysis model established using only CT image features as the baseline. We also compared multiple machine learning models and adopted a five-fold cross-validation method to establish a robust survival model. In establishing survival models, we first used CT image features to establish survival models, and the C-index from the Weibull Cox model on the test set reached 0.624. Then we used histopathlogical features to establish survival models, and the C-index from the Weibull Cox model on the test set reached 0.664, which was obviously better than CT’s. Lastly, we combined CT image features and histopathological image features to establish survival models. The performance was better than that in the models built using only CT image features or histopathological image features, and the C-index from the regularized Cox model on the test set reached 0.694. We also proved the effectiveness of the quantified histopathological image features in terms of prognosis using the log-rank test. Histopathological image features are more relevant to prognosis than features extracted from CT images using radiomics. The results of this study provide clinicians with a reference to improve the survival rate of patients with ESCC after surgery. These results have implications for advancing the process of explaining the poor prognosis of esophageal cancer.

Histopathological Validation of Dark-Blood Late Gadolinium Enhancement MRI Without Additional Magnetization Preparation.

BackgroundConventional bright‐blood late gadolinium enhancement (LGE) cardiac magnetic resonance imaging (MRI) often suffers from poor scar‐to‐blood contrast due to the bright blood pool adjacent to the enhanced scar tissue. Recently, a dark‐blood LGE method was developed which increases scar‐to‐blood contrast without using additional magnetization preparation.PurposeWe aim to histopathologically validate this dark‐blood LGE method in a porcine animal model with induced myocardial infarction (MI).Study TypeProspective.Animal ModelThirteen female Yorkshire pigs.Field Strength/Sequence 1.5 T, two‐dimensional phase‐sensitive inversion‐recovery radiofrequency‐spoiled turbo field‐echo.AssessmentMI was experimentally induced by transient coronary artery occlusion. At 1‐week and 7‐week post‐infarction, in‐vivo cardiac MRI was performed including conventional bright‐blood and novel dark‐blood LGE. Following the second MRI examination, the animals were sacrificed, and histopathology was obtained. Matching LGE slices and histopathology samples were selected based on anatomical landmarks. Independent observers, while blinded to other data, manually delineated the endocardial, epicardial, and infarct borders on either LGE images or histopathology samples. The percentage of infarcted left‐ventricular myocardium was calculated for both LGE methods on a per‐slice basis, and compared with histopathology as reference standard. Contrast‐to‐noise ratios were calculated for both LGE methods at 1‐week and 7‐week post‐infarction.Statistical TestsPearson's correlation coefficient and paired‐sample t‐tests were used. Significance was set at P < 0.05.ResultsA combined total of 24 matched LGE and histopathology slices were available for histopathological validation. Dark‐blood LGE demonstrated a high level of agreement compared to histopathology with no significant bias (−0.03%, P = 0.75). In contrast, bright‐blood LGE showed a significant bias of −1.57% (P = 0.03) with larger 95% limits of agreement than dark‐blood LGE. Image analysis demonstrated significantly higher scar‐to‐blood contrast for dark‐blood LGE compared to bright‐blood LGE, at both 1‐week and 7‐weeks post‐infarction.Data ConclusionDark‐blood LGE without additional magnetization preparation provides superior visualization and quantification of ischemic scar compared to the current in vivo reference standard.Level of Evidence1Technical Efficacy Stage2

Metabolomics analysis underlay mechanisms in the renal impairment of mice caused by combination of aflatoxin M1 and ochratoxin A

Aflatoxin M1 (AFM1) and ochratoxin A (OTA) are pernicious mycotoxins widely co-existing in the environment. However, nephrotoxicity and underlying mechanism induced by AFM1 coupled with OTA still remain to be explored. In this study, CD-1 mice were treated with 3.5 mg/kg b.w. AFM1, OTA, and AFM1 + OTA for 35 days, and UPLC-MS-based metabolomics method was effectuated to investigate metabolomic profiles of mice kidney. Subsequent experiments on human renal proximal tubular (HK-2) cells were performed to dig out the causal connections between distinguished differential metabolites and nephrotoxicity. Compared with DMSO vehicle group, all three toxin treatments (AFM1 and OTA alone, and in combination) significantly reduced final body weight, and remarkably elevated the concentration of serum creatinine (SCr) and caused abnormal histological phenotypes (shown by histopathological slices). OTA, AFM1 + OTA but not AFM1 reduced the relative weight index of kidney. These phenotypic results indicated that AFM1 and OTA were both toxic to the body, and it seemed that OTA exhibited a notable impairment to kidney while AFM1 had similar but limited effect compared with OTA. Further metabolomics analysis showed that when AFM1 and OTA were combined together, OTA exerted dominant effect on the alteration of metabolic processes. There were few differences in the number of changed metabolites between OTA and AFM1 + OTA group. Among the differentially expressed metabolites affected by OTA, and AFM1 + OTA, lysophosphatidylcholines (LysoPCs) were identified as the main type with significant upregulation, in which LysoPC (16:0) accounted for the most prime proportion. Western blotting results of HK-2 cells showed that single OTA and AFM1 + OTA increased the apoptotic protein expressions of Bax, caspase 3 and PARP, and decreased the expression of Bcl-2; while AFM1 only raised the expression of caspase 3. LysoPC (16:0) but not LysoPC (18:1) lifted the protein level of caspase 3 and PARP in HK-2 cells, and reduced the level of Bcl-2. Taken together, this study is the first effort trying to assess nephrotoxicity of AFM1 with OTA, and we guessed that OTA had a more pronounced toxicity to kidney in contrast to AFM1. No obvious synergism between AFM1 and OTA was found to contribute to the occurrence or development of nephropathy. LysoPC (16:0) might be the pivotal metabolite in response to single OTA and combined AFM1 + OTA engendering renal injury.

Evaluation of Hepatotoxicity of the Extract of Green Walnut Husks (Juglans regia L.) in Mice by a Metabonomic Approach

This work was designed to delineate the comprehensive metabolic changes of hepatotoxicity in mice induced by the extract of green walnut husks (Juglans regia L.) (GWHs). A metabonomic strategy based on high performance liquid chromatography (HPLC) is performed to characterize the metabolic profiling of mice feeding with GWHs extracts at a single dose of 40 mg/kg for ten consecutive days. Investigations on stability and precision of the established metabolic profiles indicated that the method was well controlled and reliable. Acquired chromatographic data sets were subjected to principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (PLS-DA) for differentiating the feeding and the control mice. Six metabolites which have significant contributions to the classification were selected by the variable importance for the projection value. The results indicated that energy metabolism was decelerated, while the creatinine level in serum was significantly decreased. Histopathological slices of liver feeding with GWHs extract showed obviously vacuolization and dilation in the cell sap. We believe that metabonomic approach based on chromatography is helpful to further reveal the toxicity of some medicine.

3D Registration of pre-surgical prostate MRI and histopathology images via super-resolution volume reconstruction.

The use of MRI for prostate cancer diagnosis and treatment is increasing rapidly. However, identifying the presence and extent of cancer on MRI remains challenging, leading to high variability in detection even among expert radiologists. Improvement in cancer detection on MRI is essential to reducing this variability and maximizing the clinical utility of MRI. To date, such improvement has been limited by the lack of accurately labeled MRI datasets. Data from patients who underwent radical prostatectomy enables the spatial alignment of digitized histopathology images of the resected prostate with corresponding pre-surgical MRI. This alignment facilitates the delineation of detailed cancer labels on MRI via the projection of cancer from histopathology images onto MRI. We introduce a framework that performs 3D registration of whole-mount histopathology images to pre-surgical MRI in three steps. First, we developed a novel multi-image super-resolution generative adversarial network (miSRGAN), which learns information useful for 3D registration by producing a reconstructed 3D MRI. Second, we trained the network to learn information between histopathology slices to facilitate the application of 3D registration methods. Third, we registered the reconstructed 3D histopathology volumes to the reconstructed 3D MRI, mapping the extent of cancer from histopathology images onto MRI without the need for slice-to-slice correspondence. When compared to interpolation methods, our super-resolution reconstruction resulted in the highest PSNR relative to clinical 3D MRI (32.15dB vs 30.16dB for BSpline interpolation). Moreover, the registration of 3D volumes reconstructed via super-resolution for both MRI and histopathology images showed the best alignment of cancer regions when compared to (1) the state-of-the-art RAPSODI approach, (2) volumes that were not reconstructed, or (3) volumes that were reconstructed using nearest neighbor, linear, or BSpline interpolations. The improved 3D alignment of histopathology images and MRI facilitates the projection of accurate cancer labels on MRI, allowing for the development of improved MRI interpretation schemes and machine learning models to automatically detect cancer on MRI.

Towards ultrahigh resolution OCT based endoscopical pituitary gland and adenoma screening: a performance parameter evaluation.

Ultrahigh resolution optical coherence tomography (UHR-OCT) for differentiating pituitary gland versus adenoma tissue has been investigated for the first time, indicating more than 80% accuracy. For biomarker identification, OCT images of paraffin embedded tissue are correlated to histopathological slices. The identified biomarkers are verified on fresh biopsies. Additionally, an approach, based on resolution modified UHR-OCT ex vivo data, investigating optical performance parameters for the realization in an in vivo endoscope is presented and evaluated. The identified morphological features–cell groups with reticulin framework–detectable with UHR-OCT showcase a promising differentiation ability, encouraging endoscopic OCT probe development for in vivo application.

More focus on atypical glandular cells in cervical screening: Risk of significant abnormalities and low histological follow-up rate.

Objectives:Atypical glandular cells (AGC) detected by Papanicolaou (Pap) smears are in close relation with adenocarcinoma and precursors detected by histopathology. Yet, sometimes the cytological diagnosis of AGC has been neglected. With increase of adenocarcinoma and precursors, we need more focus on glandular abnormalities.Material and Methods:Clinicopathological data of patients who had AGC on Pap smears between April 2015 and October 2018 and underwent histological follow-up were retrieved from the computerized database of Beijing Obstetrics and Gynecology Hospital, Capital Medical University. Patients with a prior history of cancer were excluded from the study. Statistical analyses were performed using Pearson’s Chi-square test in SPSS software version 23. P < 0.05 (two sided) was considered as statistical significance.Results:Liquid-based cytological examination of the uterine cervix was carried out in 164,080 women. Five hundred and twenty-five women were diagnosed with AGC, 314 with not otherwise specified (AGC-NOS), and 211 with favor neoplastic (AGC-FN). Only 310 cases had histological follow-up, 168 women (168/314, 53.5%) originally with AGC-NOS on Pap smears, and 142 (142/211, 67.3%) with AGC-FN. The median age of histological significant abnormalities was 46.7 years, and 126 women (126/162, 77.8%) were postmenopausal. Sixty-six cases (66/168, 39.3%) of AGC-NOS had significant abnormalities (96/142, 67.6%, AGC-FN). One hundred and sixty-two cases of significant abnormalities included 40 high-grade squamous abnormalities and 122 glandular abnormalities. AGC-FN was more likely to be associated with a clinically significant abnormalities (P < 0.001) compared to AGC-NOS.Conclusions:Patients with AGC on Pap smears are in close relation with significant abnormalities, especially with significant glandular abnormalities on histopathology slices. AGC should be evaluated vigilantly with histological workup, especially if patients are diagnosed with AGC-FN and are aged 41–60 years. We need more focus on AGC.

Registration of presurgical MRI and histopathology images from radical prostatectomy via RAPSODI.

PurposeMagnetic resonance imaging (MRI) has great potential to improve prostate cancer diagnosis; however, subtle differences between cancer and confounding conditions render prostate MRI interpretation challenging. The tissue collected from patients who undergo radical prostatectomy provides a unique opportunity to correlate histopathology images of the prostate with preoperative MRI to accurately map the extent of cancer from histopathology images onto MRI. We seek to develop an open‐source, easy‐to‐use platform to align presurgical MRI and histopathology images of resected prostates in patients who underwent radical prostatectomy to create accurate cancer labels on MRI.MethodsHere, we introduce RAdiology Pathology Spatial Open‐Source multi‐Dimensional Integration (RAPSODI), the first open‐source framework for the registration of radiology and pathology images. RAPSODI relies on three steps. First, it creates a three‐dimensional (3D) reconstruction of the histopathology specimen as a digital representation of the tissue before gross sectioning. Second, RAPSODI registers corresponding histopathology and MRI slices. Third, the optimized transforms are applied to the cancer regions outlined on the histopathology images to project those labels onto the preoperative MRI.ResultsWe tested RAPSODI in a phantom study where we simulated various conditions, for example, tissue shrinkage during fixation. Our experiments showed that RAPSODI can reliably correct multiple artifacts. We also evaluated RAPSODI in 157 patients from three institutions that underwent radical prostatectomy and have very different pathology processing and scanning. RAPSODI was evaluated in 907 corresponding histpathology‐MRI slices and achieved a Dice coefficient of 0.97 ± 0.01 for the prostate, a Hausdorff distance of 1.99 ± 0.70 mm for the prostate boundary, a urethra deviation of 3.09 ± 1.45 mm, and a landmark deviation of 2.80 ± 0.59 mm between registered histopathology images and MRI.ConclusionOur robust framework successfully mapped the extent of cancer from histopathology slices onto MRI providing labels from training machine learning methods to detect cancer on MRI.

MRI detection of cerebral microbleeds: size matters.

Cerebral microbleeds (CMB) play an important role as an imaging biomarker notably in vascular and neurodegenerative diseases. Current clinical brain MRI underestimates the number of CMB with respect to histopathology. It is expected that small CMBs are more likely to be false-negatives, yet this has not been demonstrated and the average size of false-negative and true-positive CMBs have not been established. The radiologic-histopathologic correlation study was approved by the local review board and included 42 consecutive cases (mean age at death, 80.7 ± 10.0years; 23 females and 19 men) between 12 January 2012 and 10 December 2012 having undergone brain autopsy. Postmortem SWI (susceptibility-weighted imaging) images were acquired on a clinical 3T system using parameters similar to clinical routine. The detection of CMB on postmortem MRI was compared with corresponding histopathological slices. Postmortem MRI detected 23 true-positive CMB. Histopathology additionally detected 68 CMBs (false-negative MRI CMBs). The average size true-positive MRI CMBs had on histopathology was 3.6 ± 7.1mm3. The average size false-negative MRI CMBs was significantly smaller (p < 0.05), measuring 0.3 ± 1.2mm3 on histopathology. Size matters. As expected, the average size of true-positive MRI CMB was around 10 times larger as compared with false-negative MRI CMB. Evidently, in addition to size, other factors will influence the detectability of CMB, including iron content, ratio of Fe2+/Fe3+, spatial configuration, and location, yet this remains to be elucidated in future studies.