Effect of lamins and emerin on nuclear morphology and histological architecture in lung adenocarcinoma.

Emerin and lamins not only influence nuclear morphology but are also involved in differentiation. We herein examined 82 resected cases of invasive lung adenocarcinoma using computer-assisted image analysis of nuclear morphology on Feulgen-stained and immunohistochemical sections of lamin A, B1, B2, and emerin (four proteins) to calculate the rank sum of the cell positivity rates for these four proteins. The rank sum of four proteins showed weak negative correlations with the nuclear area and perimeter and a weak positive correlation with the nuclear shape factor. Interestingly, the top three cases with the highest rank sum were papillary adenocarcinoma, and the bottom three cases were acinar adenocarcinomas containing cribriform patterns. We compared the rank sum for grading (differentiation: G1, G2, and G3) and predominant histological subtypes and found that the rank sum of G3 was lower than that of G1 and G2. Furthermore, the rank sum was lower for acinar adenocarcinoma with >20 % cribriform pattern (acinar+cribri) and solid adenocarcinoma than for lepidic and papillary adenocarcinoma. Individual examination of the four proteins revealed that emerin expression was lower in G3 than in G1, and lamin B2 expression was lower in G3 than in G1 and G2. Compared with lepidic adenocarcinoma, acinar+cribri showed significantly lower expression of all four proteins among histological subtypes. These data indicated that the expression of lamin A, B1, B2, and emerin was markedly decreased in poorly differentiated adenocarcinoma (i.e., G3), especially in acinar+cribri. Our data suggested that changes in these four proteins can not only affect nuclear morphology but also histological structure in lung adenocarcinoma.

Automatic analysis of nuclear features reveals a non-tumoral predictor of tumor grade in bladder cancer.

BACKGROUND & OBJECTIVES: Tumor grade determines prognosis in urothelial carcinoma. The classification of low and high grade is based on nuclear morphological features that include nuclear size, hyperchromasia and pleomorphism. These features are subjectively assessed by the pathologists and are not numerically measured, which leads to high rates of interobserver variability. The purpose of this study is to assess the value of a computer-based image analysis tool for identifying predictors of tumor grade in bladder cancer. METHODS: Four hundred images of urothelial tumors were graded by five pathologists and two expert genitourinary pathologists using a scale of 1 (lowest grade) to 5 (highest grade). A computer algorithm was used to automatically segment the nuclei and to provide morphometric parameters for each nucleus, which were used to establish the grading algorithm. Grading algorithm was compared to pathologists' agreement. RESULTS: Comparison of the grading scores of the five pathologists with the expert genitourinary pathologists score showed agreement rates between 88.5% and 97.5%.The agreement rate between the two expert genitourinary pathologists was 99.5%. The quantified algorithm based conventional parameters that determine the grade (nuclear size, pleomorphism and hyperchromasia) showed > 85% agreement with the expert genitourinary pathologists. Surprisingly, the parameter that was most associated with tumor grade was the 10th percentile of the nuclear area, and high grade was associated with lower 10th percentile nuclei, caused by the presence of more inflammatory cells in the high-grade tumors. CONCLUSION: Quantitative nuclear features could be applied to determine urothelial carcinoma grade and explore new biologically explainable parameters with better correlation to grade than those currently used.

Can the analysis of chromatin texture and nuclear fractal dimensions serve as effective means to distinguish non-invasive follicular thyroid neoplasm with papillary-like nuclear features from other malignancies with follicular pattern in the thyroid?: a study.

OBJECTIVE: Thyroid carcinoma ranks as the 9th most prevalent global cancer, accounting for 586,202 cases and 43,636 deaths in 2020. Computerized image analysis, utilizing artificial intelligence algorithms, emerges as a potential tool for tumor evaluation. AIM: This study aims to assess and compare chromatin textural characteristics and nuclear dimensions in follicular neoplasms through gray-level co-occurrence matrix (GLCM), fractal, and morphometric analysis. METHOD: A retrospective cross-sectional study involving 115 thyroid malignancies, specifically 49 papillary thyroid carcinomas with follicular morphology, was conducted from July 2021 to July 2023. Ethical approval was obtained, and histopathological examination, along with image analysis, was performed using ImageJ software. RESULTS: A statistically significant difference was observed in contrast (2.426 (1.774-3.412) vs 2.664 (1.963-3.610), p = .002), correlation (1.202 (1.071-1.298) vs 0.892 (0.833-0.946), p = .01), and ASM (0.071 (0.090-0.131) vs 0.044 (0.019-0.102), p = .036) between NIFTP and IFVPTC. However, morphometric parameters did not yield statistically significant differences among histological variants. CONCLUSION: Computerized image analysis, though promising in subtype discrimination, requires further refinement and integration with traditional diagnostic parameters. The study suggests potential applications in scenarios where conventional histopathological assessment faces limitations due to limited tissue availability. Despite limitations such as a small sample size and a retrospective design, the findings contribute to understanding thyroid carcinoma characteristics and underscore the need for comprehensive evaluations integrating various diagnostic modalities.

Nuclear long diameter as a new criterion to distinguish high-grade urothelial carcinoma cells from benign reactive cells.

OBJECTIVE: Recently, the nuclear area has attracted attention as a morphological parameter to differentiate high-grade urothelial carcinoma (HGUC) cells from benign reactive cells. The nuclear long diameter (NLD) strongly correlates with the nuclear area and is easy to subjectively estimate. Therefore, this study examined the usefulness of the NLD-to-neutrophil diameter ratio for detecting HGUC cells in urine cytology. METHODS: This study included 29, 26 and 18 patients with HGUC, glomerular disease and urolithiasis respectively. An image analysis system was used to measure the NLD of HGUC and benign reactive cells (reactive renal tubular cells and reactive urothelial cells) and the neutrophil diameter that appeared in the voided urine in these cases. The NLD index was calculated using the NLD-to-neutrophil diameter ratio. We subsequently compared HGUC and benign reactive cells with respect to NLD and NLD indices. In addition, the HGUC cell group and benign reactive cell group were compared by selecting the five cells with the largest NLD and NLD index on each slide. RESULTS: The NLD and NLD indices of HGUC cells were significantly higher than those of benign reactive cells in all cells and in the five cells with the largest NLD and NLD indices. The cut-off value of the NLD index for detecting HGUC cells was 1.25 in all cells and 1.80 in the five cells with the largest NLD index. CONCLUSIONS: The NLD index is a useful parameter that can be introduced into routine microscopic examinations to differentiate HGUC cells from benign reactive cells.

Evaluation of nuclear morphometry in exfoliative cytology of buccal mucosa in patients with high risk of oral cancer.

BACKGROUND: Oral cancer poses a significant global health burden, with India having the highest prevalence. Effective detection is crucial in effective prevention. This study aimed to evaluate nuclear morphometric parameters (NMPs) in buccal mucosa cells of smokers, correlate NMPs with dysplasia, establish cut off values for grading dysplasia, and investigate the relationship between NMPs and smoking. METHODS: After obtaining ethical approval and informed consent, patients were recruited from the outpatient department of our institution. A target sample size of 250 was calculated. The data included smoking exposure quantified in pack-years, nuclear morphometric analysis (NMA) of buccal mucosa cells obtained through oral cytology using Image J, and the severity of dysplasia of the slides assessed by pathologists. Statistical analysis assessed the impact of dysplasia and the association between nuclear characteristics and smoking exposure. Receiver operating characteristic (ROC) plots determined the potential of these parameters to distinguish dysplasia levels. RESULTS: Significant differences in NMPs were observed among different smoking groups. Dysplasia severity had a significant correlation with NMPs, and strong correlations were found between NMPs and lifetime smoking exposure. ROC analysis established cut off values for NMPs with good sensitivity and specificity for classifying dysplasia severity. CONCLUSIONS: This study highlights the potential of NMA as a tool for oral cancer screening. NMPs can distinguish dysplasia severity and correlate with tobacco (smoking). The efficiency of NMA in a non-invasive oral cytology offers promise for patient-centered screening Additionally, the findings suggest future applications in telepathology and the potential for AI integration in automated screening after conducting multicentric large-scale studies.

Structure Embedded Nucleus Classification for Histopathology Images.

Nuclei classification provides valuable information for histopathology image analysis. However, the large variations in the appearance of different nuclei types cause difficulties in identifying nuclei. Most neural network based methods are affected by the local receptive field of convolutions, and pay less attention to the spatial distribution of nuclei or the irregular contour shape of a nucleus. In this paper, we first propose a novel polygon-structure feature learning mechanism that transforms a nucleus contour into a sequence of points sampled in order, and employ a recurrent neural network that aggregates the sequential change in distance between key points to obtain learnable shape features. Next, we convert a histopathology image into a graph structure with nuclei as nodes, and build a graph neural network to embed the spatial distribution of nuclei into their representations. To capture the correlations between the categories of nuclei and their surrounding tissue patterns, we further introduce edge features that are defined as the background textures between adjacent nuclei. Lastly, we integrate both polygon and graph structure learning mechanisms into a whole framework that can extract intra and inter-nucleus structural characteristics for nuclei classification. Experimental results show that the proposed framework achieves significant improvements compared to the previous methods. Code and data are made available via https://github.com/lhaof/SENC.

Morphologic features of prostate cancer-encased native vessels: An image analysis study.

BACKGROUND: Vasculature plays a crucial role in the progression of prostate cancer (PC). Changes to the prostatic native vessels have not been studied since 2000 when Garcia et al. demonstrated marked media hypercellularity and increased artery thickness in prostatic native arteries within PC. We aim to further evaluate and characterize prostatic native vessels with a more accurate method with the use of virtual slides and digital analysis. DESIGN: Pathologist-annotated whole-mount digital slides from 96 entirely submitted prostatectomies were annotated for PC (color-coded by Gleason) using Omero platform. A subset of 44 cases met criteria for further analysis of media thickness, cellularity, and wall thickness to lumen ratio. Cases were included based on containing ≥5 native arteries (≥100 µm diameter) encased on at least 3 sides by PC, with vessels (≥100 µm diameter) designated as controls if they were ≥ 1000 µm away from PC. Annotated vessels were segmented and processed using Matlab 2023b. Mean media thickness (corrected for oblique sections), media: lumen ratio (based on numbers of pixels), and media cellularity (nuclei count) were studied by analysis with SPSS by linear mixed model with nested random effects for subject and slide to account for repeated measures. RESULTS: Vessels encased by PC showed greater media thickness (p=0.02), cellularity (p=0.02) and wall thickness/lumen ratio (p= <0.001) compared to vessels away from PC. These values showed an increasing trend according to stage in cellularity (p=0.14), media thickness (p=0.12) and wall thickness/ lumen ratio (p= 0.33) with higher stage (pT3). A Gleason group comparison showed a borderline-significant gradewise trend when analyzing wall thickness/lumen ratio (p=0.06). Grade 5 emerged as significantly different (p=0.02) from grades 3 or 4 non-cribriform. CONCLUSIONS: Similar to the 2000 study, increased media thickness and hypercellularity of vessels encased by PC were evident compared to controls. Borderline grade-dependent increased vessel cellularity changes were seen, suggesting a possible role in PC progression; the predictive value of these changes for outcome is uncertain. Whether the etiology of changes reflects locally increased intravascular pressure of vessels within tumor should be investigated.

NuInsSeg: A fully annotated dataset for nuclei instance segmentation in H&E-stained histological images.

In computational pathology, automatic nuclei instance segmentation plays an essential role in whole slide image analysis. While many computerized approaches have been proposed for this task, supervised deep learning (DL) methods have shown superior segmentation performances compared to classical machine learning and image processing techniques. However, these models need fully annotated datasets for training which is challenging to acquire, especially in the medical domain. In this work, we release one of the biggest fully manually annotated datasets of nuclei in Hematoxylin and Eosin (H&E)-stained histological images, called NuInsSeg. This dataset contains 665 image patches with more than 30,000 manually segmented nuclei from 31 human and mouse organs. Moreover, for the first time, we provide additional ambiguous area masks for the entire dataset. These vague areas represent the parts of the images where precise and deterministic manual annotations are impossible, even for human experts. The dataset and detailed step-by-step instructions to generate related segmentation masks are publicly available on the respective repositories.

Conjunctival leiomyosarcoma: A clinico-pathological study with in deep molecular characterization.

BACKGROUND: Primary and metastatic leiomyosarcomas (LMS) involving the orbital region are well known to occur however, the conjunctiva represents an extremely rare site of occurrence. METHODS: A 97-year-old male was referred to the Ocular Oncology Unit due to a rapidly growing painful mass (16×12×20 mm) in the nasal conjunctiva of his left eye. Wide excision followed by radiotherapy was performed. RESULTS: Based on the microscopic features (hypercellular neoplasm composed of spindle cells with cigar shaped and blunt ended nuclei with brightly eosinophilic fibrillary cytoplasm) and immunohistochemical findings (positive staining for Vimentin, Desmin, Caldesmon, and SMA and negative staining for AE1/AE3, EMA, CD117, S100, MelanA, SOX10, HMB45, TLE1, CD99, EMA and AE1 / AE3) the final diagnosis of grade 2 leyomiosarcoma was rendered. Moreover, 'in deep' DNA sequencing (>500 genes analysis) revealed a neoplasm with high TMB: 64 muts/Mb and numerous VUS and several pathogenic/oncogenic molecular alterations, including CNV loss or gain in > 10 genes. At the last follow-up visit, residual disease was observed in the superior fornix, at the nasal limbus and the cornea. At the time of writing, after a follow-up of 2 month the patients is still alive without evidence of metastatic disease. CONCLUSION: An uncommon molecular finding observed in our case was the presence of TSC1 gene mutation usually associated with soft tissue and gynecological PEComas. Our finding may harbor important therapeutic implications since the inactivation of the tumor suppressor genes TSC1 and TSC2 lead to upregulation of mTOR signaling, providing the rationale for target therapy with mTOR inhibitors. Additional studies on larger series are needed to validate our findings.

Differentiating BRAF V600E- and RAS-like alterations in encapsulated follicular patterned tumors through histologic features: a validation study.

Differentiating BRAF V600E- and RAS-altered encapsulated follicular-patterned thyroid tumors based on morphology remains challenging. This study aimed to validate an 8-score scale nuclear scoring system and investigate the importance of nuclear pseudoinclusions (NPIs) in aiding this differentiation. A cohort of 44 encapsulated follicular-patterned tumors with varying degrees of nuclear atypia and confirmed BRAF V600E or RAS alterations was studied. Nuclear parameters (area, diameter, and optical density) were analyzed using a deep learning model. Twelve pathologists from eight Asian countries visually assessed 22 cases after excluding the cases with any papillae. Eight nuclear features were applied, yielding a semi-quantitative score from 0 to 24. A threshold score of 14 was used to distinguish between RAS- and BRAF V600E-altered tumors. BRAF V600E-altered tumors typically demonstrated higher nuclear scores and notable morphometric alterations. Specifically, the nuclear area and diameter were significantly larger, and nuclear optical density was much lower compared to RAS-altered tumors. Observer accuracy varied, with two pathologists correctly identifying genotype of all cases. Observers were categorized into proficiency groups, with the highest group maintaining consistent accuracy across both evaluation methods. The lower group showed a significant improvement in accuracy upon utilizing the 8-score scale nuclear scoring system, with notably increased sensitivity and negative predictive value in BRAF V600E tumor detection. BRAF V600E-altered tumors had higher median total nuclear scores. Detailed reevaluation revealed NPIs in all BRAF V600E-altered cases, but in only 2 of 14 RAS-altered cases. These results could significantly assist pathologists, particularly those not specializing in thyroid pathology, in making a more accurate diagnosis.

Colorectal cancer histopathology image analysis: A comparative study of prognostic values of automatically extracted morphometric nuclear features in multispectral and red-blue-green imagery.

OBJECTIVES: Multispectral imaging (MSI) has been utilized to predict the prognosis of colorectal cancer (CRC) patients, however, our understanding of the prognostic value of nuclear morphological parameters of bright-field MSI in CRC is still limited. This study was designed to compare the efficiency of MSI and standard red-green-blue (RGB) images in predicting the prognosis of CRC. METHODS: We compared the efficiency of MS and conventional RGB images on the quantitative assessment of hematoxylin-eosin (HE) stained histopathology images. A pipeline was developed using a pixel-wise support vector machine (SVM) classifier for gland-stroma segmentation, and a marker-controlled watershed algorithm was used for nuclei segmentation. The correlation between extracted morphological parameters and the five-year disease-free survival (5-DFS) was analyzed. RESULTS: Forty-seven nuclear morphological parameters were extracted in total. Based on Kaplan-Meier analysis, eight features derived from MS images and seven featured derived from RGB images were significantly associated with 5-DFS, respectively. Compared with RGB images, MSI showed higher accuracy, precision, and Dice index in nuclei segmentation. Multivariate analysis indicated that both integrated parameters 1 (factors negatively correlated with CRC prognosis including nuclear number, circularity, eccentricity, major axis length) and 2 (factors positively correlated with CRC prognosis including nuclear average area, area perimeter, total area/total perimeter ratio, average area/perimeter ratio) in MS images were independent prognostic factors of 5-DFS, in contrast with only integrated parameter 1 (P<0.001) in RGB images. More importantly, the quantification of HE-stained MS images displayed higher accuracy in predicting 5-DFS compared with RGB images (76.9% vs 70.9%). CONCLUSIONS: Quantitative evaluation of HE-stained MS images could yield more information and better predictive performance for CRC prognosis than conventional RGB images, thereby contributing to precision oncology.

Aggressive renal cell carcinoma with biphasic papillary and solid clear cell features harboring IDH2 (R172M) mutation.

Renal cell carcinoma (RCC) is fundamentally a metabolic disease, and RCC associated with mutation of the Krebs cycle enzyme genes include fumarate hydratase-deficient and succinate dehydrogenase-deficient RCC. Most recently, the mutation of isocitrate dehydrogenase 2 (IDH2) has been suggested as the third Krebs cycle enzyme alteration to be associated with oncometabolite-induced RCC tumorigenesis. Herein, we report the second case of RCC harboring an IDH2 (R127M) mutation identified by targeted next-generation sequencing and further confirmed by reverse transcription polymerase chain reaction and Sanger sequencing. This tumor demonstrated a distinctive biphasic morphology, characterized by mixture of a clear cells solid component and an eosinophilic papillary component. These two components were intermingled and formed variably sized nodular or nested structures. Unfavorable histologic features, including infiltration into the perirenal and renal sinus adipose tissues, high nuclei grade, rhabdoid tumor cells, and focal tumor necrosis, were observed. The patient had local lymph nodes metastases at diagnosis and developed brain metastases 3 months after the surgery. This peculiar case provides further insights into RCCs harboring IDH2 mutations.

HoLy-Net: Segmentation of histological images of diffuse large B-cell lymphoma.

Over the last years, there has been large progress in automated segmentation and classification methods in histological whole slide images (WSIs) stained with hematoxylin and eosin (H&E). Current state-of-the-art (SOTA) techniques are based on diverse datasets of H&E-stained WSIs of different types of predominantly solid cancer. However, there is a scarcity of methods and datasets enabling segmentation of tumors of the lymphatic system (lymphomas). Here, we propose a solution for segmentation of diffuse large B-cell lymphoma (DLBCL), the most common non-Hodgkin's lymphoma. Our method applies to both H&E-stained slides and to a broad range of markers stained with immunohistochemistry (IHC). While IHC staining is an important tool in cancer diagnosis and treatment decisions, there are few automated segmentation and classification methods for IHC-stained WSIs. To address the challenges of nuclei segmentation in H&E- and IHC-stained DLBCL images, we propose HoLy-Net - a HoVer-Net-based deep learning model for lymphoma segmentation. We train two different models, one for segmenting H&E- and one for IHC-stained images and compare the test results with the SOTA methods as well as with the original version of HoVer-Net. Subsequently, we segment patient WSIs and perform single cell-level analysis of different cell types to identify patient-specific tumor characteristics such as high level of immune infiltration. Our method outperforms general-purpose segmentation methods for H&E staining in lymphoma WSIs (with an F1 score of 0.899) and is also a unique automated method for IHC slide segmentation (with an F1 score of 0.913). With our solution, we provide a new dataset we denote LyNSeC (lymphoma nuclear segmentation and classification) containing 73,931 annotated cell nuclei from H&E and 87,316 from IHC slides. Our method and dataset open up new avenues for quantitative, large-scale studies of morphology and microenvironment of lymphomas overlooked by the current automated segmentation methods.

Blasts with folded nuclei: A histopathologic finding in myeloid leukemia cutis with NPM1 and FLT3 mutations.

Leukemia cutis is a term used to describe cutaneous manifestations of leukemic infiltration of the skin and portends a poor prognosis. Cutaneous involvement by hematopoietic/lymphoid tumors can occur before, concurrently, or after the initial diagnosis. Early involvement of dermatologists and timely biopsies play a crucial role in achieving a prompt diagnosis. Prior reports of acute myeloid leukemia have revealed a strong association between the cup-like nuclear morphology observed in bone marrow specimens and concurrent mutations of NPM1 and FLT3-ITD. In cutaneous tissue sections of leukemia cutis, folded or indented nuclei may represent the "cup-like" counterpart previously described in bone marrow specimens. Recognizing this morphological feature could aid in identifying this molecular subtype of leukemia cutis. In this study, we present a case of leukemia cutis in a 63-year-old female with AML and NPM1 and FLT3-ITD mutations, demonstrating scattered indented/folded nuclei.

Nuclei detection in breast histopathology images with iterative correction.

This work presents a deep network architecture to improve nuclei detection performance and achieve the high localization accuracy of nuclei in breast cancer histopathology images. The proposed model consists of two parts, generating nuclear candidate module and refining nuclear localization module. We first design a novel patch learning method to obtain high-quality nuclear candidates, where in addition to categories, location representations are also added to the patch information to implement the multi-task learning process of nuclear classification and localization; meanwhile, the deep supervision mechanism is introduced to obtain the coherent contributions from each scale layer. In order to refine nuclear localization, we propose an iterative correction strategy to make the prediction progressively closer to the ground truth, which significantly improves the accuracy of nuclear localization and facilitates neighbor size selection in the nonmaximum suppression step. Experimental results demonstrate the superior performance of our method for nuclei detection on the H&E stained histopathological image dataset as compared to previous state-of-the-art methods, especially in multiple cluttered nuclei detection, can achieve better results than existing techniques.

CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting.

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge's improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSI-level results to foster the development of further methods for biomarker discovery.

Digital analyses of nuclear features can help discriminate among non-invasive follicular thyroid neoplasm with papillary-like nuclear features, papillary thyroid carcinoma follicular subtype, and follicular carcinoma in cytological specimens.

BACKGROUND: As it stands, the diagnosis of non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) is primarily based on histological analysis. We hypothesised that computerised analysis of nuclear images of cytological specimens could be used to differentiate NIFTP from papillary thyroid carcinoma follicular subtype (PTCFS) and follicular carcinoma (FC), influencing patient management. METHODS: We employed a retrospective analytical observational study based on nuclear morphometric variables of cytological material from thyroid nodules classified as PTCFS, NIFTP, or FC. Five cases of each entity were analysed. Cytological slides were photographed, and 1170 cells for each entity were analysed digitally. The captured images were evaluated (blindly) using the ImageJ software package. The morphometric evaluation included area, perimeter, width, height, and circularity. Numerical variables were expressed as mean, median, minimum, and maximum (min; max) values. Kruskal-Wallis and Dunn's tests were used with a 5% significance level. RESULTS: Regarding nuclear analysis, all variables differed among the three groups (p < 0.001). Given the interdependence among the variables, these data indicated that nuclear size was greatest in the NIFTP group, followed by FC and PTCFS. DISCUSSION/CONCLUSION: Our analysis of the digital images, with a focus on nuclear parameters, found significantly difference among cytological specimens from cases of NIFTP, PTCFS and FC. Thus, this tool has the potential to provide additional information that may help in the diagnosis of NIFTP, even during the preoperative period. Additional studies are needed to create protocols, evaluate the applicability of nuclear morphological and morphometric parameters-focusing on digital pathology-and create algorithms and tools to assist cytopathologists with their diagnostic routines.

Discrimination of early HCC using single cell nucleus image and visualization of feature distribution in whole slide images.

Among hepatocellular carcinoma (HCC), early HCC such as well-differentiated hepatocellular carcinoma is more difficult to distinguish from non-cancer than other cancers. In particular, very well-differentiated hepatocellular carcinoma is even more difficult to distinguish, and it is difficult for pathologists to distinguish between cancer and non-cancer from a single nucleus image. If a function to distinguish cancer with a single cell nucleus image is realized, it may be possible to find new features related to nuclei that are useful for differentiating early HCC. The function will also be very helpful in needle biopsy where the area that can be observed is limited.In this study, we investigated the potential to discriminate cancer/non-cancer from an image of a single hepatocyte nucleus using CNN. The results indicated that discrimination was achievable with a correct rate of around 70%.The probability of cancer/non-cancer was visualized on WSI. The visualization results indicated a difference between cancerous and non-cancerous areas in 71% of the cases, which will help pathologists distinguish region of interest. Grouping sections with similar features also proved useful in improving accuracy and visualization results.

Cell migration in dense microenvironments.

The nucleus has been viewed as a passenger during cell migration that functions merely to protect the genome. However, increasing evidence shows that the nucleus is an active organelle, constantly sensing the surrounding environment and translating extracellular mechanical inputs into intracellular signaling. The nuclear envelope has a large membrane reservoir which serves as a buffer for mechanical inputs as it unfolds without increasing its tension. In contrast, when cells cope with mechanical strain, such as migration through solid tumors or dense interstitial spaces, the nuclear envelope folds stretch, increasing nuclear envelope tension and sometimes causing rupture. Different degrees of nuclear envelope tension regulate cellular behaviors and functions, especially in cells that move and grow within dense matrices. The crosstalk between extracellular mechanical inputs and the cell nucleus is a critical component in the modulation of cell function of cells that navigate within packed microenvironments. Moreover, there is a link between regimes of nuclear envelope unfolding and different cellular behaviors, from orchestrated signaling cascades to cellular perturbations and damage.

Le noyau a longtemps été considéré comme un passager lors de la migration cellulaire, servant simplement à protéger le génome. Cependant, de plus en plus de preuves montrent que le noyau est un organite actif, qui sonde le milieu environnant et traduit les entrées mécaniques extracellulaires en signalisation intracellulaire. L'enveloppe nucléaire possède un grand réservoir membranaire qui sert de tampon face aux entrées mécaniques en se dépliant sans augmenter sa tension. En revanche, lorsque les cellules font face à des contraintes mécaniques, telles que la migration au travers de tumeurs solides ou despaces interstitiels denses, les plis de l'enveloppe nucléaire s'étirent, augmentant sa tension et provoquant parfois sa rupture. Différents degrés de tension de l'enveloppe nucléaire régulent les comportements et les fonctions cellulaires, en particulier des cellules qui se déplacent et se développent dans des matrices denses. La signalisation croisée entre les entrées mécaniques extracellulaires et le noyau cellulaire sont des composants essentiels dans la modulation de la fonction des cellules qui naviguent dans des microenvironnements encombrés. De plus, il existe un lien entre les régimes de déploiement de l'enveloppe nucléaire et les différents comportements cellulaires, allant des cascades de signalisation jusquaux perturbations et dommages cellulaires.

Early Alzheimer's disease pathology in human cortex involves transient cell states.

Cellular perturbations underlying Alzheimer's disease (AD) are primarily studied in human postmortem samples and model organisms. Here, we generated a single-nucleus atlas from a rare cohort of cortical biopsies from living individuals with varying degrees of AD pathology. We next performed a systematic cross-disease and cross-species integrative analysis to identify a set of cell states that are specific to early AD pathology. These changes-which we refer to as the early cortical amyloid response-were prominent in neurons, wherein we identified a transitional hyperactive state preceding the loss of excitatory neurons, which we confirmed by acute slice physiology on independent biopsy specimens. Microglia overexpressing neuroinflammatory-related processes also expanded as AD pathology increased. Finally, both oligodendrocytes and pyramidal neurons upregulated genes associated with ß-amyloid production and processing during this early hyperactive phase. Our integrative analysis provides an organizing framework for targeting circuit dysfunction, neuroinflammation, and amyloid production early in AD pathogenesis.