Despite recent advances, the adoption of computer vision methods into clinical and commercial applications has been hampered by the limited availability of accurate ground truth tissue annotations required to train robust supervised models. Generating such ground truth can be accelerated by annotating tissue molecularly using immunofluorescence staining (IF) and mapping these annotations to a post-IF H&E (terminal H&E). Mapping the annotations between the IF and the terminal H&E increases both the scale and accuracy by which ground truth could be generated. However, discrepancies between terminal H&E and conventional H&E caused by IF tissue processing have limited this implementation. We sought to overcome this challenge and achieve compatibility between these parallel modalities using synthetic image generation, in which a cycle-consistent generative adversarial network (CycleGAN) was applied to transfer the appearance of conventional H&E such that it emulates the terminal H&E. These synthetic emulations allowed us to train a deep learning (DL) model for the segmentation of epithelium in the terminal H&E that could be validated against the IF staining of epithelial-based cytokeratins. The combination of this segmentation model with the CycleGAN stain transfer model enabled performative epithelium segmentation in conventional H&E images. The approach demonstrates that the training of accurate segmentation models for the breadth of conventional H&E data can be executed free of human-expert annotations by leveraging molecular annotation strategies such as IF, so long as the tissue impacts of the molecular annotation protocol are captured by generative models that can be deployed prior to the segmentation process.

OBJECTIVE: This review summarizes the current and potential uses of artificial intelligence (AI) in the current state of clinical microbiology with a focus on replacement of labor-intensive tasks.
METHODS: A search was conducted on PubMed using the key terms clinical microbiology and artificial intelligence. Studies were reviewed for relevance to clinical microbiology, current diagnostic techniques, and potential advantages of AI in routine microbiology workflows.
RESULTS: Numerous studies highlight potential labor, as well as diagnostic accuracy, benefits to the implementation of AI for slide-based and macroscopic digital image analyses. These range from Gram stain interpretation to categorization and quantitation of culture growth.
CONCLUSIONS: Artificial intelligence applications in clinical microbiology significantly enhance diagnostic accuracy and efficiency, offering promising solutions to labor-intensive tasks and staffing shortages. More research efforts and US Food and Drug Administration clearance are still required to fully incorporate these AI applications into routine clinical laboratory practices.

Deep learning techniques offer improvements in computer-aided diagnosis systems. However, acquiring image domain annotations is challenging due to the knowledge and commitment required of expert pathologists. Pathologists often identify regions in whole slide images with diagnostic relevance rather than examining the entire slide, with a positive correlation between the time spent on these critical image regions and diagnostic accuracy. In this paper, a heatmap is generated to represent pathologists\' viewing patterns during diagnosis and used to guide a deep learning architecture during training. The proposed system outperforms traditional approaches based on color and texture image characteristics, integrating pathologists\' domain expertise to enhance region of interest detection without needing individual case annotations. Evaluating our best model, a U-Net model with a pre-trained ResNet-18 encoder, on a skin biopsy whole slide image dataset for melanoma diagnosis, shows its potential in detecting regions of interest, surpassing conventional methods with an increase of 20%, 11%, 22%, and 12% in precision, recall, F1-score, and Intersection over Union, respectively. In a clinical evaluation, three dermatopathologists agreed on the model\'s effectiveness in replicating pathologists\' diagnostic viewing behavior and accurately identifying critical regions. Finally, our study demonstrates that incorporating heatmaps as supplementary signals can enhance the performance of computer-aided diagnosis systems. Without the availability of eye tracking data, identifying precise focus areas is challenging, but our approach shows promise in assisting pathologists in improving diagnostic accuracy and efficiency, streamlining annotation processes, and aiding the training of new pathologists.

BACKGROUND: Neoadjuvant cisplatin-based chemotherapy (NAC) followed by cystectomy is the standard of care for patients with muscle-invasive bladder cancer (MIBC). Pathologic complete response (pCR) is associated with favorable outcomes, but only 30%-40% of patients achieve that response. The aim of this study is to investigate the role played by the Tumor and Immune Microenvironment (TIME) in association with the clinical outcome of patients with MIBC undergoing NAC.
METHODS: Nineteen patients received NAC and were classified as pCR (n = 10) or non-pCR (n = 9). Bulk RNA-seq and immune protein evaluations using Digital Spatial Profiling (DSP) were performed on formalin-fixed paraffin-embedded (FFPE) tumor biopsies collected before NAC (baseline). Immunohistochemistry (IHC) evaluation focused on CD3 and CD20 expression was performed on baseline and end-of-treatment (EOT) FFPEs. Baseline peripheral blood was assessed for lymphocyte and neutrophil counts. Kaplan-Meier analyses and Cox PH regression models were used for survival analyses (OS).
RESULTS: In the periphery, pCR patients showed lower neutrophil counts, and neutrophil/ lymphocyte ratio (NLR) when compared to non-pCR patients. In the tumor microenvironment (TME), gene expression analysis and protein evaluations highlighted an abundance of B cells and CD3+ T cells in pCR versus non-pCR patients. On the contrary, increased protein expression of ARG1+ cells, and cells expressing immune checkpoints such as LAG3, ICOS, and STING were observed in the TME of patients with non-pCR.
CONCLUSIONS: In the current study, we demonstrated that lower NLR levels and increased CD3+ T cells and B cell infiltration are associated with improved response and long-term outcomes in patients with MIBC receiving NAC. These findings suggest that the impact of immune environment should be considered in determining the clinical outcome of MIBC patients treated with NAC.

OBJECTIVE: Classical Hirschsprung disease (HD) is defined by the absence of ganglion cells in the rectosigmoid colon. The diagnosis is made from rectal biopsy, which reveals the aganglionosis and the presence of cholinergic hyperinnervation. However, depending on the method of rectal biopsy, the quality of the specimens and the related diagnostic accuracy varies substantially. To facilitate and objectify the diagnosis of HD, we investigated whether software-based identification of cholinergic hyperinnervation in digitalized histopathology slides is suitable for distinguishing healthy individuals from affected individuals.
METHODS: N = 190 samples of 112 patients who underwent open surgical rectal biopsy at our pediatric surgery center between 2009 and 2019 were included in this study. Acetylcholinesterase (AChE) stained slides of these samples were collected and digitalized via slide scanning and analyzed using two digital imaging software programs (HALO, QuPath). The AChE-positive staining area in the mucosal layers of the intestinal wall was determined. In the next step machine learning was employed to identify patterns of cholinergic hyperinnervation.
RESULTS: The area of AChE-positive staining was greater in HD patients compared to healthy individuals (p < 0.0001). Artificial intelligence-based assessment of parasympathetic hyperinnervation identified Hirschsprung disease with a high precision (area under the curve [AUC] 0.96). The accuracy of the prediction model increased when nonrectal samples were excluded (AUC 0.993).
CONCLUSIONS: Software-assisted machine-learning analysis of AChE staining is suitable to improve the diagnostic accuracy of Hirschsprung disease.

BACKGROUND: Sjögren\'s Syndrome (SS) is a rare chronic autoimmune disorder primarily affecting adult females, characterized by chronic inflammation and salivary and lacrimal gland dysfunction. It is often associated with systemic lupus erythematosus, rheumatoid arthritis and kidney disease, which can lead to increased mortality. Early diagnosis is critical, but traditional methods for diagnosing SS, mainly through histopathological evaluation of salivary gland tissue, have limitations.
METHODS: The study used 100 labial gland biopsy, creating whole-slide images (WSIs) for analysis. The proposed model, named Cell-tissue-graph-based pathological image analysis model (CTG-PAM) and based on graph theory, characterizes single-cell feature, cell-cell feature, and cell-tissue feature. Building upon these features, CTG-PAM achieves cellular-level classification, enabling lymphocyte recognition. Furthermore, it leverages connected component analysis techniques in the cell graph structure to perform SS diagnosis based on lymphocyte counts.
RESULTS: CTG-PAM outperforms traditional deep learning methods in diagnosing SS. Its area under the receiver operating characteristic curve (AUC) is 1.0 for the internal validation dataset and 0.8035 for the external test dataset. This indicates high accuracy. The sensitivity of CTG-PAM for the external dataset is 98.21%, while the accuracy is 93.75%. In comparison, the sensitivity and accuracy for traditional deep learning methods (ResNet-50) are lower. The study also shows that CTG-PAM\'s diagnostic accuracy is closer to skilled pathologists compared to beginners.
CONCLUSIONS: Our findings indicate that CTG-PAM is a reliable method for diagnosing SS. Additionally, CTG-PAM shows promise in enhancing the prognosis of SS patients and holds significant potential for the differential diagnosis of both non-neoplastic and neoplastic diseases. The AI model potentially extends its application to diagnosing immune cells in tumor microenvironments.

The quantitation of plasma cells in bone marrow (BM) is crucial for diagnosing and classifying plasma cell neoplasms. Various methods, including Romanowsky-stained BM aspirates (BMA), immunohistochemistry, flow cytometry, and radiological imaging, have been explored. However, challenges such as patchy infiltration and sample haemodilution can impact the reliability of BM plasma cell percentage estimates. Bone marrow plasma cell percentage varies across methods, with immunohistochemically stained biopsies consistently yielding higher values than Romanowsky-stained BMA or flow cytometry alone. CD138 or MUM1 immunohistochemistry and artificial intelligence image analysis on whole-slide images are emerging as promising tools for accurate plasma cell identification and quantification. Radiological imaging, particularly with advanced technologies like dual-energy computed tomography and radiomics, shows potential for multiple myeloma diagnosis, although standardisation remains a challenge. Molecular techniques, such as allele-specific oligonucleotide quantitative polymerase chain reaction and next-generation sequencing, offer insights into clonality and measurable residual disease. While no consensus exists on a gold standard method for BM plasma cell quantitation, CD138-stained biopsies are favoured for accurate estimation and play a pivotal role in diagnosing and assessing multiple myeloma treatment responses. Combining multiple methods, such as BMA, BM biopsy, and flow cytometry, enhances accuracy of diagnosis and classification of plasma cell neoplasms. The quest for a gold standard requires ongoing research and collaboration to refine existing methods. Furthermore, the rise of digital pathology is anticipated to reshape laboratory medicine and the role of pathologists in the digital era.
UNASSIGNED: This article adds a comprehensive review and comparison of different methods for plasma cell estimation in the bone marrow, highlighting their strengths and limitations. The goal is to contribute valuable insights that can guide the selection of optimal techniques for accurate plasma cell estimation.

Advances in whole-slide imaging and artificial intelligence present opportunities for improvement in Pap test screening. To date, there have been limited studies published regarding how best to validate newer AI-based digital systems for screening Pap tests in clinical practice. In this study, we validated the Genius™ Digital Diagnostics System (Hologic) by comparing the performance to traditional manual light microscopic diagnosis of ThinPrep® Pap test slides. A total of 319 ThinPrep® Pap test cases were prospectively assessed by six cytologists and three cytopathologists by light microscopy and digital evaluation and the results compared to the original ground truth Pap test diagnosis. Concordance with the original diagnosis was significantly different by digital and manual light microscopy review when comparing across: (i) exact Bethesda System diagnostic categories (62.1% vs 55.8%, respectively, p = 0.014), (ii) condensed diagnostic categories (76.8% vs 71.5%, respectively, p = 0.027), and (iii) condensed diagnoses based on clinical management (71.5% vs 65.2%, respectively, p = 0.017). Time to evaluate cases was shorter for digital (M = 3.2 min, SD = 2.2) compared to manual (M = 5.9 min, SD = 3.1) review (t(352) = 19.44, p < 0.001, Cohen\'s d = 1.035, 95% CI [0.905, 1.164]). Not only did our validation study demonstrate that AI-based digital Pap test evaluation had improved diagnostic accuracy and reduced screening time compared to light microscopy, but that participants reported a positive experience using this system.

The assessment of chemotherapy response in osteosarcoma (OS), based on the average percentage of viable cells, is limited as it overlooks the spatial heterogeneity of tumor cell response (foci of resistant cells), immune microenvironment and bone microarchitecture. Despite the resulting positive classification for response to chemotherapy, some patients experience early metastatic recurrence, demonstrating that our conventional tools for evaluating treatment response are insufficient. We studied the interactions between tumor cells, immune cells (lymphocytes, histiocytes, osteoclasts), and bone extracellular matrix (ECM) in 18 surgical resection samples of osteosarcoma using multiplex and conventional immunohistochemistry (CD8, CD163, CD68, SATB2), combined with multi-scale characterization approaches in territories of good and poor response (GRT/PRT) to treatment. GRT and PRT were defined as subregions with <10% and ≥10% of viable tumor cells, respectively. Local correlations between bone ECM porosity and density of immune cells were assessed in these territories. Immune cell density was then correlated to overall patient survival. Two patterns were identified for histiocytes and osteoclasts. In poor responder (PR) patients, CD68 osteoclast density exceeded that of CD163 histiocytes, but was not related to bone ECM load. Conversely, in good responder (GR) patients, CD163 histiocytes were more numerous than CD68 osteoclasts. For both of them, a significant negative local correlation with bone ECM porosity was found (p<0,01). Moreover, in PRT, multinucleated osteoclasts were rounded and intermingled with tumor cells, whereas in GRT they were elongated and found in close contact with bone trabeculae. CD8 levels were always low in metastatic patients and those initially considered as GR but rapidly died from their disease. The specific recruitment of histiocytes and osteoclasts within the bone ECM, and the level of CD8 represent new features of osteosarcoma response to treatment. The associated prognostic signatures should be integrated into the therapeutic stratification algorithm of patients, after surgery.

With the development of digital pathology, deep learning is increasingly being applied to endometrial cell morphology analysis for cancer screening. And cytology images with different staining may degrade the performance of these analysis algorithms. To address the impact of staining patterns, many strategies have been proposed and hematoxylin and eosin (H&E) images have been transferred to other staining styles. However, none of the existing methods are able to generate realistic cytological images with preserved cellular layout, and many important clinical structural information is lost. To address the above issues, we propose a different staining transformation model, CytoGAN, which can quickly and realistically generate images with different staining styles. It includes a novel structure preservation module that preserves the cell structure well, even if the resolution or cell size between the source and target domains do not match. Meanwhile, a stain adaptive module is designed to help the model generate realistic and high-quality endometrial cytology images. We compared our model with ten state-of-the-art stain transformation models and evaluated by two pathologists. Furthermore, in the downstream endometrial cancer classification task, our algorithm improves the robustness of the classification model on multimodal datasets, with more than 20 % improvement in accuracy. We found that generating specified specific stains from existing H&E images improves the diagnosis of endometrial cancer. Our code will be available on github.