(C) PLOS One This story was originally published by PLOS One and is unaltered. . . . . . . . . . . The tumour histopathology “glossary” for AI developers [1] ['Soham Mandal', 'Centre For Evolution', 'Cancer', 'Institute Of Cancer Research', 'London', 'United Kingdom', 'Data Science Team', 'Ann-Marie Baker', 'Trevor A. Graham', 'Konstantin Bräutigam'] Date: 2025-07 The applications of artificial intelligence (AI) and deep learning (DL) are leading to significant advances in cancer research, particularly in analysing histopathology images for prognostic and treatment-predictive insights. However, effective translation of these computational methods requires computational researchers to have at least a basic understanding of histopathology. In this work, we aim to bridge that gap by introducing essential histopathology concepts to support AI developers in their research. We cover the defining features of key cell types, including epithelial, stromal, and immune cells. The concepts of malignancy, precursor lesions, and the tumour microenvironment (TME) are discussed and illustrated. To enhance understanding, we also introduce foundational histopathology techniques, such as conventional staining with hematoxylin and eosin (HE), antibody staining by immunohistochemistry, and including the new multiplexed antibody staining methods. By providing this essential knowledge to the computational community, we aim to accelerate the development of AI algorithms for cancer research. Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: TAG and AMB are named as coinventors on a patent application that describe a method for TCR sequencing (GB2305655.9), and TAG is named on a method to measure evolutionary dynamics in cancers using DNA methylation (GB2317139.0). TAG has received honorarium from Genentech and DAiNA therapeutics. Funding: TAG and AMB are funded by Cancer Research UK (DRCNPG-May21_100001). KB was funded by the Swiss National Science Foundation (P500PM_217647 / 1). SM acknowledges funding from the Institute of Cancer Research's Data Science Initiative. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 Mandal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. While recently published literature aims at bestowing (cancer) healthcare professionals with expertise on AI, e.g., developing image analysis and modelling skills for clinicians [ 20 , 21 ], the opposite—giving AI developers an understanding of cancer histopathology fundamentals—is rare. Thus, in this work we introduce some of the essential concepts of tumour histopathology—the most frequent cell types, the concepts of “neoplasia,” “tumours,” and the tumour microenvironment (TME). We also illustrate routine histopathology protocols and special stainings that provide the visual representation of the above concepts. This article aims to bridge the gap between the AI development and the translation to routine clinical application by emphasising the importance of relevant biological knowledge, which would be helpful in enhancing model interpretability and the subsequent clinical validation. Only solid biological understanding would enable modellers to define sets of relevant features and implement their morphological properties into algorithms. Recent advancements in AI research have significantly automated a range of diagnostic (defining disease), predictive (predicting the response to a certain treatment [ 12 ]), and prognostic (stratifying patients at risk and determining outcome/patient prognosis) tasks in oncology [ 13 – 15 ]. However, these models often operate as “black boxes,” providing little transparency regarding their decision-making processes. Physicians, on the other hand, base their diagnoses on well-defined biological features and established criteria [ 16 ], especially in the case of histopathologists, whose cornerstone is tissue and cell morphology [ 17 ]. Therefore, the interpretability of both the design and the results of AI-based methods are crucial for gaining the trust and acceptance of the medical community and accelerating the clinical implementation of in-silico approaches. A good level of understanding of tumour biology instructs AI developers to incorporate relevant biological features into their computational models [ 18 ]. This understanding not only improves the accuracy and relevance of the models but also facilitates the presentation of results in a manner that physicians can validate and understand. For example, a study on early-stage oestrogen receptor-positive (ER+) breast cancer demonstrated how considering relevant biological features is crucial in the development of AI methods for survival prediction [ 19 ]. The method described in this work leveraged understanding of nuclear pleomorphism (variance in the appearance of the cell nucleus), which is a crucial factor in breast cancer grading. Building accurate AI models for medicine is an interdisciplinary task [ 8 ] and requires a complement of different expertise, i.e., computational and mathematical skills combined with clinical knowledge. This is often hampered by computer scientists and biologists/expert healthcare professionals not physically working in the same environment, where they cannot easily access the other’s expertise. The lack of interdisciplinary communication results in significant inefficiencies and misunderstandings, leading many AI models to remain prototypes rather than being integrated into clinical practice [ 9 , 10 ]. In light of the rise of AI in the clinic, teaching medics AI basics has become essential [ 11 ]; here, we propose that a similarly reciprocal understanding of biology is important for AI developers to build better models. The last decades have experienced a huge rise in the application of bioinformatics, artificial intelligence (AI), and machine learning (ML) in cancer research [ 1 – 3 ]. A plethora of AI tools have been developed specifically for histopathology, which aim to improve its diagnostic reliability and accuracy [ 4 – 6 ]. Tissue, i.e., the sum and interplay of cells, and cellular morphologies are the key variables in histopathology [ 7 ] and are the foundation for any supervised modelling attempt. Thus, we believe that successful AI development requires an understanding of tumour histology. Concepts of tumour histopathology and representative examples of modelling them In order to model disease, firstly, a solid understanding of cell types, their physiological function, overall architecture and interplay with other cells is necessary. Parameters for image analysis and neural network training are best derived by applying knowledge of their defining morphology and distinct, if not unique, individual features (the common proverb of “the eyes can’t see what the mind doesn’t know” applies). In the first section, we introduce different cell types and the concept of “neoplasia.” What are “tumours”? “Tumour” (Latin for “swelling”) is an ill-defined term, in principle designating an increase in tissue volume. It refers to a neoplastic process (“neoplasia” being the abnormal and excessive growth of cells and tissue), whose biological “potential” is in most cases dichotomously classified as either benign (localised without metastatic potential, e.g., a minute hyperplastic polyp in the colon) or malignant (invading neighbouring tissue and/or moving to distant organs, e.g., colorectal cancer). The cells of origin for a neoplasia can be classed as epithelial, lymphoid (blood cells), or mesenchymal (connective tissue). Understandably, a large number of AI models focus on the most frequent cancer types [27], which are epithelial in origin and solid, i.e., mass-forming. Non-solid neoplasia are, for instance, cancers of the blood system, which are not localised and not confined to a single organ, e.g., leukemia where neoplastic cells are in circulation in the blood. In the following, we concentrate on solid neoplasia, due to its epidemiologic relevance and localised anatomy. Histologically, solid neoplasia is composed of the tumour parenchyma, i.e., the neoplastic cells themselves, and the tumour stroma (Fig 2A and 2B). The tumour stroma has gained more and more attention in cancer research [28], and is mainly composed of cells of the tumour microenvironment (TME, see below), extracellular matrix (structural and specialised proteins surrounding units of cancer cells), and connective tissue (mainly collagen fibres, fibroblasts, and microvessels). PPT PowerPoint slide PNG larger image TIFF original image Download: Fig 2. The morphology of “cancer,” HE. (A) Nonneoplastic epithelium (left) versus adenocarcinoma glands/cells (right, separated by dotted line) in a The Cancer Genome Atlas (TCGA) sample of colorectal adenocarcinoma (COAD, TCGA-A6-2678). Dashed inset (corresponding to the dashed rectangle): Higher magnification showing malignant COAD glands with malignant nuclear features and a focus of “dirty” necrosis within the cancer gland (asterisk). Insets: On the one hand, enterocytes (e, colonic epithelium) with regular contours, polarised towards the lumen with a goblet (mucus-containing) cell (G) on top. On the other hand, cancer cell (c) with a prominent nucleolus (cross), large size and irregular contours. Arrow: Regular colonic crypts, rich in goblet cells (arrow on goblet cell). Arrowhead: High-grade dysplasia at the surface mucosa. (B) Tumour parenchyma (red) and tumour stroma (blue, green) in pancreatic adenocarcinoma (PAAD, TCGA-2J-AAB6). PAAD glands are surrounded by dense “desmoplasia” (blue, inset) with pinkish collagen deposition (black arrowhead) and immune cell infiltrate (green, inset), mainly lymphocytes (white arrowheads) among microvessels (arrows). (C) Phenotype of invasion. From left to right: Invasive breast cancer (BRCA) clusters (BRCA, TCGA-3C-AALJ), prostatic adenocarcinoma (PRAD, TCGA-EJ-7792) glands with loss of basal cell layer (arrows: benign prostatic parenchyma), invasive squamous cell carcinoma of the cervix (CESC, TCGA-C5-A7XC) with adjacent high-grade squamous intraepithelial lesion (HSIL, white asterisk) and of the head and neck region (HNSC, TCGA-BA-4076). See Table 2 and section “The hematoxylin and eosin (HE) stain” for further description. Asterisks: Surface squamous epithelium, arrowheads: invasive SC. Crosses: Extracellular matrix/stroma between BRCA clusters. https://doi.org/10.1371/journal.pcbi.1012708.g002 The “face” of malignancy—Morphology and pitfalls Not every malignancy follows the conventional benign-intermediate-malignant trajectory described above, examples being de novo (i.e., not arising from a precursor tumour, such as a skin mole (“naevus”)) malignant melanoma [34] or sarcoma (relatively rare malignant tumours of the soft tissue or bone). Briefly, malignant tumours can be classified according to their cell(s) of origin, e.g., epithelial, mesenchymal (simply put, connective tissue), or lymphoid (see above). It’s reasonable to assume that for developers and the healthcare system, epidemiologically frequent malignant tumours are most relevant, these being malignant epithelial tumours, namely (adeno)carcinomas, e.g., of the prostate, breast, colon (rectum), and lung [21]. Adenocarcinomas are a morphological (and also molecularly distinct) subset of carcinomas (e.g., in contrast to squamous cell carcinomas) and have a typical morphology with gland-like (i.e., circularly arranged with a central lumen) growth with malignant nuclear features such as bizarre cell forms, mitotic figures, and a prominent nucleolus (Fig 2A); however, there are always exceptions and this can make modelling tricky and algorithms incompatible with routine diagnostic practice. Illustrative exceptions are deviant subtypes or atypical cancer growth patterns, such as high-grade prostatic adenocarcinoma with diffuse growth [35], or invasive-lobular breast cancer with discohesive tumour cells [36]. Not incorporating these features into algorithms is severely limiting and might jeopardise clinical conclusion from a computer-assisted diagnostic setup. Non-small cell lung cancer (NSCLC), second most frequent cancer in both sexes in the United States, has a known multitude of growth patterns [37] and also colorectal cancer can exhibit a very deviant morphology, even if rare [38]. In addition, adenocarcinoma is frequently accompanied by a strong stromal response (“desmoplasia”), which can be seen by collagen deposition and extracellular matrix (ECM) recomposition [39] (Fig 2B and Table 2). Squamous cell carcinoma, most prominently in the head and neck region (Fig 2C), genital tract (cervical cancer, anal cancer) and lung is characterised by keratin “pearls” (whorl-shaped accumulations of keratin, a structural protein), and intercellular bridges (specialised connections between adjacent cells). It has to be kept in mind that even very typical diagnostic features might not be apparent in poorly differentiated tumours which have lost much of their resemblance to the tissue of origin (“dedifferentiation” is the process of losing tissue specialisation, returning to a less specialised state). Mixed carcinomas, e.g., adenosquamous, adeno-neuroendocrine (e.g., mixed neuroendocrine-nonneuroendocrine neoplasms [40]), add further to the complexity. Lastly, malignancy of other lineages, e.g., non-epithelial shows a considerable amount of variation, too. In particular, malignant melanoma is known for its plethora of “morphological faces” [41]. [END] --- [1] Url: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012708 Published and (C) by PLOS One Content appears here under this condition or license: Creative Commons - Attribution BY 4.0. via Magical.Fish Gopher News Feeds: gopher://magical.fish/1/feeds/news/plosone/