Bioengineering

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11 pages, 2946 KiB

Open AccessArticle

Predicting the Evolution of Lung Squamous Cell Carcinoma In Situ Using Computational Pathology

by Alon Vigdorovits, Gheorghe-Emilian Olteanu, Ovidiu Tica, Andrei Pascalau, Monica Boros and Ovidiu Pop

Bioengineering 2025, 12(4), 377; https://doi.org/10.3390/bioengineering12040377 - 2 Apr 2025

Viewed by 234

Abstract

Lung squamous cell carcinoma in situ (SCIS) is the preinvasive precursor lesion of lung squamous cell carcinoma (SCC). Only around two-thirds of these lesions progress to invasive cancer, while one-third undergo spontaneous regression, which presents a significant clinical challenge due to the risk [...] Read more.

Lung squamous cell carcinoma in situ (SCIS) is the preinvasive precursor lesion of lung squamous cell carcinoma (SCC). Only around two-thirds of these lesions progress to invasive cancer, while one-third undergo spontaneous regression, which presents a significant clinical challenge due to the risk of overtreatment. The ability to predict the evolution of SCIS lesions can significantly impact patient management. Our study explores the use of computational pathology in predicting the evolution of SCIS. We used a dataset consisting of 112 H&E-stained whole slide images (WSIs) that were obtained from the Image Data Resource public repository. The dataset corresponded to tumors of patients who underwent biopsies of SCIS lesions and were subsequently followed up by bronchoscopy and CT scans to monitor for progression to SCC. We used this dataset to train two models: a pathomics-based ridge classifier trained on 80 principal components derived from almost 2000 extracted features and a deep convolutional neural network with a modified ResNet18 architecture. The performance of both approaches in predicting progression was assessed. The pathomics-based ridge classifier model obtained an F1-score of 0.77, precision of 0.80, and recall of 0.77. The deep learning model performance was similar, with a WSI-level F1-score of 0.80, precision of 0.71, and recall of 0.90. These findings highlight the potential of computational pathology approaches in providing insights into the evolution of SCIS. Larger datasets will be required in order to train highly accurate models. In the future, computational pathology could be used in predicting outcomes in other preinvasive lesions. Full article

(This article belongs to the Special Issue Computational Pathology and Artificial Intelligence)

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16 pages, 7989 KiB

Open AccessArticle

Glioma Image-Level and Slide-Level Gene Predictor (GLISP) for Molecular Diagnosis and Predicting Genetic Events of Adult Diffuse Glioma

by Minh-Khang Le, Masataka Kawai, Kenta Masui, Takashi Komori, Takakazu Kawamata, Yoshihiro Muragaki, Tomohiro Inoue, Ippei Tahara, Kazunari Kasai and Tetsuo Kondo

Bioengineering 2025, 12(1), 12; https://doi.org/10.3390/bioengineering12010012 - 27 Dec 2024

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Abstract

The latest World Health Organization (WHO) classification of central nervous system tumors (WHO2021/5th) has incorporated molecular information into the diagnosis of each brain tumor type including diffuse glioma. Therefore, an artificial intelligence (AI) framework for learning histological patterns and predicting important genetic events [...] Read more.

The latest World Health Organization (WHO) classification of central nervous system tumors (WHO2021/5th) has incorporated molecular information into the diagnosis of each brain tumor type including diffuse glioma. Therefore, an artificial intelligence (AI) framework for learning histological patterns and predicting important genetic events would be useful for future studies and applications. Using the concept of multiple-instance learning, we developed an AI framework named GLioma Image-level and Slide-level gene Predictor (GLISP) to predict nine genetic abnormalities in hematoxylin and eosin sections: IDH1/2, ATRX, TP53 mutations, TERT promoter mutations, CDKN2A/B homozygous deletion (CHD), EGFR amplification (EGFRamp), 7 gain/10 loss (7+/10−), 1p/19q co-deletion, and MGMT promoter methylation. GLISP consists of a pair of patch-level GLISP-P and patient-level GLISP-W models, each pair of which is for a genetic prediction task, providing flexibility in clinical utility. In this study, the Cancer Genome Atlas whole-slide images (WSIs) were used to train the model. A total of 108 WSIs from the Tokyo Women’s Medical University were used as the external dataset. In cross-validation, GLISP yielded patch-level/case-level predictions with top performances in IDH1/2 and 1p/19q co-deletion with average areas under the curve (AUCs) of receiver operating characteristics of 0.75/0.79 and 0.73/0.80, respectively. In external validation, the patch-level/case-level AUCs of IDH1/2 and 1p/19q co-deletion detection were 0.76/0.83 and 0.78/0.88, respectively. The accuracy in diagnosing IDH-mutant astrocytoma, oligodendroglioma, and IDH-wild-type glioblastoma was 0.66, surpassing the human pathologist average of 0.62 (0.54–0.67). In conclusion, GLISP is a two-stage AI framework for histology-based prediction of genetic events in adult gliomas, which is helpful in providing essential information for WHO 2021 molecular diagnoses. Full article

(This article belongs to the Special Issue Computational Pathology and Artificial Intelligence)

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13 pages, 2319 KiB

Open AccessArticle

Nuclei Detection and Segmentation of Histopathological Images Using a Feature Pyramidal Network Variant of a Mask R-CNN

by Vignesh Ramakrishnan, Annalena Artinger, Laura Alexandra Daza Barragan, Jimmy Daza, Lina Winter, Tanja Niedermair, Timo Itzel, Pablo Arbelaez, Andreas Teufel, Cristina L. Cotarelo and Christoph Brochhausen

Bioengineering 2024, 11(10), 994; https://doi.org/10.3390/bioengineering11100994 - 1 Oct 2024

Cited by 1 | Viewed by 1674

Abstract

Cell nuclei interpretation is crucial in pathological diagnostics, especially in tumor specimens. A critical step in computational pathology is to detect and analyze individual nuclear properties using segmentation algorithms. Conventionally, a semantic segmentation network is used, where individual nuclear properties are derived after [...] Read more.

Cell nuclei interpretation is crucial in pathological diagnostics, especially in tumor specimens. A critical step in computational pathology is to detect and analyze individual nuclear properties using segmentation algorithms. Conventionally, a semantic segmentation network is used, where individual nuclear properties are derived after post-processing a segmentation mask. In this study, we focus on showing that an object-detection-based instance segmentation network, the Mask R-CNN, after integrating it with a Feature Pyramidal Network (FPN), gives mature and reliable results for nuclei detection without the need for additional post-processing. The results were analyzed using the Kumar dataset, a public dataset with over 20,000 nuclei annotations from various organs. The dice score of the baseline Mask R-CNN improved from 76% to 83% after integration with an FPN. This was comparable with the 82.6% dice score achieved by modern semantic-segmentation-based networks. Thus, evidence is provided that an end-to-end trainable detection-based instance segmentation algorithm with minimal post-processing steps can reliably be used for the detection and analysis of individual nuclear properties. This represents a relevant task for research and diagnosis in digital pathology, which can improve the automated analysis of histopathological images. Full article

(This article belongs to the Special Issue Computational Pathology and Artificial Intelligence)

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11 pages, 1073 KiB

Open AccessArticle

Multi-Scale Digital Pathology Patch-Level Prostate Cancer Grading Using Deep Learning: Use Case Evaluation of DiagSet Dataset

by Tanaya Kondejkar, Salah Mohammed Awad Al-Heejawi, Anne Breggia, Bilal Ahmad, Robert Christman, Stephen T. Ryan and Saeed Amal

Bioengineering 2024, 11(6), 624; https://doi.org/10.3390/bioengineering11060624 - 18 Jun 2024

Cited by 1 | Viewed by 1694

Abstract

Prostate cancer remains a prevalent health concern, emphasizing the critical need for early diagnosis and precise treatment strategies to mitigate mortality rates. The accurate prediction of cancer grade is paramount for timely interventions. This paper introduces an approach to prostate cancer grading, framing [...] Read more.

Prostate cancer remains a prevalent health concern, emphasizing the critical need for early diagnosis and precise treatment strategies to mitigate mortality rates. The accurate prediction of cancer grade is paramount for timely interventions. This paper introduces an approach to prostate cancer grading, framing it as a classification problem. Leveraging ResNet models on multi-scale patch-level digital pathology and the Diagset dataset, the proposed method demonstrates notable success, achieving an accuracy of 0.999 in identifying clinically significant prostate cancer. The study contributes to the evolving landscape of cancer diagnostics, offering a promising avenue for improved grading accuracy and, consequently, more effective treatment planning. By integrating innovative deep learning techniques with comprehensive datasets, our approach represents a step forward in the pursuit of personalized and targeted cancer care. Full article

(This article belongs to the Special Issue Computational Pathology and Artificial Intelligence)

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12 pages, 5124 KiB

Open AccessArticle

MurSS: A Multi-Resolution Selective Segmentation Model for Breast Cancer

by Joonho Lee, Geongyu Lee, Tae-Yeong Kwak, Sun Woo Kim, Min-Sun Jin, Chungyeul Kim and Hyeyoon Chang

Bioengineering 2024, 11(5), 463; https://doi.org/10.3390/bioengineering11050463 - 7 May 2024

Cited by 4 | Viewed by 1878

Abstract

Accurately segmenting cancer lesions is essential for effective personalized treatment and enhanced patient outcomes. We propose a multi-resolution selective segmentation (MurSS) model to accurately segment breast cancer lesions from hematoxylin and eosin (H&E) stained whole-slide images (WSIs). We used The Cancer Genome Atlas [...] Read more.

Accurately segmenting cancer lesions is essential for effective personalized treatment and enhanced patient outcomes. We propose a multi-resolution selective segmentation (MurSS) model to accurately segment breast cancer lesions from hematoxylin and eosin (H&E) stained whole-slide images (WSIs). We used The Cancer Genome Atlas breast invasive carcinoma (BRCA) public dataset for training and validation. We used the Korea University Medical Center, Guro Hospital, BRCA dataset for the final test evaluation. MurSS utilizes both low- and high-resolution patches to leverage multi-resolution features using adaptive instance normalization. This enhances segmentation performance while employing a selective segmentation method to automatically reject ambiguous tissue regions, ensuring stable training. MurSS rejects 5% of WSI regions and achieves a pixel-level accuracy of 96.88% (95% confidence interval (CI): 95.97–97.62%) and mean Intersection over Union of 0.7283 (95% CI: 0.6865–0.7640). In our study, MurSS exhibits superior performance over other deep learning models, showcasing its ability to reject ambiguous areas identified by expert annotations while using multi-resolution inputs. Full article

(This article belongs to the Special Issue Computational Pathology and Artificial Intelligence)

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14 pages, 7317 KiB

Open AccessArticle

Enhanced Nuclei Segmentation and Classification via Category Descriptors in the SAM Model

by Miguel Luna, Philip Chikontwe and Sang Hyun Park

Bioengineering 2024, 11(3), 294; https://doi.org/10.3390/bioengineering11030294 - 21 Mar 2024

Viewed by 2489

Abstract

Segmenting and classifying nuclei in H&E histopathology images is often limited by the long-tailed distribution of nuclei types. However, the strong generalization ability of image segmentation foundation models like the Segment Anything Model (SAM) can help improve the detection quality of rare types [...] Read more.

Segmenting and classifying nuclei in H&E histopathology images is often limited by the long-tailed distribution of nuclei types. However, the strong generalization ability of image segmentation foundation models like the Segment Anything Model (SAM) can help improve the detection quality of rare types of nuclei. In this work, we introduce category descriptors to perform nuclei segmentation and classification by prompting the SAM model. We close the domain gap between histopathology and natural scene images by aligning features in low-level space while preserving the high-level representations of SAM. We performed extensive experiments on the Lizard dataset, validating the ability of our model to perform automatic nuclei segmentation and classification, especially for rare nuclei types, where achieved a significant detection improvement in the F1 score of up to 12%. Our model also maintains compatibility with manual point prompts for interactive refinement during inference without requiring any additional training. Full article

(This article belongs to the Special Issue Computational Pathology and Artificial Intelligence)

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Review

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16 pages, 1271 KiB

Open AccessReview

Challenges and Opportunities in Cytopathology Artificial Intelligence

by Meredith A. VandeHaar, Hussien Al-Asi, Fatih Doganay, Ibrahim Yilmaz, Heba Alazab, Yao Xiao, Jagadheshwar Balan, Bryan J. Dangott, Aziza Nassar, Jordan P. Reynolds and Zeynettin Akkus

Bioengineering 2025, 12(2), 176; https://doi.org/10.3390/bioengineering12020176 - 13 Feb 2025

Viewed by 1094

Abstract

Artificial Intelligence (AI) has the potential to revolutionize cytopathology by enhancing diagnostic accuracy, efficiency, and accessibility. However, the implementation of AI in this field presents significant challenges and opportunities. This review paper explores the current landscape of AI applications in cytopathology, highlighting the [...] Read more.

Artificial Intelligence (AI) has the potential to revolutionize cytopathology by enhancing diagnostic accuracy, efficiency, and accessibility. However, the implementation of AI in this field presents significant challenges and opportunities. This review paper explores the current landscape of AI applications in cytopathology, highlighting the critical challenges, including data quality and availability, algorithm development, integration and standardization, and clinical validation. We discuss challenges such as the limitation of only one optical section and z-stack scanning, the complexities associated with acquiring high-quality labeled data, the intricacies of developing robust and generalizable AI models, and the difficulties in integrating AI tools into existing laboratory workflows. The review also identifies substantial opportunities that AI brings to cytopathology. These include the potential for improved diagnostic accuracy through enhanced detection capabilities and consistent, reproducible results, which can reduce observer variability. AI-driven automation of routine tasks can significantly increase efficiency, allowing cytopathologists to focus on more complex analyses. Furthermore, AI can serve as a valuable educational tool, augmenting the training of cytopathologists and facilitating global health initiatives by making high-quality diagnostics accessible in resource-limited settings. The review underscores the importance of addressing these challenges to harness the full potential of AI in cytopathology, ultimately improving patient care and outcomes. Full article

(This article belongs to the Special Issue Computational Pathology and Artificial Intelligence)

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13 pages, 1237 KiB

Open AccessReview

Applications of Large Language Models in Pathology

by Jerome Cheng

Bioengineering 2024, 11(4), 342; https://doi.org/10.3390/bioengineering11040342 - 31 Mar 2024

Cited by 10 | Viewed by 4940

Abstract

Large language models (LLMs) are transformer-based neural networks that can provide human-like responses to questions and instructions. LLMs can generate educational material, summarize text, extract structured data from free text, create reports, write programs, and potentially assist in case sign-out. LLMs combined with [...] Read more.

Large language models (LLMs) are transformer-based neural networks that can provide human-like responses to questions and instructions. LLMs can generate educational material, summarize text, extract structured data from free text, create reports, write programs, and potentially assist in case sign-out. LLMs combined with vision models can assist in interpreting histopathology images. LLMs have immense potential in transforming pathology practice and education, but these models are not infallible, so any artificial intelligence generated content must be verified with reputable sources. Caution must be exercised on how these models are integrated into clinical practice, as these models can produce hallucinations and incorrect results, and an over-reliance on artificial intelligence may lead to de-skilling and automation bias. This review paper provides a brief history of LLMs and highlights several use cases for LLMs in the field of pathology. Full article

(This article belongs to the Special Issue Computational Pathology and Artificial Intelligence)

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