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Artificial Intelligence in Pathological Image Analysis—2nd Edition
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Artificial Intelligence in Pathological Image Analysis—2nd Edition

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 9013

Special Issue Editor

Dr. Masayuki Tsuneki

E-Mail Website
Guest Editor
Medmain Inc, Medmain Res, Fukuoka 8100042, Japan
Interests: mathematical and theoretical approaches in pathology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In routine pathological diagnosis, histopathological and cytopathological examination of specimens is conventionally performed under light microscopy. Whole slide images (WSIs) are the digitized counterparts of conventional glass slides obtained via specialized scanning devices. In recent years, the introduction of digital pathology into clinical workflows such as intraoperative consultations and secondary consultations is increasing steadily. The advent of WSIs has led to the application of medical image analysis, machine learning, and deep learning approaches for aiding pathologists in inspecting WSIs and routine diagnosis. Deep learning in particular has found a wide array of applications (e.g., classification, segmentation, and patient outcome predictions) in computational pathology.

In a time of distinct paradigm shifts, it is necessary for us to establish unified comprehension(s) of artificial intelligence approaches in experimental and clinical pathology in light of recent technological innovations in pathology. To contribute to the knowledge base on artificial intelligence in pathology, the following topics will be considered:

  • Artificial intelligence models in clinical and experimental pathology;
  • Computer vision in pathological image analysis.

Dr. Masayuki Tsuneki
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • histopathology
  • cytopathology
  • molecular pathology
  • surgical pathology
  • digital pathology
  • microscopy
  • whole slide image (WSI)
  • clinical data
  • computer vision
  • deep learning
  • machine learning
  • computation
  • mathematics
  • domain adaptation
  • segmentation
  • classification
  • pattern recognition
  • explainable AI
  • reconstruction

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Related Special Issue

Published Papers (6 papers)

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12 pages, 2965 KiB  
Article
Deep Neural Network-Based Automatic Dicentric Chromosome Detection Using a Model Pretrained on Common Objects
by Kangsan Kim, Kwang Seok Kim, Won Il Jang, Seongjae Jang, Gil Tae Hwang and Sang-Keun Woo
Diagnostics 2023, 13(20), 3191; https://doi.org/10.3390/diagnostics13203191 - 12 Oct 2023
Viewed by 1460
Abstract
Dicentric chromosome assay (DCA) is one of the cytogenetic dosimetry methods where the absorbed dose is estimated by counting the number of dicentric chromosomes, which is a major radiation-induced change in DNA. However, DCA is a time-consuming task and requires technical expertise. In [...] Read more.
Dicentric chromosome assay (DCA) is one of the cytogenetic dosimetry methods where the absorbed dose is estimated by counting the number of dicentric chromosomes, which is a major radiation-induced change in DNA. However, DCA is a time-consuming task and requires technical expertise. In this study, a neural network was applied for automating the DCA. We used YOLOv5, a one-stage detection algorithm, to mitigate these limitations by automating the estimation of the number of dicentric chromosomes in chromosome metaphase images. YOLOv5 was pretrained on common object datasets. For training, 887 augmented chromosome images were used. We evaluated the model using validation and test datasets with 380 and 300 images, respectively. With pretrained parameters, the trained model detected chromosomes in the images with a maximum F1 score of 0.94 and a mean average precision (mAP) of 0.961. Conversely, when the model was randomly initialized, the training performance decreased, with a maximum F1 score and mAP of 0.82 and 0.873%, respectively. These results confirm that the model could effectively detect dicentric chromosomes in an image. Consequently, automatic DCA is expected to be conducted based on deep learning for object detection, requiring a relatively small amount of chromosome data for training using the pretrained network. Full article
(This article belongs to the Special Issue Artificial Intelligence in Pathological Image Analysis—2nd Edition)
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15 pages, 3056 KiB  
Article
Development of Automated Risk Stratification for Sporadic Odontogenic Keratocyst Whole Slide Images with an Attention-Based Image Sequence Analyzer
by Samahit Mohanty, Divya B. Shivanna, Roopa S. Rao, Madhusudan Astekar, Chetana Chandrashekar, Raghu Radhakrishnan, Shylaja Sanjeevareddygari, Vijayalakshmi Kotrashetti and Prashant Kumar
Diagnostics 2023, 13(23), 3539; https://doi.org/10.3390/diagnostics13233539 - 27 Nov 2023
Cited by 1 | Viewed by 1139
Abstract
(1) Background: The categorization of recurrent and non-recurrent odontogenic keratocyst is complex and challenging for both clinicians and pathologists. What sets this cyst apart is its aggressive nature and high likelihood of recurrence. Despite identifying various predictive clinical/radiological/histopathological parameters, clinicians still face difficulties [...] Read more.
(1) Background: The categorization of recurrent and non-recurrent odontogenic keratocyst is complex and challenging for both clinicians and pathologists. What sets this cyst apart is its aggressive nature and high likelihood of recurrence. Despite identifying various predictive clinical/radiological/histopathological parameters, clinicians still face difficulties in therapeutic management due to its inherent aggressive nature. This research aims to build a pipeline system that accurately detects recurring and non-recurring OKC. (2) Objective: To automate the risk stratification of OKCs as recurring or non-recurring based on whole slide images (WSIs) using an attention-based image sequence analyzer (ABISA). (3) Materials and methods: The presented architecture combines transformer-based self-attention mechanisms with sequential modeling using LSTM (long short-term memory) to predict the class label. This architecture leverages self-attention to capture spatial dependencies in image patches and LSTM to capture sequential dependencies across patches or frames, making it suitable for this image analysis. These two powerful combinations were integrated and applied on a custom dataset of 48 labeled WSIs (508 tiled images) generated from the highest zoom level WSI. (4) Results: The proposed ABISA algorithm attained 0.98, 1.0, and 0.98 testing accuracy, recall, and area under the curve, respectively, whereas VGG16, VGG19, and Inception V3, standard vision transformer attained testing accuracies of 0.80, 0.73, 0.82, 0.91, respectively. ABISA used 58% fewer trainable parameters than the standard vision transformer. (5) Conclusions: The proposed novel ABISA algorithm was integrated into a risk stratification pipeline to automate the detection of recurring OKC significantly faster, thus allowing the pathologist to define risk stratification faster. Full article
(This article belongs to the Special Issue Artificial Intelligence in Pathological Image Analysis—2nd Edition)
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13 pages, 1769 KiB  
Article
A Novel Automatic Algorithm to Support Lung Ultrasound Non-Expert Physicians in Interstitial Pneumonia Evaluation: A Single-Center Study
by Marialuisa Sveva Marozzi, Sebastiano Cicco, Francesca Mancini, Francesco Corvasce, Fiorella Anna Lombardi, Vanessa Desantis, Luciana Loponte, Tiziana Giliberti, Claudia Maria Morelli, Stefania Longo, Gianfranco Lauletta, Antonio G. Solimando, Roberto Ria and Angelo Vacca
Diagnostics 2024, 14(2), 155; https://doi.org/10.3390/diagnostics14020155 - 10 Jan 2024
Cited by 1 | Viewed by 1393
Abstract
Introduction: Lung ultrasound (LUS) is widely used in clinical practice for identifying interstitial lung diseases (ILDs) and assessing their progression. Although high-resolution computed tomography (HRCT) remains the gold standard for evaluating the severity of ILDs, LUS can be performed as a screening method [...] Read more.
Introduction: Lung ultrasound (LUS) is widely used in clinical practice for identifying interstitial lung diseases (ILDs) and assessing their progression. Although high-resolution computed tomography (HRCT) remains the gold standard for evaluating the severity of ILDs, LUS can be performed as a screening method or as a follow-up tool post-HRCT. Minimum training is needed to better identify typical lesions, and the integration of innovative artificial intelligence (AI) automatic algorithms may enhance diagnostic efficiency. Aim: This study aims to assess the effectiveness of a novel AI algorithm in automatic ILD recognition and scoring in comparison to an expert LUS sonographer. The “SensUS Lung” device, equipped with an automatic algorithm, was employed for the automatic recognition of the typical ILD patterns and to calculate an index grading of the interstitial involvement. Methods: We selected 33 Caucasian patients in follow-up for ILDs exhibiting typical HRCT patterns (honeycombing, ground glass, fibrosis). An expert physician evaluated all patients with LUS on twelve segments (six per side). Next, blinded to the previous evaluation, an untrained operator, a non-expert in LUS, performed the exam with the SensUS device equipped with the automatic algorithm (“SensUS Lung”) using the same protocol. Pulmonary functional tests (PFT) and DLCO were conducted for all patients, categorizing them as having reduced or preserved DLCO. The SensUS device indicated different grades of interstitial involvement named Lung Staging that were scored from 0 (absent) to 4 (peak), which was compared to the Lung Ultrasound Score (LUS score) by dividing it by the number of segments evaluated. Statistical analyses were done with Wilcoxon tests for paired values or Mann–Whitney for unpaired samples, and correlations were performed using Spearman analysis; p < 0.05 was considered significant. Results: Lung Staging was non-inferior to LUS score in identifying the risk of ILDs (median SensUS 1 [0–2] vs. LUS 0.67 [0.25–1.54]; p = 0.84). Furthermore, the grade of interstitial pulmonary involvement detected with the SensUS device is directly related to the LUS score (r = 0.607, p = 0.002). Lung Staging values were inversely correlated with forced expiratory volume at first second (FEV1%, r = −0.40, p = 0.027), forced vital capacity (FVC%, r = −0.39, p = 0.03) and forced expiratory flow (FEF) at 25th percentile (FEF25%, r = −0.39, p = 0.02) while results directly correlated with FEF25–75% (r = 0.45, p = 0.04) and FEF75% (r = 0.43, p = 0.01). Finally, in patients with reduced DLCO, the Lung Staging was significantly higher, overlapping the LUS (reduced median 1 [1–2] vs. preserved 0 [0–1], p = 0.001), and overlapping the LUS (reduced median 18 [4–20] vs. preserved 5.5 [2–9], p = 0.035). Conclusions: Our data suggest that the considered AI automatic algorithm may assist non-expert physicians in LUS, resulting in non-inferior-to-expert LUS despite a tendency to overestimate ILD lesions. Therefore, the AI algorithm has the potential to support physicians, particularly non-expert LUS sonographers, in daily clinical practice to monitor patients with ILDs. The adopted device is user-friendly, offering a fully automatic real-time analysis. However, it needs proper training in basic skills. Full article
(This article belongs to the Special Issue Artificial Intelligence in Pathological Image Analysis—2nd Edition)
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18 pages, 10057 KiB  
Article
Surrogate Biomarker Prediction from Whole-Slide Images for Evaluating Overall Survival in Lung Adenocarcinoma
by Pierre Murchan, Anne-Marie Baird, Pilib Ó Broin, Orla Sheils and Stephen P. Finn
Diagnostics 2024, 14(5), 462; https://doi.org/10.3390/diagnostics14050462 - 20 Feb 2024
Viewed by 1551
Abstract
Background: Recent advances in computational pathology have shown potential in predicting biomarkers from haematoxylin and eosin (H&E) whole-slide images (WSI). However, predicting the outcome directly from WSIs remains a substantial challenge. In this study, we aimed to investigate how gene expression, predicted from [...] Read more.
Background: Recent advances in computational pathology have shown potential in predicting biomarkers from haematoxylin and eosin (H&E) whole-slide images (WSI). However, predicting the outcome directly from WSIs remains a substantial challenge. In this study, we aimed to investigate how gene expression, predicted from WSIs, could be used to evaluate overall survival (OS) in patients with lung adenocarcinoma (LUAD). Methods: Differentially expressed genes (DEGs) were identified from The Cancer Genome Atlas (TCGA)-LUAD cohort. Cox regression analysis was performed on DEGs to identify the gene prognostics of OS. Attention-based multiple instance learning (AMIL) models were trained to predict the expression of identified prognostic genes from WSIs using the TCGA-LUAD dataset. Models were externally validated in the Clinical Proteomic Tumour Analysis Consortium (CPTAC)-LUAD dataset. The prognostic value of predicted gene expression values was then compared to the true gene expression measurements. Results: The expression of 239 prognostic genes could be predicted in TCGA-LUAD with cross-validated Pearson’s R > 0.4. Predicted gene expression demonstrated prognostic performance, attaining a cross-validated concordance index of up to 0.615 in TCGA-LUAD through Cox regression. In total, 36 genes had predicted expression in the external validation cohort that was prognostic of OS. Conclusions: Gene expression predicted from WSIs is an effective method of evaluating OS in patients with LUAD. These results may open up new avenues of cost- and time-efficient prognosis assessment in LUAD treatment. Full article
(This article belongs to the Special Issue Artificial Intelligence in Pathological Image Analysis—2nd Edition)
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17 pages, 2659 KiB  
Article
Artificial Intelligence-Based Quality Assessment of Histopathology Whole-Slide Images within a Clinical Workflow: Assessment of ‘PathProfiler’ in a Diagnostic Pathology Setting
by Lisa Browning, Christine Jesus, Stefano Malacrino, Yue Guan, Kieron White, Alison Puddle, Nasullah Khalid Alham, Maryam Haghighat, Richard Colling, Jacqueline Birks, Jens Rittscher and Clare Verrill
Diagnostics 2024, 14(10), 990; https://doi.org/10.3390/diagnostics14100990 - 9 May 2024
Cited by 2 | Viewed by 2257
Abstract
Digital pathology continues to gain momentum, with the promise of artificial intelligence to aid diagnosis and for assessment of features which may impact prognosis and clinical management. Successful adoption of these technologies depends upon the quality of digitised whole-slide images (WSI); however, current [...] Read more.
Digital pathology continues to gain momentum, with the promise of artificial intelligence to aid diagnosis and for assessment of features which may impact prognosis and clinical management. Successful adoption of these technologies depends upon the quality of digitised whole-slide images (WSI); however, current quality control largely depends upon manual assessment, which is inefficient and subjective. We previously developed PathProfiler, an automated image quality assessment tool, and in this feasibility study we investigate its potential for incorporation into a diagnostic clinical pathology setting in real-time. A total of 1254 genitourinary WSI were analysed by PathProfiler. PathProfiler was developed and trained on prostate tissue and, of the prostate biopsy WSI, representing 46% of the WSI analysed, 4.5% were flagged as potentially being of suboptimal quality for diagnosis. All had concordant subjective issues, mainly focus-related, 54% severe enough to warrant remedial action which resulted in improved image quality. PathProfiler was less reliable in assessment of non-prostate surgical resection-type cases, on which it had not been trained. PathProfiler shows potential for incorporation into a digitised clinical pathology workflow, with opportunity for image quality improvement. Whilst its reliability in the current form appears greatest for assessment of prostate specimens, other specimen types, particularly biopsies, also showed benefit. Full article
(This article belongs to the Special Issue Artificial Intelligence in Pathological Image Analysis—2nd Edition)
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13 pages, 1229 KiB  
Article
Image Quality Assessment and Reliability Analysis of Artificial Intelligence-Based Tumor Classification of Stimulated Raman Histology of Tumor Biobank Samples
by Anna-Katharina Meißner, Tobias Blau, David Reinecke, Gina Fürtjes, Lili Leyer, Nina Müller, Niklas von Spreckelsen, Thomas Stehle, Abdulkader Al Shugri, Reinhard Büttner, Roland Goldbrunner, Marco Timmer and Volker Neuschmelting
Diagnostics 2024, 14(23), 2701; https://doi.org/10.3390/diagnostics14232701 (registering DOI) - 30 Nov 2024
Viewed by 155
Abstract
Background: Stimulated Raman histology (SRH) is a label-free optical imaging method for rapid intraoperative analysis of fresh tissue samples. Analysis of SRH images using Convolutional Neural Networks (CNN) has shown promising results for predicting the main histopathological classes of neurooncological tumors. Due to [...] Read more.
Background: Stimulated Raman histology (SRH) is a label-free optical imaging method for rapid intraoperative analysis of fresh tissue samples. Analysis of SRH images using Convolutional Neural Networks (CNN) has shown promising results for predicting the main histopathological classes of neurooncological tumors. Due to the relatively low number of rare tumor representations in CNN training datasets, a valid prediction of rarer entities remains limited. To develop new reliable analysis tools, larger datasets and greater tumor variety are crucial. One way to accomplish this is through research biobanks storing frozen tumor tissue samples. However, there is currently no data available regarding the pertinency of previously frozen tissue samples for SRH analysis. The aim of this study was to assess image quality and perform a comparative reliability analysis of artificial intelligence-based tumor classification using SRH in fresh and frozen tissue samples. Methods: In a monocentric prospective study, tissue samples from 25 patients undergoing brain tumor resection were obtained. SRH was acquired in fresh and defrosted samples of the same specimen after varying storage durations at −80 °C. Image quality was rated by an experienced neuropathologist, and prediction of histopathological diagnosis was performed using two established CNNs. Results: The image quality of SRH in fresh and defrosted tissue samples was high, with a mean image quality score of 1.96 (range 1–5) for both groups. CNN analysis showed high internal consistency for histo-(Cα 0.95) and molecular (Cα 0.83) pathological tumor classification. The results were confirmed using a dataset with samples from the local tumor biobank (Cα 0.91 and 0.53). Conclusions: Our results showed that SRH appears comparably reliable in fresh and frozen tissue samples, enabling the integration of tumor biobank specimens to potentially improve the diagnostic range and reliability of CNN prediction tools. Full article
(This article belongs to the Special Issue Artificial Intelligence in Pathological Image Analysis—2nd Edition)
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