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4 pages, 187 KiB

Open AccessEditorial

Editorial on Special Issue “Artificial Intelligence in Pathological Image Analysis”

by Masayuki Tsuneki

Diagnostics 2023, 13(5), 828; https://doi.org/10.3390/diagnostics13050828 - 21 Feb 2023

Cited by 1 | Viewed by 1359

Abstract

The artificial intelligence (AI), especially deep learning models, is highly compatible with medical images and natural language processing and is expected to be applied to pathological image analysis and other medical fields [...] Full article

(This article belongs to the Special Issue Artificial Intelligence in Pathological Image Analysis)

Research

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12 pages, 2016 KiB

Open AccessArticle

Determining HER2 Status by Artificial Intelligence: An Investigation of Primary, Metastatic, and HER2 Low Breast Tumors

by Christiane Palm, Catherine E. Connolly, Regina Masser, Barbara Padberg Sgier, Eva Karamitopoulou, Quentin Simon, Beata Bode and Marianne Tinguely

Diagnostics 2023, 13(1), 168; https://doi.org/10.3390/diagnostics13010168 - 3 Jan 2023

Cited by 22 | Viewed by 5006

Abstract

The expression of human epidermal growth factor receptor 2 (HER2) protein or gene transcripts is critical for therapeutic decision making in breast cancer. We examined the performance of a digitalized and artificial intelligence (AI)-assisted workflow for HER2 status determination in accordance with the [...] Read more.

The expression of human epidermal growth factor receptor 2 (HER2) protein or gene transcripts is critical for therapeutic decision making in breast cancer. We examined the performance of a digitalized and artificial intelligence (AI)-assisted workflow for HER2 status determination in accordance with the American Society of Clinical Oncology (ASCO)/College of Pathologists (CAP) guidelines. Our preliminary cohort consisted of 495 primary breast carcinomas, and our study cohort included 67 primary breast carcinomas and 30 metastatic deposits, which were evaluated for HER2 status by immunohistochemistry (IHC) and in situ hybridization (ISH). Three practicing breast pathologists independently assessed and scored slides, building the ground truth. Following a washout period, pathologists were provided with the results of the AI digital image analysis (DIA) and asked to reassess the slides. Both rounds of assessment from the pathologists were compared to the AI results and ground truth for each slide. We observed an overall HER2 positivity rate of 15% in our study cohort. Moderate agreement (Cohen’s κ 0.59) was observed between the ground truth and AI on IHC, with most discrepancies occurring between 0 and 1+ scores. Inter-observer agreement amongst pathologists was substantial (Fleiss´ κ 0.77) and pathologists’ agreement with AI scores was 80.6%. Substantial agreement of the AI with the ground truth (Cohen´s κ 0.80) was detected on ISH-stained slides, and the accuracy of AI was similar for the primary and metastatic tumors. We demonstrated the feasibility of a combined HER2 IHC and ISH AI workflow, with a Cohen’s κ of 0.94 when assessed in accordance with the ASCO/CAP recommendations. Full article

(This article belongs to the Special Issue Artificial Intelligence in Pathological Image Analysis)

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17 pages, 4233 KiB

Open AccessArticle

Allred Scoring of ER-IHC Stained Whole-Slide Images for Hormone Receptor Status in Breast Carcinoma

by Mohammad Faizal Ahmad Fauzi, Wan Siti Halimatul Munirah Wan Ahmad, Mohammad Fareed Jamaluddin, Jenny Tung Hiong Lee, See Yee Khor, Lai Meng Looi, Fazly Salleh Abas and Nouar Aldahoul

Diagnostics 2022, 12(12), 3093; https://doi.org/10.3390/diagnostics12123093 - 8 Dec 2022

Cited by 14 | Viewed by 6320

Abstract

Hormone receptor status is determined primarily to identify breast cancer patients who may benefit from hormonal therapy. The current clinical practice for the testing using either Allred score or H-score is still based on laborious manual counting and estimation of the amount and [...] Read more.

Hormone receptor status is determined primarily to identify breast cancer patients who may benefit from hormonal therapy. The current clinical practice for the testing using either Allred score or H-score is still based on laborious manual counting and estimation of the amount and intensity of positively stained cancer cells in immunohistochemistry (IHC)-stained slides. This work integrates cell detection and classification workflow for breast carcinoma estrogen receptor (ER)-IHC-stained images and presents an automated evaluation system. The system first detects all cells within the specific regions and classifies them into negatively, weakly, moderately, and strongly stained, followed by Allred scoring for ER status evaluation. The generated Allred score relies heavily on accurate cell detection and classification and is compared against pathologists’ manual estimation. Experiments on 40 whole-slide images show 82.5% agreement on hormonal treatment recommendation, which we believe could be further improved with an advanced learning model and enhancement to address the cases with 0% ER status. This promising system can automate the exhaustive exercise to provide fast and reliable assistance to pathologists and medical personnel. The system has the potential to improve the overall standards of prognostic reporting for cancer patients, benefiting pathologists, patients, and also the public at large. Full article

(This article belongs to the Special Issue Artificial Intelligence in Pathological Image Analysis)

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20 pages, 3083 KiB

Open AccessArticle

Computational Pipeline for Glomerular Segmentation and Association of the Quantified Regions with Prognosis of Kidney Function in IgA Nephropathy

by Yoshimasa Kawazoe, Kiminori Shimamoto, Ryohei Yamaguchi, Issei Nakamura, Kota Yoneda, Emiko Shinohara, Yukako Shintani-Domoto, Tetsuo Ushiku, Tatsuo Tsukamoto and Kazuhiko Ohe

Diagnostics 2022, 12(12), 2955; https://doi.org/10.3390/diagnostics12122955 - 25 Nov 2022

Cited by 7 | Viewed by 2556

Abstract

The histopathological findings of the glomeruli from whole slide images (WSIs) of a renal biopsy play an important role in diagnosing and grading kidney disease. This study aimed to develop an automated computational pipeline to detect glomeruli and to segment the histopathological regions [...] Read more.

The histopathological findings of the glomeruli from whole slide images (WSIs) of a renal biopsy play an important role in diagnosing and grading kidney disease. This study aimed to develop an automated computational pipeline to detect glomeruli and to segment the histopathological regions inside of the glomerulus in a WSI. In order to assess the significance of this pipeline, we conducted a multivariate regression analysis to determine whether the quantified regions were associated with the prognosis of kidney function in 46 cases of immunoglobulin A nephropathy (IgAN). The developed pipelines showed a mean intersection over union (IoU) of 0.670 and 0.693 for five classes (i.e., background, Bowman’s space, glomerular tuft, crescentic, and sclerotic regions) against the WSI of its facility, and 0.678 and 0.609 against the WSI of the external facility. The multivariate analysis revealed that the predicted sclerotic regions, even those that were predicted by the external model, had a significant negative impact on the slope of the estimated glomerular filtration rate after biopsy. This is the first study to demonstrate that the quantified sclerotic regions that are predicted by an automated computational pipeline for the segmentation of the histopathological glomerular components on WSIs impact the prognosis of kidney function in patients with IgAN. Full article

(This article belongs to the Special Issue Artificial Intelligence in Pathological Image Analysis)

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18 pages, 2788 KiB

Open AccessArticle

Enhancing Annotation Efficiency with Machine Learning: Automated Partitioning of a Lung Ultrasound Dataset by View

by Bennett VanBerlo, Delaney Smith, Jared Tschirhart, Blake VanBerlo, Derek Wu, Alex Ford, Joseph McCauley, Benjamin Wu, Rushil Chaudhary, Chintan Dave, Jordan Ho, Jason Deglint, Brian Li and Robert Arntfield

Diagnostics 2022, 12(10), 2351; https://doi.org/10.3390/diagnostics12102351 - 28 Sep 2022

Cited by 4 | Viewed by 3145

Abstract

Background: Annotating large medical imaging datasets is an arduous and expensive task, especially when the datasets in question are not organized according to deep learning goals. Here, we propose a method that exploits the hierarchical organization of annotating tasks to optimize efficiency. Methods: [...] Read more.

Background: Annotating large medical imaging datasets is an arduous and expensive task, especially when the datasets in question are not organized according to deep learning goals. Here, we propose a method that exploits the hierarchical organization of annotating tasks to optimize efficiency. Methods: We trained a machine learning model to accurately distinguish between one of two classes of lung ultrasound (LUS) views using 2908 clips from a larger dataset. Partitioning the remaining dataset by view would reduce downstream labelling efforts by enabling annotators to focus on annotating pathological features specific to each view. Results: In a sample view-specific annotation task, we found that automatically partitioning a 780-clip dataset by view saved 42 min of manual annotation time and resulted in

55 \pm 6

additional relevant labels per hour. Conclusions: Automatic partitioning of a LUS dataset by view significantly increases annotator efficiency, resulting in higher throughput relevant to the annotating task at hand. The strategy described in this work can be applied to other hierarchical annotation schemes. Full article

(This article belongs to the Special Issue Artificial Intelligence in Pathological Image Analysis)

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12 pages, 5421 KiB

Open AccessArticle

Dermatopathology of Malignant Melanoma in the Era of Artificial Intelligence: A Single Institutional Experience

by Gerardo Cazzato, Alessandro Massaro, Anna Colagrande, Teresa Lettini, Sebastiano Cicco, Paola Parente, Eleonora Nacchiero, Lucia Lospalluti, Eliano Cascardi, Giuseppe Giudice, Giuseppe Ingravallo, Leonardo Resta, Eugenio Maiorano and Angelo Vacca

Diagnostics 2022, 12(8), 1972; https://doi.org/10.3390/diagnostics12081972 - 15 Aug 2022

Cited by 16 | Viewed by 2385

Abstract

The application of artificial intelligence (AI) algorithms in medicine could support diagnostic and prognostic analyses and decision making. In the field of dermatopathology, there have been various papers that have trained algorithms for the recognition of different types of skin lesions, such as [...] Read more.

The application of artificial intelligence (AI) algorithms in medicine could support diagnostic and prognostic analyses and decision making. In the field of dermatopathology, there have been various papers that have trained algorithms for the recognition of different types of skin lesions, such as basal cell carcinoma (BCC), seborrheic keratosis (SK) and dermal nevus. Furthermore, the difficulty in diagnosing particular melanocytic lesions, such as Spitz nevi and melanoma, considering the grade of interobserver variability among dermatopathologists, has led to an objective difficulty in training machine learning (ML) algorithms to a totally reliable, reportable and repeatable level. In this work we tried to train a fast random forest (FRF) algorithm, typically used for the classification of clusters of pixels in images, to highlight anomalous areas classified as melanoma “defects” following the Allen–Spitz criteria. The adopted image vision diagnostic protocol was structured in the following steps: image acquisition by selecting the best zoom level of the microscope; preliminary selection of an image with a good resolution; preliminary identification of macro-areas of defect in each preselected image; identification of a class of a defect in the selected macro-area; training of the supervised machine learning FRF algorithm by selecting the micro-defect in the macro-area; execution of the FRF algorithm to find an image vision performance indicator; and analysis of the output images by enhancing lesion defects. The precision achieved by the FRF algorithm proved to be appropriate with a discordance of 17% with respect to the dermatopathologist, allowing this type of supervised algorithm to be nominated as a help to the dermatopathologist in the challenging diagnosis of malignant melanoma. Full article

(This article belongs to the Special Issue Artificial Intelligence in Pathological Image Analysis)

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25 pages, 3475 KiB

Open AccessArticle

Natural Language Processing in Diagnostic Texts from Nephropathology

by Maximilian Legnar, Philipp Daumke, Jürgen Hesser, Stefan Porubsky, Zoran Popovic, Jan Niklas Bindzus, Joern-Helge Heinrich Siemoneit and Cleo-Aron Weis

Diagnostics 2022, 12(7), 1726; https://doi.org/10.3390/diagnostics12071726 - 15 Jul 2022

Cited by 7 | Viewed by 3012

Abstract

Introduction: This study investigates whether it is possible to predict a final diagnosis based on a written nephropathological description—as a surrogate for image analysis—using various NLP methods. Methods: For this work, 1107 unlabelled nephropathological reports were included. (i) First, after separating each report [...] Read more.

Introduction: This study investigates whether it is possible to predict a final diagnosis based on a written nephropathological description—as a surrogate for image analysis—using various NLP methods. Methods: For this work, 1107 unlabelled nephropathological reports were included. (i) First, after separating each report into its microscopic description and diagnosis section, the diagnosis sections were clustered unsupervised to less than 20 diagnostic groups using different clustering techniques. (ii) Second, different text classification methods were used to predict the diagnostic group based on the microscopic description section. Results: The best clustering results (i) could be achieved with HDBSCAN, using BoW-based feature extraction methods. Based on keywords, these clusters can be mapped to certain diagnostic groups. A transformer encoder-based approach as well as an SVM worked best regarding diagnosis prediction based on the histomorphological description (ii). Certain diagnosis groups reached F1-scores of up to 0.892 while others achieved weak classification metrics. Conclusion: While textual morphological description alone enables retrieving the correct diagnosis for some entities, it does not work sufficiently for other entities. This is in accordance with a previous image analysis study on glomerular change patterns, where some diagnoses are associated with one pattern, but for others, there exists a complex pattern combination. Full article

(This article belongs to the Special Issue Artificial Intelligence in Pathological Image Analysis)

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18 pages, 4292 KiB

Open AccessArticle

Improving the Diagnosis of Skin Biopsies Using Tissue Segmentation

by Shima Nofallah, Beibin Li, Mojgan Mokhtari, Wenjun Wu, Stevan Knezevich, Caitlin J. May, Oliver H. Chang, Joann G. Elmore and Linda G. Shapiro

Diagnostics 2022, 12(7), 1713; https://doi.org/10.3390/diagnostics12071713 - 14 Jul 2022

Cited by 7 | Viewed by 2000

Abstract

Invasive melanoma, a common type of skin cancer, is considered one of the deadliest. Pathologists routinely evaluate melanocytic lesions to determine the amount of atypia, and if the lesion represents an invasive melanoma, its stage. However, due to the complicated nature of these [...] Read more.

Invasive melanoma, a common type of skin cancer, is considered one of the deadliest. Pathologists routinely evaluate melanocytic lesions to determine the amount of atypia, and if the lesion represents an invasive melanoma, its stage. However, due to the complicated nature of these assessments, inter- and intra-observer variability among pathologists in their interpretation are very common. Machine-learning techniques have shown impressive and robust performance on various tasks including healthcare. In this work, we study the potential of including semantic segmentation of clinically important tissue structure in improving the diagnosis of skin biopsy images. Our experimental results show a 6% improvement in F-score when using whole slide images along with epidermal nests and cancerous dermal nest segmentation masks compared to using whole-slide images alone in training and testing the diagnosis pipeline. Full article

(This article belongs to the Special Issue Artificial Intelligence in Pathological Image Analysis)

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17 pages, 3721 KiB

Open AccessArticle

Nodular and Micronodular Basal Cell Carcinoma Subtypes Are Different Tumors Based on Their Morphological Architecture and Their Interaction with the Surrounding Stroma

by Mircea-Sebastian Șerbănescu, Raluca Maria Bungărdean, Carmen Georgiu and Maria Crișan

Diagnostics 2022, 12(7), 1636; https://doi.org/10.3390/diagnostics12071636 - 5 Jul 2022

Cited by 4 | Viewed by 3816

Abstract

Basal cell carcinoma (BCC) is the most frequent cancer of the skin and comprises low-risk and high-risk subtypes. We selected a low-risk subtype, namely, nodular (N), and a high-risk subtype, namely, micronodular (MN), with the aim to identify differences between them using a [...] Read more.

Basal cell carcinoma (BCC) is the most frequent cancer of the skin and comprises low-risk and high-risk subtypes. We selected a low-risk subtype, namely, nodular (N), and a high-risk subtype, namely, micronodular (MN), with the aim to identify differences between them using a classical morphometric approach through a gray-level co-occurrence matrix and histogram analysis, as well as an approach based on deep learning semantic segmentation. From whole-slide images, pathologists selected 216 N and 201 MN BCC images. The two groups were then manually segmented and compared based on four morphological areas: center of the BCC islands (tumor, T), peripheral palisading of the BCC islands (touching tumor, TT), peritumoral cleft (PC) and surrounding stroma (S). We found that the TT pattern varied the least, while the PC pattern varied the most between the two subtypes. The combination of two distinct analysis approaches yielded fresh insights into the characterization of BCC, and thus, we were able to describe two different morphological patterns for the T component of the two subtypes. Full article

(This article belongs to the Special Issue Artificial Intelligence in Pathological Image Analysis)

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21 pages, 32993 KiB

Open AccessArticle

MixPatch: A New Method for Training Histopathology Image Classifiers

by Youngjin Park, Mujin Kim, Murtaza Ashraf, Young Sin Ko and Mun Yong Yi

Diagnostics 2022, 12(6), 1493; https://doi.org/10.3390/diagnostics12061493 - 18 Jun 2022

Cited by 3 | Viewed by 3010

Abstract

CNN-based image processing has been actively applied to histopathological analysis to detect and classify cancerous tumors automatically. However, CNN-based classifiers generally predict a label with overconfidence, which becomes a serious problem in the medical domain. The objective of this study is to propose [...] Read more.

CNN-based image processing has been actively applied to histopathological analysis to detect and classify cancerous tumors automatically. However, CNN-based classifiers generally predict a label with overconfidence, which becomes a serious problem in the medical domain. The objective of this study is to propose a new training method, called MixPatch, designed to improve a CNN-based classifier by specifically addressing the prediction uncertainty problem and examine its effectiveness in improving diagnosis performance in the context of histopathological image analysis. MixPatch generates and uses a new sub-training dataset, which consists of mixed-patches and their predefined ground-truth labels, for every single mini-batch. Mixed-patches are generated using a small size of clean patches confirmed by pathologists while their ground-truth labels are defined using a proportion-based soft labeling method. Our results obtained using a large histopathological image dataset shows that the proposed method performs better and alleviates overconfidence more effectively than any other method examined in the study. More specifically, our model showed 97.06% accuracy, an increase of 1.6% to 12.18%, while achieving 0.76% of expected calibration error, a decrease of 0.6% to 6.3%, over the other models. By specifically considering the mixed-region variation characteristics of histopathology images, MixPatch augments the extant mixed image methods for medical image analysis in which prediction uncertainty is a crucial issue. The proposed method provides a new way to systematically alleviate the overconfidence problem of CNN-based classifiers and improve their prediction accuracy, contributing toward more calibrated and reliable histopathology image analysis. Full article

(This article belongs to the Special Issue Artificial Intelligence in Pathological Image Analysis)

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28 pages, 9140 KiB

Open AccessArticle

A New Artificial Intelligence-Based Method for Identifying Mycobacterium Tuberculosis in Ziehl–Neelsen Stain on Tissue

by Sabina Zurac, Cristian Mogodici, Teodor Poncu, Mihai Trăscău, Cristiana Popp, Luciana Nichita, Mirela Cioplea, Bogdan Ceachi, Liana Sticlaru, Alexandra Cioroianu, Mihai Busca, Oana Stefan, Irina Tudor, Andrei Voicu, Daliana Stanescu, Petronel Mustatea, Carmen Dumitru and Alexandra Bastian

Diagnostics 2022, 12(6), 1484; https://doi.org/10.3390/diagnostics12061484 - 17 Jun 2022

Cited by 14 | Viewed by 5875

Abstract

Mycobacteria identification is crucial to diagnose tuberculosis. Since the bacillus is very small, finding it in Ziehl–Neelsen (ZN)-stained slides is a long task requiring significant pathologist’s effort. We developed an automated (AI-based) method of identification of mycobacteria. We prepared a training dataset of [...] Read more.

Mycobacteria identification is crucial to diagnose tuberculosis. Since the bacillus is very small, finding it in Ziehl–Neelsen (ZN)-stained slides is a long task requiring significant pathologist’s effort. We developed an automated (AI-based) method of identification of mycobacteria. We prepared a training dataset of over 260,000 positive and over 700,000,000 negative patches annotated on scans of 510 whole slide images (WSI) of ZN-stained slides (110 positive and 400 negative). Several image augmentation techniques coupled with different custom computer vision architectures were used. WSIs automatic analysis was followed by a report indicating areas more likely to present mycobacteria. Our model performs AI-based diagnosis (the final decision of the diagnosis of WSI belongs to the pathologist). The results were validated internally on a dataset of 286,000 patches and tested in pathology laboratory settings on 60 ZN slides (23 positive and 37 negative). We compared the pathologists’ results obtained by separately evaluating slides and WSIs with the results given by a pathologist aided by automatic analysis of WSIs. Our architecture showed 0.977 area under the receiver operating characteristic curve. The clinical test presented 98.33% accuracy, 95.65% sensitivity, and 100% specificity for the AI-assisted method, outperforming any other AI-based proposed methods for AFB detection. Full article

(This article belongs to the Special Issue Artificial Intelligence in Pathological Image Analysis)

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12 pages, 3344 KiB

Open AccessArticle

Artificial Intelligence-Assisted Image Analysis of Acetaminophen-Induced Acute Hepatic Injury in Sprague-Dawley Rats

by Eun Bok Baek, Ji-Hee Hwang, Heejin Park, Byoung-Seok Lee, Hwa-Young Son, Yong-Bum Kim, Sang-Yeop Jun, Jun Her, Jaeku Lee and Jae-Woo Cho

Diagnostics 2022, 12(6), 1478; https://doi.org/10.3390/diagnostics12061478 - 16 Jun 2022

Cited by 6 | Viewed by 2426

Abstract

Although drug-induced liver injury (DILI) is a major target of the pharmaceutical industry, we currently lack an efficient model for evaluating liver toxicity in the early stage of its development. Recent progress in artificial intelligence-based deep learning technology promises to improve the accuracy [...] Read more.

Although drug-induced liver injury (DILI) is a major target of the pharmaceutical industry, we currently lack an efficient model for evaluating liver toxicity in the early stage of its development. Recent progress in artificial intelligence-based deep learning technology promises to improve the accuracy and robustness of current toxicity prediction models. Mask region-based CNN (Mask R-CNN) is a detection-based segmentation model that has been used for developing algorithms. In the present study, we applied a Mask R-CNN algorithm to detect and predict acute hepatic injury lesions induced by acetaminophen (APAP) in Sprague-Dawley rats. To accomplish this, we trained, validated, and tested the model for various hepatic lesions, including necrosis, inflammation, infiltration, and portal triad. We confirmed the model performance at the whole-slide image (WSI) level. The training, validating, and testing processes, which were performed using tile images, yielded an overall model accuracy of 96.44%. For confirmation, we compared the model’s predictions for 25 WSIs at 20× magnification with annotated lesion areas determined by an accredited toxicologic pathologist. In individual WSIs, the expert-annotated lesion areas of necrosis, inflammation, and infiltration tended to be comparable with the values predicted by the algorithm. The overall predictions showed a high correlation with the annotated area. The R square values were 0.9953, 0.9610, and 0.9445 for necrosis, inflammation plus infiltration, and portal triad, respectively. The present study shows that the Mask R-CNN algorithm is a useful tool for detecting and predicting hepatic lesions in non-clinical studies. This new algorithm might be widely useful for predicting liver lesions in non-clinical and clinical settings. Full article

(This article belongs to the Special Issue Artificial Intelligence in Pathological Image Analysis)

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16 pages, 1660 KiB

Open AccessArticle

The Use of Digital Pathology and Artificial Intelligence in Histopathological Diagnostic Assessment of Prostate Cancer: A Survey of Prostate Cancer UK Supporters

by Kai Rakovic, Richard Colling, Lisa Browning, Monica Dolton, Margaret R. Horton, Andrew Protheroe, Alastair D. Lamb, Richard J. Bryant, Richard Scheffer, James Crofts, Ewart Stanislaus and Clare Verrill

Diagnostics 2022, 12(5), 1225; https://doi.org/10.3390/diagnostics12051225 - 13 May 2022

Cited by 7 | Viewed by 4884

Abstract

There has been particular interest in the deployment of digital pathology (DP) and artificial intelligence (AI) in the diagnosis of prostate cancer, but little is known about the views of the public on their use. Prostate Cancer UK supporters were invited to an [...] Read more.

There has been particular interest in the deployment of digital pathology (DP) and artificial intelligence (AI) in the diagnosis of prostate cancer, but little is known about the views of the public on their use. Prostate Cancer UK supporters were invited to an online survey which included quantitative and qualitative questions exploring views on the use of DP and AI in histopathological assessment. A total of 1276 responses to the survey were analysed (response rate 12.5%). Most respondents were supportive of DP (87%, 1113/1276) and of testing AI in clinical practice as a diagnostic adjunct (83%, 1058/1276). Respondents saw DP as potentially increasing workflow efficiency, facilitating research, education/training and fostering clinical discussions between clinician and patient. Some respondents raised concerns regarding data security, reliability and the need for human oversight. Among those who were unsure about AI, information was requested regarding its performance and others wanted to defer the decision to use it to an expert. Although most are in favour of its use, some are unsure, and their concerns could be addressed with more information or better communication. A small minority (<1%) are not in favour of the testing of the use of AI in histopathology for reasons which are not easily addressed. Full article

(This article belongs to the Special Issue Artificial Intelligence in Pathological Image Analysis)

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17 pages, 13882 KiB

Open AccessArticle

A Deep Learning Model for Prostate Adenocarcinoma Classification in Needle Biopsy Whole-Slide Images Using Transfer Learning

by Masayuki Tsuneki, Makoto Abe and Fahdi Kanavati

Diagnostics 2022, 12(3), 768; https://doi.org/10.3390/diagnostics12030768 - 21 Mar 2022

Cited by 19 | Viewed by 5354

Abstract

The histopathological diagnosis of prostate adenocarcinoma in needle biopsy specimens is of pivotal importance for determining optimum prostate cancer treatment. Since diagnosing a large number of cases containing 12 core biopsy specimens by pathologists using a microscope is time-consuming manual system and limited [...] Read more.

The histopathological diagnosis of prostate adenocarcinoma in needle biopsy specimens is of pivotal importance for determining optimum prostate cancer treatment. Since diagnosing a large number of cases containing 12 core biopsy specimens by pathologists using a microscope is time-consuming manual system and limited in terms of human resources, it is necessary to develop new techniques that can rapidly and accurately screen large numbers of histopathological prostate needle biopsy specimens. Computational pathology applications that can assist pathologists in detecting and classifying prostate adenocarcinoma from whole-slide images (WSIs) would be of great benefit for routine pathological practice. In this paper, we trained deep learning models capable of classifying needle biopsy WSIs into adenocarcinoma and benign (non-neoplastic) lesions. We evaluated the models on needle biopsy, transurethral resection of the prostate (TUR-P), and The Cancer Genome Atlas (TCGA) public dataset test sets, achieving an ROC-AUC up to 0.978 in needle biopsy test sets and up to 0.9873 in TCGA test sets for adenocarcinoma. Full article

(This article belongs to the Special Issue Artificial Intelligence in Pathological Image Analysis)

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Review

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20 pages, 2840 KiB

Open AccessReview

Application of Artificial Intelligence in Pathology: Trends and Challenges

by Inho Kim, Kyungmin Kang, Youngjae Song and Tae-Jung Kim

Diagnostics 2022, 12(11), 2794; https://doi.org/10.3390/diagnostics12112794 - 15 Nov 2022

Cited by 41 | Viewed by 8421

Abstract

Given the recent success of artificial intelligence (AI) in computer vision applications, many pathologists anticipate that AI will be able to assist them in a variety of digital pathology tasks. Simultaneously, tremendous advancements in deep learning have enabled a synergy with artificial intelligence [...] Read more.

Given the recent success of artificial intelligence (AI) in computer vision applications, many pathologists anticipate that AI will be able to assist them in a variety of digital pathology tasks. Simultaneously, tremendous advancements in deep learning have enabled a synergy with artificial intelligence (AI), allowing for image-based diagnosis on the background of digital pathology. There are efforts for developing AI-based tools to save pathologists time and eliminate errors. Here, we describe the elements in the development of computational pathology (CPATH), its applicability to AI development, and the challenges it faces, such as algorithm validation and interpretability, computing systems, reimbursement, ethics, and regulations. Furthermore, we present an overview of novel AI-based approaches that could be integrated into pathology laboratory workflows. Full article

(This article belongs to the Special Issue Artificial Intelligence in Pathological Image Analysis)

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