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Cancers
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Image Analysis and Computational Pathology in Cancer Diagnosis
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Image Analysis and Computational Pathology in Cancer Diagnosis

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Informatics and Big Data".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 34075

Special Issue Editor

Prof. Dr. Emiel A.M. Janssen

E-Mail Website
Guest Editor
Department of Pathology, Stavanger University Hospital, 4011 Stavanger, Norway
Department of Chemistry, Bioscience and Environmental Engineering, University of Stavanger, 4036 Stavanger, Norway
Interests: breast cancer; bladder cancer; molecular pathology; computational pathology; biomarkers

Special Issue Information

Dear Colleagues,

The specialty of pathology is currently the focus of enormous attention as both fields of precision medicine and computational pathology are growing exponentially. Investments in infrastructure and research projects are expanding at a rapid pace. The computational potential reaches from simple distance measurements to quantification of biomarkers and finally the classification of lesions by machine learning systems. In fact, a whole new field “computational pathology” is emerging, enabling large-scale computer-aided diagnosis (CAD).

For both conventional image analysis and computational pathology, the way into routine pathology diagnostics is still long and bumpy. Validation and standardization are key elements that demand collaboration and large datasets.

This Special Issue welcomes articles that have developed new image analysis tools or ML algorithms, but also reports on validation and standardization of previously reported tools and algorithms that can help improve cancer diagnostics or treatment/prognosis prediction.

Prof. Dr. Emiel A.M. Janssen
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 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. Cancers 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 2900 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

  • machine learning
  • immunohistochemistry
  • histopathology
  • omics with spatial attention
  • image analyses

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Published Papers (9 papers)

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Research

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23 pages, 4641 KiB  
Article
Transcriptomic Deconvolution of Neuroendocrine Neoplasms Predicts Clinically Relevant Characteristics
by Raik Otto, Katharina M. Detjen, Pamela Riemer, Melanie Fattohi, Carsten Grötzinger, Guido Rindi, Bertram Wiedenmann, Christine Sers and Ulf Leser
Cancers 2023, 15(3), 936; https://doi.org/10.3390/cancers15030936 - 1 Feb 2023
Viewed by 2115
Abstract
Pancreatic neuroendocrine neoplasms (panNENs) are a rare yet diverse type of neoplasia whose precise clinical–pathological classification is frequently challenging. Since incorrect classifications can affect treatment decisions, additional tools which support the diagnosis, such as machine learning (ML) techniques, are critically needed but generally [...] Read more.
Pancreatic neuroendocrine neoplasms (panNENs) are a rare yet diverse type of neoplasia whose precise clinical–pathological classification is frequently challenging. Since incorrect classifications can affect treatment decisions, additional tools which support the diagnosis, such as machine learning (ML) techniques, are critically needed but generally unavailable due to the scarcity of suitable ML training data for rare panNENs. Here, we demonstrate that a multi-step ML framework predicts clinically relevant panNEN characteristics while being exclusively trained on widely available data of a healthy origin. The approach classifies panNENs by deconvolving their transcriptomes into cell type proportions based on shared gene expression profiles with healthy pancreatic cell types. The deconvolution results were found to provide a prognostic value with respect to the prediction of the overall patient survival time, neoplastic grading, and carcinoma versus tumor subclassification. The performance with which a proliferation rate agnostic deconvolution ML model could predict the clinical characteristics was found to be comparable to that of a comparative baseline model trained on the proliferation rate-informed MKI67 levels. The approach is novel in that it complements established proliferation rate-oriented classification schemes whose results can be reproduced and further refined by differentiating between identically graded subgroups. By including non-endocrine cell types, the deconvolution approach furthermore provides an in silico quantification of panNEN dedifferentiation, optimizing it for challenging clinical classification tasks in more aggressive panNEN subtypes. Full article
(This article belongs to the Special Issue Image Analysis and Computational Pathology in Cancer Diagnosis)
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13 pages, 3499 KiB  
Article
Squeeze-MNet: Precise Skin Cancer Detection Model for Low Computing IoT Devices Using Transfer Learning
by Rupali Kiran Shinde, Md. Shahinur Alam, Md. Biddut Hossain, Shariar Md Imtiaz, JoonHyun Kim, Anuja Anil Padwal and Nam Kim
Cancers 2023, 15(1), 12; https://doi.org/10.3390/cancers15010012 - 20 Dec 2022
Cited by 16 | Viewed by 3168
Abstract
Cancer remains a deadly disease. We developed a lightweight, accurate, general-purpose deep learning algorithm for skin cancer classification. Squeeze-MNet combines a Squeeze algorithm for digital hair removal during preprocessing and a MobileNet deep learning model with predefined weights. The Squeeze algorithm extracts important [...] Read more.
Cancer remains a deadly disease. We developed a lightweight, accurate, general-purpose deep learning algorithm for skin cancer classification. Squeeze-MNet combines a Squeeze algorithm for digital hair removal during preprocessing and a MobileNet deep learning model with predefined weights. The Squeeze algorithm extracts important image features from the image, and the black-hat filter operation removes noise. The MobileNet model (with a dense neural network) was developed using the International Skin Imaging Collaboration (ISIC) dataset to fine-tune the model. The proposed model is lightweight; the prototype was tested on a Raspberry Pi 4 Internet of Things device with a Neo pixel 8-bit LED ring; a medical doctor validated the device. The average precision (AP) for benign and malignant diagnoses was 99.76% and 98.02%, respectively. Using our approach, the required dataset size decreased by 66%. The hair removal algorithm increased the accuracy of skin cancer detection to 99.36% with the ISIC dataset. The area under the receiver operating curve was 98.9%. Full article
(This article belongs to the Special Issue Image Analysis and Computational Pathology in Cancer Diagnosis)
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19 pages, 4626 KiB  
Article
Accurate Diagnosis and Survival Prediction of Bladder Cancer Using Deep Learning on Histological Slides
by Qingyuan Zheng, Rui Yang, Xinmiao Ni, Song Yang, Lin Xiong, Dandan Yan, Lingli Xia, Jingping Yuan, Jingsong Wang, Panpan Jiao, Jiejun Wu, Yiqun Hao, Jianguo Wang, Liantao Guo, Zhengyu Jiang, Lei Wang, Zhiyuan Chen and Xiuheng Liu
Cancers 2022, 14(23), 5807; https://doi.org/10.3390/cancers14235807 - 25 Nov 2022
Cited by 18 | Viewed by 3648
Abstract
(1) Background: Early diagnosis and treatment are essential to reduce the mortality rate of bladder cancer (BLCA). We aimed to develop deep learning (DL)-based weakly supervised models for the diagnosis of BLCA and prediction of overall survival (OS) in muscle-invasive bladder cancer (MIBC) [...] Read more.
(1) Background: Early diagnosis and treatment are essential to reduce the mortality rate of bladder cancer (BLCA). We aimed to develop deep learning (DL)-based weakly supervised models for the diagnosis of BLCA and prediction of overall survival (OS) in muscle-invasive bladder cancer (MIBC) patients using whole slide digitized histological images (WSIs). (2) Methods: Diagnostic and prognostic models were developed using 926 WSIs of 412 BLCA patients from The Cancer Genome Atlas cohort. We collected 250 WSIs of 150 BLCA patients from the Renmin Hospital of Wuhan University cohort for external validation of the models. Two DL models were developed: a BLCA diagnostic model (named BlcaMIL) and an MIBC prognostic model (named MibcMLP). (3) Results: The BlcaMIL model identified BLCA with accuracy 0.987 in the external validation set, comparable to that of expert uropathologists and outperforming a junior pathologist. The C-index values for the MibcMLP model on the internal and external validation sets were 0.631 and 0.622, respectively. The risk score predicted by MibcMLP was a strong predictor independent of existing clinical or histopathologic indicators, as demonstrated by univariate Cox (HR = 2.390, p < 0.0001) and multivariate Cox (HR = 2.414, p < 0.0001) analyses. The interpretability of DL models can help in the analysis of critical regions associated with tumors to enrich the information obtained from WSIs. Furthermore, the expression of six genes (ANAPC7, MAPKAPK5, COX19, LINC01106, AL161431.1 and MYO16-AS1) was significantly associated with MibcMLP-predicted risk scores, revealing possible potential biological correlations. (4) Conclusions: Our study developed DL models for accurately diagnosing BLCA and predicting OS in MIBC patients, which will help promote the precise pathological diagnosis of BLCA and risk stratification of MIBC to improve clinical treatment decisions. Full article
(This article belongs to the Special Issue Image Analysis and Computational Pathology in Cancer Diagnosis)
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19 pages, 1818 KiB  
Article
Contrastive Multiple Instance Learning: An Unsupervised Framework for Learning Slide-Level Representations of Whole Slide Histopathology Images without Labels
by Thomas E. Tavolara, Metin N. Gurcan and M. Khalid Khan Niazi
Cancers 2022, 14(23), 5778; https://doi.org/10.3390/cancers14235778 - 24 Nov 2022
Cited by 16 | Viewed by 4059
Abstract
Recent methods in computational pathology have trended towards semi- and weakly-supervised methods requiring only slide-level labels. Yet, even slide-level labels may be absent or irrelevant to the application of interest, such as in clinical trials. Hence, we present a fully unsupervised method to [...] Read more.
Recent methods in computational pathology have trended towards semi- and weakly-supervised methods requiring only slide-level labels. Yet, even slide-level labels may be absent or irrelevant to the application of interest, such as in clinical trials. Hence, we present a fully unsupervised method to learn meaningful, compact representations of WSIs. Our method initially trains a tile-wise encoder using SimCLR, from which subsets of tile-wise embeddings are extracted and fused via an attention-based multiple-instance learning framework to yield slide-level representations. The resulting set of intra-slide-level and inter-slide-level embeddings are attracted and repelled via contrastive loss, respectively. This resulted in slide-level representations with self-supervision. We applied our method to two tasks— (1) non-small cell lung cancer subtyping (NSCLC) as a classification prototype and (2) breast cancer proliferation scoring (TUPAC16) as a regression prototype—and achieved an AUC of 0.8641 ± 0.0115 and correlation (R2) of 0.5740 ± 0.0970, respectively. Ablation experiments demonstrate that the resulting unsupervised slide-level feature space can be fine-tuned with small datasets for both tasks. Overall, our method approaches computational pathology in a novel manner, where meaningful features can be learned from whole-slide images without the need for annotations of slide-level labels. The proposed method stands to benefit computational pathology, as it theoretically enables researchers to benefit from completely unlabeled whole-slide images. Full article
(This article belongs to the Special Issue Image Analysis and Computational Pathology in Cancer Diagnosis)
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18 pages, 5974 KiB  
Article
Generalization of Deep Learning in Digital Pathology: Experience in Breast Cancer Metastasis Detection
by Sofia Jarkman, Micael Karlberg, Milda Pocevičiūtė, Anna Bodén, Péter Bándi, Geert Litjens, Claes Lundström, Darren Treanor and Jeroen van der Laak
Cancers 2022, 14(21), 5424; https://doi.org/10.3390/cancers14215424 - 3 Nov 2022
Cited by 14 | Viewed by 3555
Abstract
Poor generalizability is a major barrier to clinical implementation of artificial intelligence in digital pathology. The aim of this study was to test the generalizability of a pretrained deep learning model to a new diagnostic setting and to a small change in surgical [...] Read more.
Poor generalizability is a major barrier to clinical implementation of artificial intelligence in digital pathology. The aim of this study was to test the generalizability of a pretrained deep learning model to a new diagnostic setting and to a small change in surgical indication. A deep learning model for breast cancer metastases detection in sentinel lymph nodes, trained on CAMELYON multicenter data, was used as a base model, and achieved an AUC of 0.969 (95% CI 0.926–0.998) and FROC of 0.838 (95% CI 0.757–0.913) on CAMELYON16 test data. On local sentinel node data, the base model performance dropped to AUC 0.929 (95% CI 0.800–0.998) and FROC 0.744 (95% CI 0.566–0.912). On data with a change in surgical indication (axillary dissections) the base model performance indicated an even larger drop with a FROC of 0.503 (95%CI 0.201–0.911). The model was retrained with addition of local data, resulting in about a 4% increase for both AUC and FROC for sentinel nodes, and an increase of 11% in AUC and 49% in FROC for axillary nodes. Pathologist qualitative evaluation of the retrained model´s output showed no missed positive slides. False positives, false negatives and one previously undetected micro-metastasis were observed. The study highlights the generalization challenge even when using a multicenter trained model, and that a small change in indication can considerably impact the model´s performance. Full article
(This article belongs to the Special Issue Image Analysis and Computational Pathology in Cancer Diagnosis)
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14 pages, 1283 KiB  
Article
Novel Human Artificial Intelligence Hybrid Framework Pinpoints Thyroid Nodule Malignancy and Identifies Overlooked Second-Order Ultrasonographic Features
by Xiaohong Jia, Zehao Ma, Dexing Kong, Yamin Li, Hairong Hu, Ling Guan, Jiping Yan, Ruifang Zhang, Ying Gu, Xia Chen, Liying Shi, Xiaomao Luo, Qiaoying Li, Baoyan Bai, Xinhua Ye, Hong Zhai, Hua Zhang, Yijie Dong, Lei Xu, Jianqiao Zhou and CAAUadd Show full author list remove Hide full author list
Cancers 2022, 14(18), 4440; https://doi.org/10.3390/cancers14184440 - 13 Sep 2022
Cited by 7 | Viewed by 2672
Abstract
We present a Human Artificial Intelligence Hybrid (HAIbrid) integrating framework that reweights Thyroid Imaging Reporting and Data System (TIRADS) features and the malignancy score predicted by a convolutional neural network (CNN) for nodule malignancy stratification and diagnosis. We defined extra ultrasonographical features from [...] Read more.
We present a Human Artificial Intelligence Hybrid (HAIbrid) integrating framework that reweights Thyroid Imaging Reporting and Data System (TIRADS) features and the malignancy score predicted by a convolutional neural network (CNN) for nodule malignancy stratification and diagnosis. We defined extra ultrasonographical features from color Doppler images to explore malignancy-relevant features. We proposed Gated Attentional Factorization Machine (GAFM) to identify second-order interacting features trained via a 10 fold distribution-balanced stratified cross-validation scheme on ultrasound images of 3002 nodules all finally characterized by postoperative pathology (1270 malignant ones), retrospectively collected from 131 hospitals. Our GAFM-HAIbrid model demonstrated significant improvements in Area Under the Curve (AUC) value (p-value < 10−5), reaching about 0.92 over the standalone CNN (~0.87) and senior radiologists (~0.86), and identified a second-order vascularity localization and morphological pattern which was overlooked if only first-order features were considered. We validated the advantages of the integration framework on an already-trained commercial CNN system and our findings using an extra set of ultrasound images of 500 nodules. Our HAIbrid framework allows natural integration to clinical workflow for thyroid nodule malignancy risk stratification and diagnosis, and the proposed GAFM-HAIbrid model may help identify novel diagnosis-relevant second-order features beyond ultrasonography. Full article
(This article belongs to the Special Issue Image Analysis and Computational Pathology in Cancer Diagnosis)
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16 pages, 14084 KiB  
Article
iMIL4PATH: A Semi-Supervised Interpretable Approach for Colorectal Whole-Slide Images
by Pedro C. Neto, Sara P. Oliveira, Diana Montezuma, João Fraga, Ana Monteiro, Liliana Ribeiro, Sofia Gonçalves, Isabel M. Pinto and Jaime S. Cardoso
Cancers 2022, 14(10), 2489; https://doi.org/10.3390/cancers14102489 - 18 May 2022
Cited by 14 | Viewed by 3765
Abstract
Colorectal cancer (CRC) diagnosis is based on samples obtained from biopsies, assessed in pathology laboratories. Due to population growth and ageing, as well as better screening programs, the CRC incidence rate has been increasing, leading to a higher workload for pathologists. In this [...] Read more.
Colorectal cancer (CRC) diagnosis is based on samples obtained from biopsies, assessed in pathology laboratories. Due to population growth and ageing, as well as better screening programs, the CRC incidence rate has been increasing, leading to a higher workload for pathologists. In this sense, the application of AI for automatic CRC diagnosis, particularly on whole-slide images (WSI), is of utmost relevance, in order to assist professionals in case triage and case review. In this work, we propose an interpretable semi-supervised approach to detect lesions in colorectal biopsies with high sensitivity, based on multiple-instance learning and feature aggregation methods. The model was developed on an extended version of the recent, publicly available CRC dataset (the CRC+ dataset with 4433 WSI), using 3424 slides for training and 1009 slides for evaluation. The proposed method attained 90.19% classification ACC, 98.8% sensitivity, 85.7% specificity, and a quadratic weighted kappa of 0.888 at slide-based evaluation. Its generalisation capabilities are also studied on two publicly available external datasets. Full article
(This article belongs to the Special Issue Image Analysis and Computational Pathology in Cancer Diagnosis)
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Review

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19 pages, 774 KiB  
Review
Deep Learning Approaches in Histopathology
by Alhassan Ali Ahmed, Mohamed Abouzid and Elżbieta Kaczmarek
Cancers 2022, 14(21), 5264; https://doi.org/10.3390/cancers14215264 - 26 Oct 2022
Cited by 18 | Viewed by 5837
Abstract
The revolution of artificial intelligence and its impacts on our daily life has led to tremendous interest in the field and its related subtypes: machine learning and deep learning. Scientists and developers have designed machine learning- and deep learning-based algorithms to perform various [...] Read more.
The revolution of artificial intelligence and its impacts on our daily life has led to tremendous interest in the field and its related subtypes: machine learning and deep learning. Scientists and developers have designed machine learning- and deep learning-based algorithms to perform various tasks related to tumor pathologies, such as tumor detection, classification, grading with variant stages, diagnostic forecasting, recognition of pathological attributes, pathogenesis, and genomic mutations. Pathologists are interested in artificial intelligence to improve the diagnosis precision impartiality and to minimize the workload combined with the time consumed, which affects the accuracy of the decision taken. Regrettably, there are already certain obstacles to overcome connected to artificial intelligence deployments, such as the applicability and validation of algorithms and computational technologies, in addition to the ability to train pathologists and doctors to use these machines and their willingness to accept the results. This review paper provides a survey of how machine learning and deep learning methods could be implemented into health care providers’ routine tasks and the obstacles and opportunities for artificial intelligence application in tumor morphology. Full article
(This article belongs to the Special Issue Image Analysis and Computational Pathology in Cancer Diagnosis)
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Other

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19 pages, 356 KiB  
Systematic Review
Deep Learning for Skin Melanocytic Tumors in Whole-Slide Images: A Systematic Review
by Andrés Mosquera-Zamudio, Laëtitia Launet, Zahra Tabatabaei, Rafael Parra-Medina, Adrián Colomer, Javier Oliver Moll, Carlos Monteagudo, Emiel Janssen and Valery Naranjo
Cancers 2023, 15(1), 42; https://doi.org/10.3390/cancers15010042 - 21 Dec 2022
Cited by 8 | Viewed by 3416
Abstract
The rise of Artificial Intelligence (AI) has shown promising performance as a support tool in clinical pathology workflows. In addition to the well-known interobserver variability between dermatopathologists, melanomas present a significant challenge in their histological interpretation. This study aims to analyze all previously [...] Read more.
The rise of Artificial Intelligence (AI) has shown promising performance as a support tool in clinical pathology workflows. In addition to the well-known interobserver variability between dermatopathologists, melanomas present a significant challenge in their histological interpretation. This study aims to analyze all previously published studies on whole-slide images of melanocytic tumors that rely on deep learning techniques for automatic image analysis. Embase, Pubmed, Web of Science, and Virtual Health Library were used to search for relevant studies for the systematic review, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. Articles from 2015 to July 2022 were included, with an emphasis placed on the used artificial intelligence methods. Twenty-eight studies that fulfilled the inclusion criteria were grouped into four groups based on their clinical objectives, including pathologists versus deep learning models (n = 10), diagnostic prediction (n = 7); prognosis (n = 5), and histological features (n = 6). These were then analyzed to draw conclusions on the general parameters and conditions of AI in pathology, as well as the necessary factors for better performance in real scenarios. Full article
(This article belongs to the Special Issue Image Analysis and Computational Pathology in Cancer Diagnosis)
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