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A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Causes, Screening and Diagnosis".

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 35736

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Prof. Dr. Mario Mascalchi

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Guest Editor

Department of Experimental and Clinical Biomedical Sciences “Mario Serio”, University of Florence, 50121 Florence, Italy
Interests: biomarkers; colorectal cancer; imaging; lung cancer

Dr. Stefano Diciotti

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Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”—DEI, University of Bologna, Bologna, Italy
Interests: artificial intelligence in medicine; biomedical engineering; computational methods; machine and deep learning; medical imaging
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Dear Colleagues,

Cancer is expected to rank as the leading cause of death worldwide in the 21st century. Screening is possible and recommended for several cancers, including those of the breast, colon and rectum, uterine cervix, and lungs. Screening by enabling the detection and radical cure of early-stage lesions offers the opportunity for a remarkable increase in participant life expectancy. Several screening tools are available, and each screening intervention has its own peculiarities. However, the fundamentals of cancer screening procedures are relatively homogeneous and well encoded, and some unresolved issues, albeit presenting some specificities, are shared.

Artificial intelligence (AI) is a powerful class of methods for decision support, having rapidly evolved over recent years. Several approaches have been developed to analyze the increasingly extensive and variably complex datasets often publicly available. With enthusiasm, AI is beginning to be applied to several facets of screening intervention, from the risk stratification of subjects to be invited, to the reading of radiological, optical, and histo-cytopathological images, and integration with stool, serum, and fluid biomarkers to ultimately promote and reinforce the implementation of screening in public health services, thanks to an improved cost/benefit ratio.

In this Special Issue of Cancers, we offer an overview of the current and potential applications of AI to the screening interventions considering, on the one hand, the AI methodological and technical developments and, on the other hand, the applications to specific screening contexts.

Prof. Dr. Mario Mascalchi
Prof. Dr. Stefano Diciotti
Guest Editors

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Keywords

artificial intelligence
biomarkers
breast cancer
cancer screening
cervical cancer
colorectal cancer
deep learning
lung cancer
machine learning
risk stratification

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Published Papers (11 papers)

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Research

21 pages, 1488 KiB

Open AccessArticle

Novelty Classification Model Use in Reinforcement Learning for Cervical Cancer

by Shakhnoza Muksimova, Sabina Umirzakova, Khusanboy Shoraimov, Jushkin Baltayev and Young-Im Cho

Cancers 2024, 16(22), 3782; https://doi.org/10.3390/cancers16223782 - 10 Nov 2024

Viewed by 529

Abstract

Purpose: Cervical cancer significantly impacts global health, where early detection is piv- otal for improving patient outcomes. This study aims to enhance the accuracy of cervical cancer diagnosis by addressing class imbalance through a novel hybrid deep learning model. Methods: The proposed model, RL-CancerNet, integrates EfficientNetV2 and Vision Transformers (ViTs) within a Reinforcement Learning (RL) framework. EfficientNetV2 extracts local features from cervical cytology images to capture fine-grained details, while ViTs analyze these features to recognize global dependencies across image patches. To address class imbalance, an RL agent dynamically adjusts the focus towards minority classes, thus reducing the common bias towards majority classes in medical image classification. Additionally, a Supporter Module incorporating Conv3D and BiLSTM layers with an attention mechanism enhances contextual learning. Results: RL-CancerNet was evaluated on the benchmark cervical cytology datasets Herlev and SipaKMeD, achieving an exceptional accuracy of 99.7%. This performance surpasses several state-of-the-art models, demonstrating the model’s effectiveness in identifying subtle diagnostic features in complex backgrounds. Conclusions: The integration of CNNs, ViTs, and RL into RL-CancerNet significantly improves the diagnostic accuracy of cervical cancer screenings. This model not only advances the field of automated medical screening but also provides a scalable framework adaptable to other medical imaging tasks, potentially enhancing diagnostic processes across various medical domains. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cancer Screening)

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20 pages, 4283 KiB

Open AccessArticle

UCapsNet: A Two-Stage Deep Learning Model Using U-Net and Capsule Network for Breast Cancer Segmentation and Classification in Ultrasound Imaging

by Golla Madhu, Avinash Meher Bonasi, Sandeep Kautish, Abdulaziz S. Almazyad, Ali Wagdy Mohamed, Frank Werner, Mehdi Hosseinzadeh and Mohammad Shokouhifar

Cancers 2024, 16(22), 3777; https://doi.org/10.3390/cancers16223777 - 9 Nov 2024

Viewed by 706

Abstract

Background/Objectives: Breast cancer remains one of the biggest health challenges for women worldwide, and early detection can be truly lifesaving. Although ultrasound imaging is commonly used to detect tumors, the images are not always of sufficient quality, and, thus, traditional U-Net models often miss the finer details needed for accurate detection. This outcome can result in lower accuracy, making early and precise diagnosis more difficult. Methods: This study presents an enhanced U-Net model integrated with a Capsule Network (called UCapsNet) to overcome the limitations of conventional techniques. Our approach improves segmentation by leveraging higher filter counts and skip connections, while the capsule network enhances classification by preserving spatial hierarchies through dynamic routing. The proposed UCapsNet model operates in two stages: first, it segments tumor regions using an enhanced U-Net, followed by a classification of the segmented images with the capsule network. Results: We have tested our model against well-known pre-trained models, including VGG-19, DenseNet, MobileNet, ResNet-50, and Xception. By properly addressing the limitations found in previous studies and using a capsule network trained on the Breast Ultrasound Image (BUSI) dataset, our model resulted in top-achieving impressive precision, recall, and accuracy rates of 98.12%, 99.52%, and 99.22%, respectively. Conclusions: By combining the U-Net’s powerful segmentation capabilities with the capsule network’s high classification accuracy, UCapsNet boosts diagnostic precision and addresses key weaknesses in existing methods. The findings demonstrate that the proposed model is not only more effective in detecting tumors but also more reliable for practical applications in clinical settings. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cancer Screening)

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11 pages, 2840 KiB

Open AccessArticle

Development and Validation of a Deep Learning Model for Histopathological Slide Analysis in Lung Cancer Diagnosis

by Alhassan Ali Ahmed, Muhammad Fawi, Agnieszka Brychcy, Mohamed Abouzid, Martin Witt and Elżbieta Kaczmarek

Cancers 2024, 16(8), 1506; https://doi.org/10.3390/cancers16081506 - 15 Apr 2024

Cited by 2 | Viewed by 2046

Abstract

Lung cancer is the leading cause of cancer-related deaths worldwide. Two of the crucial factors contributing to these fatalities are delayed diagnosis and suboptimal prognosis. The rapid advancement of deep learning (DL) approaches provides a significant opportunity for medical imaging techniques to play a pivotal role in the early detection of lung tumors and subsequent monitoring during treatment. This study presents a DL-based model for efficient lung cancer detection using whole-slide images. Our methodology combines convolutional neural networks (CNNs) and separable CNNs with residual blocks, thereby improving classification performance. Our model improves accuracy (96% to 98%) and robustness in distinguishing between cancerous and non-cancerous lung cell images in less than 10 s. Moreover, the model’s overall performance surpassed that of active pathologists, with an accuracy of 100% vs. 79%. There was a significant linear correlation between pathologists’ accuracy and years of experience (r Pearson = 0.71, 95% CI 0.14 to 0.93, p = 0.022). We conclude that this model enhances the accuracy of cancer detection and can be used to train junior pathologists. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cancer Screening)

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11 pages, 2944 KiB

Open AccessArticle

Artificial-Intelligence-Assisted Detection of Metastatic Colorectal Cancer Cells in Ascitic Fluid

by Hyung Kyung Kim, Eunkyung Han, Jeonghyo Lee, Kwangil Yim, Jamshid Abdul-Ghafar, Kyung Jin Seo, Jang Won Seo, Gyungyub Gong, Nam Hoon Cho, Milim Kim, Chong Woo Yoo and Yosep Chong

Cancers 2024, 16(5), 1064; https://doi.org/10.3390/cancers16051064 - 5 Mar 2024

Viewed by 1590

Abstract

Ascites cytology is a cost-effective test for metastatic colorectal cancer (CRC) in the abdominal cavity. However, metastatic carcinoma of the peritoneum is difficult to diagnose based on biopsy findings, and ascitic aspiration cytology has a low sensitivity and specificity and a high inter-observer variability. The aim of the present study was to apply artificial intelligence (AI) to classify benign and malignant cells in ascites cytology patch images of metastatic CRC using a deep convolutional neural network. Datasets were collected from The OPEN AI Dataset Project, a nationwide cytology dataset for AI research. The numbers of patch images used for training, validation, and testing were 56,560, 7068, and 6534, respectively. We evaluated 1041 patch images of benign and metastatic CRC in the ascitic fluid to compare the performance of pathologists and an AI algorithm, and to examine whether the diagnostic accuracy of pathologists improved with the assistance of AI. This AI method showed an accuracy, a sensitivity, and a specificity of 93.74%, 87.76%, and 99.75%, respectively, for the differential diagnosis of malignant and benign ascites. The diagnostic accuracy and sensitivity of the pathologist with the assistance of the proposed AI method increased from 86.8% to 90.5% and from 73.3% to 79.3%, respectively. The proposed deep learning method may assist pathologists with different levels of experience in diagnosing metastatic CRC cells of ascites. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cancer Screening)

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22 pages, 524 KiB

Open AccessArticle

Improved Prediction of Ovarian Cancer Using Ensemble Classifier and Shaply Explainable AI

by Nihal Abuzinadah, Sarath Kumar Posa, Aisha Ahmed Alarfaj, Ebtisam Abdullah Alabdulqader, Muhammad Umer, Tai-Hoon Kim, Shtwai Alsubai and Imran Ashraf

Cancers 2023, 15(24), 5793; https://doi.org/10.3390/cancers15245793 - 11 Dec 2023

Cited by 1 | Viewed by 2619

Abstract

The importance of detecting and preventing ovarian cancer is of utmost significance for women’s overall health and wellness. Referred to as the “silent killer,” ovarian cancer exhibits inconspicuous symptoms during its initial phases, posing a challenge for timely identification. Identification of ovarian cancer during its advanced stages significantly diminishes the likelihood of effective treatment and survival. Regular screenings, such as pelvic exams, ultrasound, and blood tests for specific biomarkers, are essential tools for detecting the disease in its early, more treatable stages. This research makes use of the Soochow University ovarian cancer dataset, containing 50 features for the accurate detection of ovarian cancer. The proposed predictive model makes use of a stacked ensemble model, merging the strengths of bagging and boosting classifiers, and aims to enhance predictive accuracy and reliability. This combination harnesses the benefits of variance reduction and improved generalization, contributing to superior ovarian cancer prediction outcomes. The proposed model gives 96.87% accuracy, which is currently the highest model result obtained on this dataset so far using all features. Moreover, the outcomes are elucidated utilizing the explainable artificial intelligence method referred to as SHAPly. The excellence of the suggested model is demonstrated through a comparison of its performance with that of other cutting-edge models. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cancer Screening)

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19 pages, 571 KiB

Open AccessArticle

Improving Prediction of Cervical Cancer Using KNN Imputed SMOTE Features and Multi-Model Ensemble Learning Approach

by Hanen Karamti, Raed Alharthi, Amira Al Anizi, Reemah M. Alhebshi, Ala’ Abdulmajid Eshmawi, Shtwai Alsubai and Muhammad Umer

Cancers 2023, 15(17), 4412; https://doi.org/10.3390/cancers15174412 - 4 Sep 2023

Cited by 13 | Viewed by 2691

Abstract

Objective: Cervical cancer ranks among the top causes of death among females in developing countries. The most important procedures that should be followed to guarantee the minimizing of cervical cancer’s aftereffects are early identification and treatment under the finest medical guidance. One of the best methods to find this sort of malignancy is by looking at a Pap smear image. For automated detection of cervical cancer, the available datasets often have missing values, which can significantly affect the performance of machine learning models. Methods: To address these challenges, this study proposes an automated system for predicting cervical cancer that efficiently handles missing values with SMOTE features to achieve high accuracy. The proposed system employs a stacked ensemble voting classifier model that combines three machine learning models, along with KNN Imputer and SMOTE up-sampled features for handling missing values. Results: The proposed model achieves 99.99% accuracy, 99.99% precision, 99.99% recall, and 99.99% F1 score when using KNN imputed SMOTE features. The study compares the performance of the proposed model with multiple other machine learning algorithms under four scenarios: with missing values removed, with KNN imputation, with SMOTE features, and with KNN imputed SMOTE features. The study validates the efficacy of the proposed model against existing state-of-the-art approaches. Conclusions: This study investigates the issue of missing values and class imbalance in the data collected for cervical cancer detection and might aid medical practitioners in timely detection and providing cervical cancer patients with better care. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cancer Screening)

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13 pages, 2576 KiB

Open AccessArticle

AI-Assisted Screening of Oral Potentially Malignant Disorders Using Smartphone-Based Photographic Images

by Vivek Talwar, Pragya Singh, Nirza Mukhia, Anupama Shetty, Praveen Birur, Karishma M. Desai, Chinnababu Sunkavalli, Konala S. Varma, Ramanathan Sethuraman, C. V. Jawahar and P. K. Vinod

Cancers 2023, 15(16), 4120; https://doi.org/10.3390/cancers15164120 - 16 Aug 2023

Cited by 10 | Viewed by 3853

Abstract

The prevalence of oral potentially malignant disorders (OPMDs) and oral cancer is surging in low- and middle-income countries. A lack of resources for population screening in remote locations delays the detection of these lesions in the early stages and contributes to higher mortality and a poor quality of life. Digital imaging and artificial intelligence (AI) are promising tools for cancer screening. This study aimed to evaluate the utility of AI-based techniques for detecting OPMDs in the Indian population using photographic images of oral cavities captured using a smartphone. A dataset comprising 1120 suspicious and 1058 non-suspicious oral cavity photographic images taken by trained front-line healthcare workers (FHWs) was used for evaluating the performance of different deep learning models based on convolution (DenseNets) and Transformer (Swin) architectures. The best-performing model was also tested on an additional independent test set comprising 440 photographic images taken by untrained FHWs (set I). DenseNet201 and Swin Transformer (base) models show high classification performance with an F1-score of 0.84 (CI 0.79–0.89) and 0.83 (CI 0.78–0.88) on the internal test set, respectively. However, the performance of models decreases on test set I, which has considerable variation in the image quality, with the best F1-score of 0.73 (CI 0.67–0.78) obtained using DenseNet201. The proposed AI model has the potential to identify suspicious and non-suspicious oral lesions using photographic images. This simplified image-based AI solution can assist in screening, early detection, and prompt referral for OPMDs. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cancer Screening)

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28 pages, 7398 KiB

Open AccessArticle

DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images

by Maryam Tahir, Ahmad Naeem, Hassaan Malik, Jawad Tanveer, Rizwan Ali Naqvi and Seung-Won Lee

Cancers 2023, 15(7), 2179; https://doi.org/10.3390/cancers15072179 - 6 Apr 2023

Cited by 65 | Viewed by 9436

Abstract

Skin cancer is one of the most lethal kinds of human illness. In the present state of the health care system, skin cancer identification is a time-consuming procedure and if it is not diagnosed initially then it can be threatening to human life. To attain a high prospect of complete recovery, early detection of skin cancer is crucial. In the last several years, the application of deep learning (DL) algorithms for the detection of skin cancer has grown in popularity. Based on a DL model, this work intended to build a multi-classification technique for diagnosing skin cancers such as melanoma (MEL), basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanocytic nevi (MN). In this paper, we have proposed a novel model, a deep learning-based skin cancer classification network (DSCC_Net) that is based on a convolutional neural network (CNN), and evaluated it on three publicly available benchmark datasets (i.e., ISIC 2020, HAM10000, and DermIS). For the skin cancer diagnosis, the classification performance of the proposed DSCC_Net model is compared with six baseline deep networks, including ResNet-152, Vgg-16, Vgg-19, Inception-V3, EfficientNet-B0, and MobileNet. In addition, we used SMOTE Tomek to handle the minority classes issue that exists in this dataset. The proposed DSCC_Net obtained a 99.43% AUC, along with a 94.17%, accuracy, a recall of 93.76%, a precision of 94.28%, and an F1-score of 93.93% in categorizing the four distinct types of skin cancer diseases. The rates of accuracy for ResNet-152, Vgg-19, MobileNet, Vgg-16, EfficientNet-B0, and Inception-V3 are 89.32%, 91.68%, 92.51%, 91.12%, 89.46% and 91.82%, respectively. The results showed that our proposed DSCC_Net model performs better as compared to baseline models, thus offering significant support to dermatologists and health experts to diagnose skin cancer. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cancer Screening)

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22 pages, 3517 KiB

Open AccessArticle

Proposal and Definition of an Intelligent Clinical Decision Support System Applied to the Screening and Early Diagnosis of Breast Cancer

by Manuel Casal-Guisande, Antía Álvarez-Pazó, Jorge Cerqueiro-Pequeño, José-Benito Bouza-Rodríguez, Gustavo Peláez-Lourido and Alberto Comesaña-Campos

Cancers 2023, 15(6), 1711; https://doi.org/10.3390/cancers15061711 - 10 Mar 2023

Cited by 14 | Viewed by 2919

Abstract

Breast cancer is the most frequently diagnosed tumor pathology on a global scale, being the leading cause of mortality in women. In light of this problem, screening programs have been implemented on the population at risk in the form of mammograms, starting in the 20th century. This has considerably reduced the associated deaths, as well as improved the prognosis of the patients who suffer from this disease. In spite of this, the evaluation of mammograms is not without certain variability and depends, to a large extent, on the experience and training of the medical team carrying out the assessment. With the aim of supporting the evaluation process of mammogram images and improving the diagnosis process, this work presents the design, development and proof of concept of a novel intelligent clinical decision support system, grounded on two predictive approaches that work concurrently. The first of them applies a series of expert systems based on fuzzy inferential engines, geared towards the treatment of the characteristics associated with the main findings present in mammograms. This allows the determination of a series of risk indicators, the Symbolic Risks, related to the risk of developing breast cancer according to the different findings. The second one implements a classification machine learning algorithm, which using data related to mammography findings as well as general patient information determines another metric, the Statistical Risk, also linked to the risk of developing breast cancer. These risk indicators are then combined, resulting in a new indicator, the Global Risk. This could then be corrected using a weighting factor according to the BI-RADS category, allocated to each patient by the medical team in charge. Thus, the Corrected Global Risk is obtained, which after interpretation can be used to establish the patient’s status as well as generate personalized recommendations. The proof of concept and software implementation of the system were carried out using a data set with 130 patients from a database from the School of Medicine and Public Health of the University of Wisconsin-Madison. The results obtained were encouraging, highlighting the potential use of the application, albeit pending intensive clinical validation in real environments. Moreover, its possible integration in hospital computer systems is expected to improve diagnostic processes as well as patient prognosis. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cancer Screening)

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24 pages, 13445 KiB

Open AccessArticle

Improving Automatic Melanoma Diagnosis Using Deep Learning-Based Segmentation of Irregular Networks

by Anand K. Nambisan, Akanksha Maurya, Norsang Lama, Thanh Phan, Gehana Patel, Keith Miller, Binita Lama, Jason Hagerty, Ronald Stanley and William V. Stoecker

Cancers 2023, 15(4), 1259; https://doi.org/10.3390/cancers15041259 - 16 Feb 2023

Cited by 14 | Viewed by 3061

Abstract

Deep learning has achieved significant success in malignant melanoma diagnosis. These diagnostic models are undergoing a transition into clinical use. However, with melanoma diagnostic accuracy in the range of ninety percent, a significant minority of melanomas are missed by deep learning. Many of the melanomas missed have irregular pigment networks visible using dermoscopy. This research presents an annotated irregular network database and develops a classification pipeline that fuses deep learning image-level results with conventional hand-crafted features from irregular pigment networks. We identified and annotated 487 unique dermoscopic melanoma lesions from images in the ISIC 2019 dermoscopic dataset to create a ground-truth irregular pigment network dataset. We trained multiple transfer learned segmentation models to detect irregular networks in this training set. A separate, mutually exclusive subset of the International Skin Imaging Collaboration (ISIC) 2019 dataset with 500 melanomas and 500 benign lesions was used for training and testing deep learning models for the binary classification of melanoma versus benign. The best segmentation model, U-Net++, generated irregular network masks on the 1000-image dataset. Other classical color, texture, and shape features were calculated for the irregular network areas. We achieved an increase in the recall of melanoma versus benign of 11% and in accuracy of 2% over DL-only models using conventional classifiers in a sequential pipeline based on the cascade generalization framework, with the highest increase in recall accompanying the use of the random forest algorithm. The proposed approach facilitates leveraging the strengths of both deep learning and conventional image processing techniques to improve the accuracy of melanoma diagnosis. Further research combining deep learning with conventional image processing on automatically detected dermoscopic features is warranted. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cancer Screening)

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18 pages, 7071 KiB

Open AccessArticle

Deep Learning-Based Screening of Urothelial Carcinoma in Whole Slide Images of Liquid-Based Cytology Urine Specimens

by Masayuki Tsuneki, Makoto Abe and Fahdi Kanavati

Cancers 2023, 15(1), 226; https://doi.org/10.3390/cancers15010226 - 30 Dec 2022

Cited by 8 | Viewed by 4045

Abstract

Urinary cytology is a useful, essential diagnostic method in routine urological clinical practice. Liquid-based cytology (LBC) for urothelial carcinoma screening is commonly used in the routine clinical cytodiagnosis because of its high cellular yields. Since conventional screening processes by cytoscreeners and cytopathologists using microscopes is limited in terms of human resources, it is important to integrate new deep learning methods that can automatically and rapidly diagnose a large amount of specimens without delay. The goal of this study was to investigate the use of deep learning models for the classification of urine LBC whole-slide images (WSIs) into neoplastic and non-neoplastic (negative). We trained deep learning models using 786 WSIs by transfer learning, fully supervised, and weakly supervised learning approaches. We evaluated the trained models on two test sets, one of which was representative of the clinical distribution of neoplastic cases, with a combined total of 750 WSIs, achieving an area under the curve for diagnosis in the range of 0.984–0.990 by the best model, demonstrating the promising potential use of our model for aiding urine cytodiagnostic processes. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cancer Screening)

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