Efficient Cancer Diagnosis Research Articles

Point-of-Care Sensor Using Modified Nickel-doped Zinc Oxide Nanoparticle Carbon Paste Electrode for Homovanillic Acid Cancer Biomarker Detection in Urine

The prevalence of cancer worldwide has prompted efforts to develop and produce a range of electrochemical biosensors for cancer diagnosis. Efficient cancer diagnosis can be enhanced by the sensitive detection of biomarkers, which can also lower the cost of medical diagnostics. Neuroblastoma is an embryonic cancer arising from neural crest stem cells and is considered the most common malignancy in infants and the extracranial solid tumor in children. In this paper, we describe the construction of a nanoparticle-modified electrochemical sensor for detecting and quantifying homovanillic acid (HVA), a biomarker for neuroblastoma. The electrooxidation of HVA was studied at a carbon paste electrode (CPE) modified with nickel-doped zinc oxide nanoparticles (Ni-ZnO NPs). The use of these nanoparticles enhanced electrochemical sensitivity and the electrocatalytic activity. The differential pulse voltammetric response of HVA was found to be linear in the concentration range of (3.96 × 10−6 to 3.83 × 10−5 M) with a lower detection limit of 1.01 × 10−6 M. The electrode demonstrated good stability in the HVA determination process, with a minor decrease in response after 10 weeks. The proposed sensor was successfully applied to determine HVA in a urine sample with a good detection result and a worthwhile biological impact.

Pioneering Prostate Cancer Diagnosis: Evaluating AI Against Traditional Methods - A Systematic Literature Review

ABSTRACT Prostate cancer is one of the most common forms of men's cancer in the world and the understanding of its epidemiology is crucial to effective prevention, early detection and management strategies. This systematic review highlights the potential of artificial intelligence-based diagnostic methods to revolutionize the early diagnosis of prostate cancer. A comprehensive literature search was carried out using databases such as PubMed, Google Scholar and Scopus. After removing duplicates, titles and abstracts were examined in 279 studies and the following full texts were examined in 128 studies. We have ten studies to review in total. Therefore, we chose to review data for all 10 identified studies that applied AI techniques to detect ca prostate. The Paige Prostate Alpha AI system significantly enhances pathologists' diagnostic capabilities, leading to more accurate and effective prostate cancer detection. These limitations indicate that further research is required to address these issues and validate the results in a more diverse and controlled environment. AI-based diagnostic methods have shown significant promise in enhancing the early detection of prostate cancer. As technology continues to advance, integrating AI with traditional diagnostic approaches could lead to more effective, efficient, and accurate prostate cancer screening and diagnosis. Future studies should focus on large-scale clinical trials and real-world applications to validate these findings and facilitate the adoption of AI in clinical settings. Keywords: Prostate Cancer, Artificial Intelligent, Deep Machine Learning, MRI

Computer-aided diagnosis of liver cancer with improved SegNet and deep stacking ensemble model

Liver cancer is a leading cause of cancer-related deaths, often diagnosed at advanced stages due to reliance on traditional imaging methods. Existing computer-aided diagnosis systems struggle with noise, anatomical complexity, and ineffective feature integration, leading to inaccuracies in lesion segmentation and classification. By effectively addressing these challenges, the model aims to enhance early detection and assist clinicians in making informed decisions. Ultimately, this research seeks to contribute to more efficient and accurate liver cancer diagnosis. This paper presents a novel model for liver cancer classification, called SegNet-based Liver Cancer Classification via SqueezeNet (SgN-LCC-SqN). The model effectively executes liver cancer segmentation and classification through four key steps: preprocessing, segmentation, feature extraction, and classification. During preprocessing, Quadratic Mean Estimated Wiener Filtering (QMEWF) is utilized to minimize image noise. Segmentation divides the image into segments using Enhanced Feature Pyramid SegNet (EFP-SgN), which is essential for precise diagnosis. Feature extraction encompasses color features, Local Directional Pattern Variance, and Correlation Filtering-Local Gradient Increasing Pattern (CF-LGIP) features. The extracted features are then processed through an ensemble model, Deep Convolutional, Recurrent, Long Short Term Memory with SqueezeNet (DCR-LSTM-SqN), which includes Deep Convolutional Neural Network (DCNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Modified Loss Function in SqueezeNet (MLF-SqN) classifiers, sequentially analyzing the feature sets through DCNN, RNN, and LSTM before classification by MLF-SqN. The performance of the suggested DCR-LSTM-SqN model is evaluated over conventional methods for positive, negative and other metrics. The DCR-LSTM-SqN model consistently demonstrates superior accuracy, ranging from 0.947 to 0.984, across all training data percentages. Thus, the proposed model effectively segments liver lesions and classifies cancerous areas, demonstrating its potential as a valuable resource for clinicians to enhance the efficiency and accuracy of liver cancer diagnosis.

Data-driven risk stratification and precision management of pulmonary nodules detected on chest computed tomography.

The widespread implementation of low-dose computed tomography (LDCT) in lung cancer screening has led to the increasing detection of pulmonary nodules. However, precisely evaluating the malignancy risk of pulmonary nodules remains a formidable challenge. Here we propose a triage-driven Chinese Lung Nodules Reporting and Data System (C-Lung-RADS) utilizing a medical checkup cohort of 45,064 cases. The system was operated in a stepwise fashion, initially distinguishing low-, mid-, high- and extremely high-risk nodules based on their size and density. Subsequently, it progressively integrated imaging information, demographic characteristics and follow-up data to pinpoint suspicious malignant nodules and refine the risk scale. The multidimensional system achieved a state-of-the-art performance with an area under the curve (AUC) of 0.918 (95% confidence interval (CI) 0.918-0.919) on the internal testing dataset, outperforming the single-dimensional approach (AUC of 0.881, 95% CI 0.880-0.882). Moreover, C-Lung-RADS exhibited a superior sensitivity compared with Lung-RADS v2022 (87.1% versus 63.3%) in an independent cohort, which was screened using mobile computed tomography scanners to broaden screening accessibility in resource-constrained settings. With its foundation in precise risk stratification and tailored management, this system has minimized unnecessary invasive procedures for low-risk cases and recommended prompt intervention for extremely high-risk nodules to avert diagnostic delays. This approach has the potential to enhance the decision-making paradigm and facilitate a more efficient diagnosis of lung cancer during routine checkups as well as screening scenarios.

Performance enhancement of machine learning algorithm for breast cancer diagnosis using hyperparameter optimization

Breast cancer is the most fatal women’s cancer, and accurate diagnosis of this disease in the initial phase is crucial to abate death rates worldwide. The demand for computer-aided disease diagnosis technologies in healthcare is growing significantly to assist physicians in ensuring the effectual treatment of critical diseases. The vital purpose of this study is to analyze and evaluate the classification efficiency of several machine learning algorithms with hyperparameter optimization techniques using grid search and random search to reveal an efficient breast cancer diagnosis approach. Choosing the optimal combination of hyperparameters using hyperparameter optimization for machine learning models has a straight influence on the performance of models. According to the findings of several evaluation studies, the k-nearest neighbor is addressed in this study for effective diagnosis of breast cancer, which got a 100.00% recall value with hyperparameters found utilizing grid search. k-nearest neighbor, logistic regression, and multilayer perceptron obtained 99.42% accuracy after utilizing hyperparameter optimization. All machine learning models showed higher efficiency in breast cancer diagnosis with grid search-based hyperparameter optimization except for XGBoost. Therefore, the evaluation outcomes strongly validate the effectiveness and reliability of the proposed technique for breast cancer diagnosis.

Efficient breast cancer diagnosis using multi‐level progressive feature aggregation based deep transfer learning system

AbstractBreast cancer is a worldwide fatal disease that exists mostly among women. The deep learning technique has proven its effectiveness, but the performance of the existing deep learning systems is quite compromising. In this work, a deep transfer learning system is suggested for efficient breast cancer classification from histopathology images. This system is based on a novel multi‐level progressive feature aggregation (MPFA) and a spatial domain learning approach. The combination of a pretrained Resnet101 backbone network with MPFA is implemented to extract more significant features. In addition, a mixed‐dilated spatial domain learning network (MSLN) is further incorporated to enhance the receptive field and increase discrimination between features. The proposed method achieved superior performance as compared to the existing state‐of‐the‐art methods, offering 99.24% accuracy, a 98.79% F‐1 score, 98.59% precision, and 98.99% recall values over BreaKHis dataset. An ablation study is carried out over the ICIAR2018 dataset to verify the generalizability and effectiveness of the system.

Abstract 299: FAP/αSMA association with tumor visibility and progression in prostate cancer

Abstract Introduction Prostate cancer (PCa) ranks as the second most frequently diagnosed cancer in the male population worldwide. The challenge in effectively managing PCa is the absence of well-established biological markers, which often leads to both over and under treatment. To address this issue, a deeper understanding of the molecular alterations within PCa is essential for more accurate patient stratification. In this research, we embark on a comprehensive examination of how the presence of fibroblast activation protein (FAP) and alpha smooth muscle actin (αSMA) in PCa tissue associates with the visibility of the disease in Magnetic Resonance Imaging (MRI) and their impact on cancer progression. The objective of this study is to analyze cell compartmental expression of FAP and aSMA using an AI based image analysis method. We also study the role of FAP and αSMA expression in predicting disease progression and mortality. Materials and Methods We applied FAP (ab207178, Abcam)/αSMA (M0851, DAKO) double chromogen immunohistochemical (IHC) staining to three independent cohorts of formalin-fixed paraffin-embedded (FFPE) prostate cancer tissue microarrays. All together, we analyzed 835 patients’ samples. First cohort consists of 387 patients with preoperative multiparametric MRI and robot-assisted laparoscopic prostatectomy (RALP) as primary treatment. Second cohort was case vs control cohort of Grade group 2-4 localized PCa at radical prostatectomy (RP) with 168 patients. Third cohort consists PCa patents from continuous, population-based collection of radical prostatectomies of 319 patients. Image analysis was performed with Aiforia Create, which is cloud-based deep-learning artificial intelligence software that provides platform for training and validating automated image analysis pipelines using convolutional neural networks. Analysis algorithm in this project was based on semantic, pixel-level area segmentation. Results FAP expression was higher and αSMA lower in MRI-visible tumors compared with MRI-invisible tumors. Stromal and epithelial FAP expression associate with tumor progression. We could also see statistically significant differences in FAP positivity between clinically significant (GS ≥7) and insignificant (GS ≤6) tumors. Hazard ratios imply that FAP and αSMA have significant impact on risk of biochemical recurrence in patients with MRI-visible tumors. Conclusions FAP is closely linked to tumor MRI visibility, cancer progression, and Gleason scores, marking it as a valuable biomarker in PCa assessment. High stromal αSMA is primarily associated with MRI visibility. Our automated AI-based image analysis method has demonstrated reliability, aligning with our previous findings. This advances the potential for more accurate and efficient prostate cancer diagnosis and prognosis, offering a potential benefit to future patient care. Citation Format: Jenni Säilä, Timo-Pekka Lehto, Annabrita Schoonenberg, Katja Välimäki, Andrew Erickson, Antti Rannikko, Olli Kallioniemi, Tuomas Mirtti, Teijo Pellinen. FAP/αSMA association with tumor visibility and progression in prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 299.

Role of autophagy in cancer-associated fibroblast activation, signaling and metabolic reprograming.

Tumors not only consist of cancerous cells, but they also harbor several normal-like cell types and non-cellular components. cancer-associated fibroblasts (CAFs) are one of these cellular components that are found predominantly in the tumor stroma. Autophagy is an intracellular degradation and quality control mechanism, and recent studies provided evidence that autophagy played a critical role in CAF formation, metabolic reprograming and tumor-stroma crosstalk. Therefore, shedding light on the autophagy and its role in CAF biology might help us better understand the roles of CAFs and the TME in cancer progression and may facilitate the exploitation of more efficient cancer diagnosis and treatment. Here, we provide an overview about the involvement of autophagy in CAF-related pathways, including transdifferentiation and activation of CAFs, and further discuss the implications of targeting tumor stroma as a treatment option.

Nanoflower-shaped MXene-based field-effect transistor capable of ultrasensitive microRNA-21 determination towards efficient lung cancer diagnosis

We introduced a MXene-based field-effect transistor (FET) biosensor with nano-flower morphology for ultrasensitive determination of miR-21, a crucial biomarker in lung cancer diagnosis.

CerviXpert: A multi-structural convolutional neural network for predicting cervix type and cervical cell abnormalities

Objectives Cervical cancer, a leading cause of cancer-related deaths among women globally, has a significantly higher survival rate when diagnosed early. Traditional diagnostic methods like Pap smears and cervical biopsies rely heavily on the skills of cytologists, making the process prone to errors. This study aims to develop CerviXpert, a multi-structural convolutional neural network designed to classify cervix types and detect cervical cell abnormalities efficiently. Methods We introduced CerviXpert, a computationally efficient convolutional neural network model that classifies cervical cancer using images from the publicly available SiPaKMeD dataset. Our approach emphasizes simplicity, using a limited number of convolutional layers followed by max-pooling and dense layers, trained from scratch. We compared CerviXpert’s performance against other state-of-the-art convolutional neural network models, including ResNet50, VGG16, MobileNetV2, and InceptionV3, evaluating them on accuracy, computational efficiency, and robustness using five-fold cross-validation. Results CerviXpert achieved an accuracy of 98.04% in classifying cervical cell abnormalities into three classes (normal, abnormal, and benign) and 98.60% for five-class cervix type classification, outperforming MobileNetV2 and InceptionV3 in both accuracy and computational demands. It demonstrated comparable results to ResNet50 and VGG16, with significantly reduced computational complexity and resource usage. Conclusion CerviXpert offers a promising solution for efficient cervical cancer screening and diagnosis, striking a balance between accuracy and computational feasibility. Its streamlined architecture makes it suitable for deployment in resource-constrained environments, potentially improving early detection and management of cervical cancer.

Advances in Rolling Circle Amplification (RCA)‐Based DNA‐Functional Materials for Cancer Diagnosis and Therapy

Developing biocompatible material systems with accurate functional designability and powerful integration capability is the urgent demand of efficient cancer diagnosis and therapy. Deoxyribonucleic acids (DNAs) as biomacromolecules are characterized with sequence programmability, rich biological activity, and molecular recognition, and show great performance in the fabrication of biomedical materials. Rolling circle amplification (RCA) is an efficient isothermal enzymatic amplification strategy for production of ultralong single‐stranded DNA (ssDNA) with defined repeat sequences and structures. By virtue of rational design of the RCA templates sequences, the produced ssDNA enables to integrate and amplify the required function modules, which endows RCA‐based DNA materials with extraordinary performance in cancer therapeutics. In this review, RCA‐based strategies for integration of functional modules are systematically summarized; construction of RCA‐based functional DNA materials and their recent progress in cancer therapeutics including detection, bioimaging, and therapy are overviewed; and finally the opportunities and challenges of RCA‐based assembly strategy in terms of material construction and applications in cancer diagnosis and therapy are discussed. It is envisioned that RCA‐based DNA‐functional materials will provide typical paradigms for the application of DNA‐functional materials in the field of cancer therapeutics, and hopefully provide more possibilities for precision medicine.

Macrocyclic-Albumin Conjugates for Precise Delivery of Radionuclides and Anticancer Drugs to Tumors.

Precise delivery of radionuclides and anticancer drugs to tumor tissue is crucial to ensuring drug synergism and optimal therapeutic effects in radionuclide-based combination radio-chemotherapy. However, current codelivery vectors often rely on physical embedment/adsorption to load anticancer drugs, which lacks precise mechanisms for drug loading and release, resulting in unpredictable combination effects. Herein, a macrocyclic-albumin conjugate (MAC) that enables precise loading and controlled release of anticancer drugs is presented. By conjugating multiple macrocyclic hosts (sulfonate azocalix[4]arenes, SAC4A) to albumin molecules, the MAC facilitates the precise loading of anticancer drugs through host-guest interactions and site-specific labeling of radionuclides. Furthermore, the MAC degrades under hypoxic conditions, enabling the release of loaded drugs upon reaching tumor tissues. Through precise loading and targeted delivery of radionuclides and anticancer drugs, MAC achieves efficient cancer diagnosis and combined radio-chemotherapy in breast cancer cell (4T1)-bearing mice. Considering that SAC4A can load many anticancer drugs, MAC may provide a promising platform for effective combination radio-chemotherapy.

A Light Gradient-Boosting Machine algorithm with Tree-Structured Parzen Estimator for breast cancer diagnosis

Breast cancer is a common and potentially life-threatening disease. Early and accurate diagnosis of breast cancer is crucial for effective treatment and improved patient outcomes. This study proposed using the Light Gradient-Boosting Machine (LightGBM) algorithm, Borderline- Synthetic Minority Oversampling Technique (SMOTE), and the Tree-Structured Parzen Estimator (TPE) for hyperparameter tuning to enhance the effectiveness of the Machine Learning (ML) model for diagnosing breast cancer. A 10-fold cross-validated TPE optimized Borderline-SMOTE LightGBM classifier was modelled on the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset and evaluated for its performance compared to a baseline LightGBM model. The TPE-optimized Borderline-SMOTE LightGBM model exhibited a significant improvement in performance over the baseline model, achieving an average accuracy of 99.12%, specificity of 100%, precision of 100%, recall of 97.62%, F1-score of 98.80%, and a Mathews Correlation Coefficient of 98.12%. Compared to previous studies, the TPE-optimized Borderline-SMOTE LightGBM model performed exceptionally well. The study demonstrates the effectiveness of using data augmentation and hyperparameter optimization techniques to improve the performance of ML models for breast cancer diagnosis, which has significant implications for the medical field where the accurate and efficient diagnosis of breast cancer is critical.

Optimized lung cancer detection by amended whale optimizer and rough set theory

AbstractThe current paper proposes a new hierarchical procedure for efficient diagnosis of lung cancer computed tomography (CT) images. Here, after noise removal based on median filtering, a contrast enhancement based on general histogram equalization (GHE) has been utilized. Then, a modified version of K‐means clustering has been used for the area of interest segmentation in the CT images. The major characteristics of the segmented images have been selected during an optimization technique and the outputs are injected into an optimized radial basis function (RBF) network for the final classification. Optimization in the classification stage and feature selection is by an improved metaheuristic technique, called Amended Whale Optimization Algorithm was proposed. The designed method is then applied to “The RIDER Lung CT” database and its achievements are validated by several latest techniques to show its higher efficacy.

An efficient hybrid computer-aided breast cancer diagnosis system with wavelet packet transform and synthetically-generated contrast-enhanced spectral mammography images

Contrast-enhanced spectral mammography (CESM) is an emerging modality for breast cancer diagnosis. This work investigates the feasibility of a computationally-efficient computer-aided diagnosis (CAD) system for breast lesion classification. Moreover, to avoid the need for intravenous contrast agents, we investigated the synthesis of contrast-enhanced (SynCESM) images. A total of 504 pairs of low-energy (LE) and CESM images were collected from 160 female subjects. A semi-automatic active-contour method was used for lesion segmentation. Then, 20 morphological and textural features were extracted. To improve the computational efficiency of the proposed system, the wavelet packet transform (WPT) was applied. Then, the same features were extracted from the WPT-approximated segmented lesions. Using LE images, a sigmoid-kernel SVM classifier exhibited a 90.20% accuracy, an 88.39% sensitivity, an 88.26% specificity, and a 0.92 AUC. The per-frame classification time was significantly reduced from 1.1046 s to 0.0734 s with WPT. Using CESM images, we achieved a 93.26% accuracy, a 95.94% sensitivity, a 93.37% specificity, a 0.94 AUC, and a 0.0657 s classification time. Interestingly, with SynCESM images, reasonable performance was still obtained with a 92.14% accuracy, a 93.87% sensitivity, a 91.58% specificity, a 0.93 AUC and a 0.0657 s classification time. Finally, with hybrid pairs of LE and CESM images, we got the best performance with a 96.87% accuracy, a 97.23% sensitivity, a 95.47% specificity, a 0.98 AUC, and a 0.0696 s classification time. The results demonstrate several advantages of the proposed system including its clinical feasibility, lower complexity, and reduced need for contrast agents via synthetic data generation.

Porphyrin Macrocycles: General Properties and Theranostic Potential.

Despite specialists' efforts to find the best solutions for cancer diagnosis and therapy, this pathology remains the biggest health threat in the world. Global statistics concerning deaths associated with cancer are alarming; therefore, it is necessary to intensify interdisciplinary research in order to identify efficient strategies for cancer diagnosis and therapy, by using new molecules with optimal therapeutic potential and minimal adverse effects. This review focuses on studies of porphyrin macrocycles with regard to their structural and spectral profiles relevant to their applicability in efficient cancer diagnosis and therapy. Furthermore, we present a critical overview of the main commercial formulations, followed by short descriptions of some strategies approached in the development of third-generation photosensitizers.

Proteogenomic Approaches to Understand Gene Mutations and Protein Structural Alterations in Colon Cancer

With colon cancer being one of the deadliest and most common cancers, understanding the mechanisms behind colon cancer is crucial in improving therapies. One of the newest approaches in cancer research is the concept of proteogenomics. While genomic data is not sufficient to understand cancer, the integration of multi-omics data including proteomics in conjugation with protein modeling has a better potential to elucidate protein structural alterations and characterize tumors. This enables a more efficient diagnosis of cancer and improves remedial strategies. In this review, we aim to discuss the linkage between gene mutations and protein structural alterations that lead to colon cancer. Topics include alterations in the glycoproteome and structures of proteases that impact colon cancer development. Additionally, we highlight the importance of precision oncology with an emphasis on proteogenomic approaches, protein modeling, and the potential impact on colon cancer therapy.

Diagnostic accuracy and added value of blood-based protein biomarkers for pancreatic cancer: A meta-analysis of aggregate and individual participant data.

Novel blood-based protein biomarkers may be of value for efficient, accurate, and non-invasive diagnosis of pancreatic cancer. This study assesses the diagnostic accuracy of newly recognized blood-based protein biomarkers for detecting pancreatic cancer, and investigates their added value to CA19-9, the common blood-based biomarker in clinical use for pancreatic cancer. PubMed, Embase, Web of Science, and the Wiley/Cochrane Library were systematically searched from inception until June 2022. A meta-analysis of aggregate and individual participant data was conducted using frequentist and Bayesian hierarchical random-effects models. The added clinical utility of protein biomarkers was investigated using bootstrap bias-corrected decision curve analyses. Aggregate data from 28 primary studies (6127 participants) were included, of which 8 studies (1790 participants) provided individual participant data. CA19-9 was significantly more accurate than MIC-1 for distinguishing pancreatic cancer from benign disease (AUC, 0.83 vs 0.74; relative diagnostic odds ratio [rDOR], 2.10 [95% CI, 0.98-4.48]; p=0.002), THBS2 (AUC, 0.87 vs 0.69; rDOR, 4.53 [2.16-9.39]; p<0.0001), TIMP-1 (AUC, 0.91 vs 0.70; rDOR, 8.00 [3.81-16.9]; p<0.0001), OPN (AUC, 0.89 vs 0.74; rDOR, 4.22 [1.13-15.6]; p<0.0001), ICAM-1 (AUC, 0.91 vs 0.68; rDOR 9.30 [0.87-99.5]; p<0.0001), and IGFBP2 (AUC, 0.91 vs 0.68; rDOR, 4.48 [0.78-24.3]; p<0.0001). The addition of these novel protein biomarkers to CA19-9 did not significantly improve the AUC, and resulted in minor increases or limited decreases in clinical utility. Novel protein biomarkers have moderate diagnostic accuracy, do not outperform CA19-9 in differentiating pancreatic cancer from benign disease, and show limited added clinical value to CA19-9. We propose recommendations to aid the development of minimally invasive diagnostic tests with sufficient clinical utility to improve the management of patients with suspected pancreatic cancer. Bennink Foundation, Dutch Cancer Foundation (KWF Kankerbestrijding), and AIRC.

Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network.

Medical image analysis places a significant focus on breast cancer, which poses a significant threat to women's health and contributes to many fatalities. An early and precise diagnosis of breast cancer through digital mammograms can significantly improve the accuracy of disease detection. Computer-aided diagnosis (CAD) systems must analyze the medical imagery and perform detection, segmentation, and classification processes to assist radiologists with accurately detecting breast lesions. However, early-stage mammography cancer detection is certainly difficult. The deep convolutional neural network has demonstrated exceptional results and is considered a highly effective tool in the field. This study proposes a computational framework for diagnosing breast cancer using a ResNet-50 convolutional neural network to classify mammogram images. To train and classify the INbreast dataset into benign or malignant categories, the framework utilizes transfer learning from the pretrained ResNet-50 CNN on ImageNet. The results revealed that the proposed framework achieved an outstanding classification accuracy of 93%, surpassing other models trained on the same dataset. This novel approach facilitates early diagnosis and classification of malignant and benign breast cancer, potentially saving lives and resources. These outcomes highlight that deep convolutional neural network algorithms can be trained to achieve highly accurate results in various mammograms, along with the capacity to enhance medical tools by reducing the error rate in screening mammograms.

An AI-assisted tool for efficient prostate cancer diagnosis in low-grade and low-volume cases

Pathologists diagnose prostate cancer by core needle biopsy. In low-grade and low-volume cases, they look for a few malignant glands out of hundreds within a core. They may miss a few malignant glands, resulting in repeat biopsies or missed therapeutic opportunities. This study developed a multi-resolution deep-learning pipeline to assist pathologists in detecting malignant glands in core needle biopsies of low-grade and low-volume cases. Analyzing a gland at multiple resolutions, our model exploited morphology and neighborhood information, which were crucial in prostate gland classification. We developed and tested our pipeline on the slides of a local cohort of 99 patients in Singapore. Besides, we made the images publicly available, becoming the first digital histopathology dataset of patients of Asian ancestry with prostatic carcinoma. Our multi-resolution classification model achieved an area under the receiver operating characteristic curve (AUROC) value of 0.992 (95% confidence interval [CI]: 0.985-0.997) in the external validation study, showing the generalizability of our multi-resolution approach.