Single-detector multiplex imaging flow cytometry for cancer cell classification with deep learning.

Imaging flow cytometry, which combines the advantages of flow cytometry and microscopy, has emerged as a powerful tool for cell analysis in various biomedical fields such as cancer detection. In this study, we develop multiplex imaging flow cytometry (mIFC) by employing a spatial wavelength division multiplexing technique. Our mIFC can simultaneously obtain brightfield and multi-color fluorescence images of individual cells in flow, which are excited by a metal halide lamp and measured by a single detector. Statistical analysis results of multiplex imaging experiments with resolution test lens, magnification test lens, and fluorescent microspheres validate the operation of the mIFC with good imaging channel consistency and micron-scale differentiation capabilities. A deep learning method is designed for multiplex image processing that consists of three deep learning networks (U-net, very deep super resolution, and visual geometry group 19). It is demonstrated that the cluster of differentiation 24 (CD24) imaging channel is more sensitive than the brightfield, nucleus, or cancer antigen 125 (CA125) imaging channel in classifying the three types of ovarian cell lines (IOSE80 normal cell, A2780, and OVCAR3 cancer cells). An average accuracy rate of 97.1% is achieved for the classification of these three types of cells by deep learning analysis when all four imaging channels are considered. Our single-detector mIFC is promising for the development of future imaging flow cytometers and for the automatic single-cell analysis with deep learning in various biomedical fields.

Detecting pulmonary malignancy against benign nodules using noninvasive cell-free DNA fragmentomics assay.

BACKGROUND: Early screening using low-dose computed tomography (LDCT) can reduce mortality caused by non-small-cell lung cancer. However, ∼25% of the 'suspicious' pulmonary nodules identified by LDCT are later confirmed benign through resection surgery, adding to patients' discomfort and the burden on the healthcare system. In this study, we aim to develop a noninvasive liquid biopsy assay for distinguishing pulmonary malignancy from benign yet 'suspicious' lung nodules using cell-free DNA (cfDNA) fragmentomics profiling. METHODS: An independent training cohort consisting of 193 patients with malignant nodules and 44 patients with benign nodules was used to construct a machine learning model. Base models using four different fragmentomics profiles were optimized using an automated machine learning approach before being stacked into the final predictive model. An independent validation cohort, including 96 malignant nodules and 22 benign nodules, and an external test cohort, including 58 malignant nodules and 41 benign nodules, were used to assess the performance of the stacked ensemble model. RESULTS: Our machine learning models demonstrated excellent performance in detecting patients with malignant nodules. The area under the curves reached 0.857 and 0.860 in the independent validation cohort and the external test cohort, respectively. The validation cohort achieved an excellent specificity (68.2%) at the targeted 90% sensitivity (89.6%). An equivalently good performance was observed while applying the cut-off to the external cohort, which reached a specificity of 63.4% at 89.7% sensitivity. A subgroup analysis for the independent validation cohort showed that the sensitivities for detecting various subgroups of nodule size (<1 cm: 91.7%; 1-3 cm: 88.1%; >3 cm: 100%; unknown: 100%) and smoking history (yes: 88.2%; no: 89.9%) all remained high among the lung cancer group. CONCLUSIONS: Our cfDNA fragmentomics assay can provide a noninvasive approach to distinguishing malignant nodules from radiographically suspicious but pathologically benign ones, amending LDCT false positives.

Transformer-based AI technology improves early ovarian cancer diagnosis using cfDNA methylation markers.

Epithelial ovarian cancer (EOC) is the deadliest women's cancer and has a poor prognosis. Early detection is the key for improving survival (a 5-year survival rate in stage I/II is over 70% compared to that of 25% in stage III/IV) and can be achieved through methylation markers from circulating cell-free DNA (cfDNA) using a liquid biopsy. In this study, we first identify top 500 EOC markers differentiating EOC from healthy female controls from 3.3 million methylome-wide CpG sites and validated them in 1,800 independent cfDNA samples. We then utilize a pretrained AI transformer system called MethylBERT to develop an EOC diagnostic model which achieves 80% sensitivity and 95% specificity in early-stage EOC diagnosis. We next develop a simple digital droplet PCR (ddPCR) assay which archives good performance, facilitating early EOC detection.

Hybrid healthcare unit recommendation system using computational techniques with lung cancer segmentation.

Introduction: Our research addresses the critical need for accurate segmentation in medical healthcare applications, particularly in lung nodule detection using Computed Tomography (CT). Our investigation focuses on determining the particle composition of lung nodules, a vital aspect of diagnosis and treatment planning. Methods: Our model was trained and evaluated using several deep learning classifiers on the LUNA-16 dataset, achieving superior performance in terms of the Probabilistic Rand Index (PRI), Variation of Information (VOI), Region of Interest (ROI), Dice Coecient, and Global Consistency Error (GCE). Results: The evaluation demonstrated a high accuracy of 91.76% for parameter estimation, confirming the effectiveness of the proposed approach. Discussion: Our investigation focuses on determining the particle composition of lung nodules, a vital aspect of diagnosis and treatment planning. We proposed a novel segmentation model to identify lung disease from CT scans to achieve this. We proposed a learning architecture that combines U-Net with a Two-parameter logistic distribution for accurate image segmentation; this hybrid model is called U-Net++, leveraging Contrast Limited Adaptive Histogram Equalization (CLAHE) on a 5,000 set of CT scan images.

Diagnostic accuracy of digital breast tomosynthesis and digital mammography in women with dense or non-dense breast tissue: A systematic review and meta-analysis.

BACKGROUND: Despite its excellent screening effectiveness and sensitivity for breast cancer (BC), digital breast tomosynthesis (DBT) is controversial due to its high radiation exposure and long reading time. This study examines the diagnostic accuracy of DBT and digital mammography (DM) for BC screening and diagnosis in women with dense or non-dense breast tissue. MATERIAL AND METHODS: PRISMA-compliant searches were performed on Medline, Embase, PubMed, Web of Science, and the Cochrane databases for articles comparing DBT and DM for BC screening until March 2023. Meta-analysis was performed using RevMan sofware, and the Cochrane Risk of Bias Assessment Tool was employed to assess study quality. RESULTS: This meta-analysis included 11 trials with a total of 2,124,018 individuals. Screening with DBT resulted in a greater cancer detection rate, as demonstrated by a risk ratio (RR) of 1.27 (95% confidence interval (95% CI): 1.14-1.41). Digital breast tomosynthesis also had a reduced recall rate, with a RR of 0.88 (95% CI: 0.78-0.99), higher sensitivity and specificity values (pooled sensitivity of 0.91 (95% CI: 0.59-0.99)) and pooled specificity of 0.90 (95% CI: 0.42-1.0)) than DM (pooled sensitivity of 0.86 (95% CI: 0.52-1.0) and pooled specificity of 0.81 (95% CI: 0.12-1.0)). All acquired data exhibited reliability, lack of bias and statistical significance (p < 0.05). CONCLUSION: Digital breast tomosynthesis is a more effective screening and diagnostic assessment tool for women with dense or non-dense breasts than DM in terms of incremental cancer detection, sensitivity and recall rate.

Mammary Paget's Disease as a Sign of Local Recurrence Two Decades Following Breast Conservation and Adjuvant Therapy for Early Stage Breast Cancer.

Reports of mammary Paget's disease (MPD) as a manifestation of breast cancer recurrence are rare. MPD presents a particular challenge when emerging more than two decades after a breast cancer treated with evidence-based therapy. There is a broad spectrum of non-malignant causes for dermatitis of the nipple during the initial presentation that may delay cancer work-up. This case highlights the MPD work-up and management in the context of a personal history of breast cancer. This unique clinical presentation emphasizes the importance of vigilant cancer surveillance for timely intervention, especially for a presumed cured cancer.

Ductal Carcinoma In Situ.

In breast cancer (BC) pathogenesis models, normal cells acquire somatic mutations and there is a stepwise progression from high-risk lesions and ductal carcinoma in situ to invasive cancer. The precancer biology of mammary tissue warrants better characterization to understand how different BC subtypes emerge. Primary methods for BC prevention or risk reduction include lifestyle changes, surgery, and chemoprevention. Surgical intervention for BC prevention involves risk-reducing prophylactic mastectomy, typically performed either synchronously with the treatment of a primary tumor or as a bilateral procedure in high-risk women. Chemoprevention with endocrine therapy carries adherence-limiting toxicity.

Improving skin cancer detection by Raman spectroscopy using convolutional neural networks and data augmentation.

Background: Our previous studies have demonstrated that Raman spectroscopy could be used for skin cancer detection with good sensitivity and specificity. The objective of this study is to determine if skin cancer detection can be further improved by combining deep neural networks and Raman spectroscopy. Patients and methods: Raman spectra of 731 skin lesions were included in this study, containing 340 cancerous and precancerous lesions (melanoma, basal cell carcinoma, squamous cell carcinoma and actinic keratosis) and 391 benign lesions (melanocytic nevus and seborrheic keratosis). One-dimensional convolutional neural networks (1D-CNN) were developed for Raman spectral classification. The stratified samples were divided randomly into training (70%), validation (10%) and test set (20%), and were repeated 56 times using parallel computing. Different data augmentation strategies were implemented for the training dataset, including added random noise, spectral shift, spectral combination and artificially synthesized Raman spectra using one-dimensional generative adversarial networks (1D-GAN). The area under the receiver operating characteristic curve (ROC AUC) was used as a measure of the diagnostic performance. Conventional machine learning approaches, including partial least squares for discriminant analysis (PLS-DA), principal component and linear discriminant analysis (PC-LDA), support vector machine (SVM), and logistic regression (LR) were evaluated for comparison with the same data splitting scheme as the 1D-CNN. Results: The ROC AUC of the test dataset based on the original training spectra were 0.886±0.022 (1D-CNN), 0.870±0.028 (PLS-DA), 0.875±0.033 (PC-LDA), 0.864±0.027 (SVM), and 0.525±0.045 (LR), which were improved to 0.909±0.021 (1D-CNN), 0.899±0.022 (PLS-DA), 0.895±0.022 (PC-LDA), 0.901±0.020 (SVM), and 0.897±0.021 (LR) respectively after augmentation of the training dataset (p<0.0001, Wilcoxon test). Paired analyses of 1D-CNN with conventional machine learning approaches showed that 1D-CNN had a 1-3% improvement (p<0.001, Wilcoxon test). Conclusions: Data augmentation not only improved the performance of both deep neural networks and conventional machine learning techniques by 2-4%, but also improved the performance of the models on spectra with higher noise or spectral shifting. Convolutional neural networks slightly outperformed conventional machine learning approaches for skin cancer detection by Raman spectroscopy.

Clinical Utility of Tumor-Naïve Presurgical Circulating Tumor DNA Detection in Early-Stage NSCLC.

OBJECTIVES: The use of tumor-informed circulating tumor DNA (ctDNA) testing in patients with early-stage disease before surgery is limited, mainly owing to restricted tissue access and extended turnaround times. This study aimed to evaluate the clinical value of a tumor-naïve, methylation-based cell-free DNA assay in a large cohort of patients with resected NSCLC. METHOD: We analyzed presurgical plasma samples from 895 patients with EGFR and anaplastic lymphoma kinase-wild-type, clinical stage I or II NSCLC. The ctDNA status was evaluated for its prognostic significance in relation to tumor volume, metabolic activity, histologic diagnosis, histologic subtypes, and clinical-to-pathologic TNM upstaging. RESULTS: Presurgical ctDNA detection was observed in 55 of 414 patients (13%) with clinical stage I lung adenocarcinoma (LUAD) and was associated with poor recurrence-free survival (2-year recurrence-free survival 69% versus 91%; log-rank p < 0.001), approaching that of clinical stage II LUAD. Presurgical ctDNA detection was not prognostic in patients with clinical stage II LUAD or non-LUAD. Within LUAD, tumor volume and positron emission tomography avidity interacted to predict presurgical ctDNA detection. Moreover, presurgical ctDNA detection was predictive of the postsurgical discovery of International Association for the Study of Lung Cancer grade 3 tumors (p < 0.001) and pathologic TNM upstaging (p < 0.001). Notably, presurgical ctDNA detection strongly correlated with higher programmed death-ligand 1 expression in tumors (positive rates 28% versus 55%, p < 0.001), identifying a subgroup likely to benefit from anti-programmed death-ligand 1 therapies. CONCLUSION: These findings support the integration of ctDNA testing into routine diagnostic workflows in early-stage NSCLC without the need for tumor tissue profiling. Furthermore, it is clinically useful in identifying patients at high risk who might benefit from innovative treatments, including neoadjuvant immune checkpoint inhibitors.

Vitacrystallography: Structural Biomarkers of Breast Cancer Obtained by X-ray Scattering.

With breast cancer being one of the most widespread causes of death for women, there is an unmet need for its early detection. For this purpose, we propose a non-invasive approach based on X-ray scattering. We measured samples from 107 unique patients provided by the Breast Cancer Now Tissue Biobank, with the total dataset containing 2958 entries. Two different sample-to-detector distances, 2 and 16 cm, were used to access various structural biomarkers at distinct ranges of momentum transfer values. The biomarkers related to lipid metabolism are consistent with those of previous studies. Machine learning analysis based on the Random Forest Classifier demonstrates excellent performance metrics for cancer/non-cancer binary decisions. The best sensitivity and specificity values are 80% and 92%, respectively, for the sample-to-detector distance of 2 cm and 86% and 83% for the sample-to-detector distance of 16 cm.

Extracellular Vesicles, Circadian Rhythms, and Cancer: A Comprehensive Review with Emphasis on Hepatocellular Carcinoma.

This review comprehensively explores the complex interplay between extracellular vesicles (ECVs)/exosomes and circadian rhythms, with a focus on the role of this interaction in hepatocellular carcinoma (HCC). Exosomes are nanovesicles derived from cells that facilitate intercellular communication by transporting bioactive molecules such as proteins, lipids, and RNA/DNA species. ECVs are implicated in a range of diseases, where they play crucial roles in signaling between cells and their surrounding environment. In the setting of cancer, ECVs are known to influence cancer initiation and progression. The scope of this review extends to all cancer types, synthesizing existing knowledge on the various roles of ECVs. A unique aspect of this review is the emphasis on the circadian-controlled release and composition of exosomes, highlighting their potential as biomarkers for early cancer detection and monitoring metastasis. We also discuss how circadian rhythms affect multiple cancer-related pathways, proposing that disruptions in the circadian clock can alter tumor development and treatment response. Additionally, this review delves into the influence of circadian clock components on ECV biogenesis and their impact on reshaping the tumor microenvironment, a key component driving HCC progression. Finally, we address the potential clinical applications of ECVs, particularly their use as diagnostic tools and drug delivery vehicles, while considering the challenges associated with clinical implementation.

Innovative Semi-Nested Realtime PCR Assay with Extendable Blocking Probe for Enhanced Analysis of SEPT9 Methylation in Colorectal Cancer.

(1) Background: The detection of methylated SEPT9 (mSEPT9) in plasma is a promising approach to non-invasive colorectal cancer (CRC) screening. Traditional approaches have limitations in sensitivity and cost-effectiveness, particularly in resource-limited settings. (2) Methods: We developed a semi-nested realtime PCR assay utilizing extendable blocking probes (ExBP) to enhance the detection of low-level mSEPT9 based on DNA melting. This assay allows for the discrimination of mSEPT9 in the presence of high concentrations of non-methylated SEPT9 (up to 100,000 times higher). (3) Results: The assay demonstrated a sensitivity of 73.91% and specificity of 80%, showcasing its ability to detect very low levels of methylated DNA effectively. The innovative use of ExBP without costly modified probes simplifies the assay setup and reduces the overall costs, enhancing its applicability in diverse clinical settings. (4) Conclusions: This novel assay significantly improves the detection of mSEPT9, offering a potential advance in CRC screening and monitoring. Its cost-efficiency and high sensitivity make it particularly suitable for the early detection and management of CRC, especially in settings with limited resources. Future studies are encouraged to validate this assay in larger populations to establish its clinical benefits and practical utility.

Understanding the Landscape of Multi-Cancer Detection Tests: The Current Data and Clinical Considerations.

Multi-cancer detection (MCD) tests are blood-based assays that screen for multiple cancers concurrently and offer a promising approach to improve early cancer detection and screening uptake. To date, there have been two prospective interventional studies evaluating MCD tests as a screening tool in human subjects. No MCD tests are currently approved by the FDA, but there is one commercially available MCD test. Ongoing trials continue to assess the efficacy, safety, and cost implications of MCD tests. In this review, we discuss the performance of CancerSEEK and Galleri, two leading MCD platforms, and discuss the clinical consideration for the broader application of this new technology.

Detection of invasive ductal carcinoma in quadrant breast areas by electrical impedance tomography implemented with gaussian relaxation-time distribution (EIT-GRTD).

Invasive ductal carcinoma (IDC) in breast specimens has been detected in the quadrant breast area: (I) upper outer, (II) upper inner, (III) lower inner, and (IV) lower outer areas by electrical impedance tomography implemented with Gaussian relaxation-time distribution (EIT-GRTD). The EIT-GRTD consists of two steps which are (1) the optimum frequencyfoptselection and (2) the time constant enhancement of breast imaging reconstruction.foptis characterized by a peak in the majority measurement pair of the relaxation-time distribution functionγ,which indicates the presence of IDC.γrepresents the inverse of conductivity and indicates the response of breast tissues to electrical currents across varying frequencies based on the Voigt circuit model. The EIT-GRTD is quantitatively evaluated by multi-physics simulations using a hemisphere container of mimic breast, consisting of IDC and adipose tissues as normal breast tissue under one condition with known IDC in quadrant breast area II. The simulation results show that EIT-GRTD is able to detect the IDC in four layers atfopt= 30, 170 Hz. EIT-GRTD is applied in the real breast by employed six mastectomy specimens from IDC patients. The placement of the mastectomy specimens in a hemisphere container is an important factor in the success of quadrant breast area reconstruction. In order to perform the evaluation, EIT-GRTD reconstruction images are compared to the CT scan images. The experimental results demonstrate that EIS-GRTD exhibits proficiency in the detection of the IDC in quadrant breast areas while compared qualitatively to CT scan images.

Revisiting the standards of cancer detection and therapy alongside their comparison to modern methods.

In accordance with the World Health Organization data, cancer remains at the forefront of fatal diseases. An upward trend in cancer incidence and mortality has been observed globally, emphasizing that efforts in developing detection and treatment methods should continue. The diagnostic path typically begins with learning the medical history of a patient; this is followed by basic blood tests and imaging tests to indicate where cancer may be located to schedule a needle biopsy. Prompt initiation of diagnosis is crucial since delayed cancer detection entails higher costs of treatment and hospitalization. Thus, there is a need for novel cancer detection methods such as liquid biopsy, elastography, synthetic biosensors, fluorescence imaging, and reflectance confocal microscopy. Conventional therapeutic methods, although still common in clinical practice, pose many limitations and are unsatisfactory. Nowadays, there is a dynamic advancement of clinical research and the development of more precise and effective methods such as oncolytic virotherapy, exosome-based therapy, nanotechnology, dendritic cells, chimeric antigen receptors, immune checkpoint inhibitors, natural product-based therapy, tumor-treating fields, and photodynamic therapy. The present paper compares available data on conventional and modern methods of cancer detection and therapy to facilitate an understanding of this rapidly advancing field and its future directions. As evidenced, modern methods are not without drawbacks; there is still a need to develop new detection strategies and therapeutic approaches to improve sensitivity, specificity, safety, and efficacy. Nevertheless, an appropriate route has been taken, as confirmed by the approval of some modern methods by the Food and Drug Administration.

Examining feature extraction and classification modules in machine learning for diagnosis of low-dose computed tomographic screening-detected in vivo lesions.

Purpose: Medical imaging-based machine learning (ML) for computer-aided diagnosis of in vivo lesions consists of two basic components or modules of (i) feature extraction from non-invasively acquired medical images and (ii) feature classification for prediction of malignancy of lesions detected or localized in the medical images. This study investigates their individual performances for diagnosis of low-dose computed tomography (CT) screening-detected lesions of pulmonary nodules and colorectal polyps. Approach: Three feature extraction methods were investigated. One uses the mathematical descriptor of gray-level co-occurrence image texture measure to extract the Haralick image texture features (HFs). One uses the convolutional neural network (CNN) architecture to extract deep learning (DL) image abstractive features (DFs). The third one uses the interactions between lesion tissues and X-ray energy of CT to extract tissue-energy specific characteristic features (TFs). All the above three categories of extracted features were classified by the random forest (RF) classifier with comparison to the DL-CNN method, which reads the images, extracts the DFs, and classifies the DFs in an end-to-end manner. The ML diagnosis of lesions or prediction of lesion malignancy was measured by the area under the receiver operating characteristic curve (AUC). Three lesion image datasets were used. The lesions' tissue pathological reports were used as the learning labels. Results: Experiments on the three datasets produced AUC values of 0.724 to 0.878 for the HFs, 0.652 to 0.965 for the DFs, and 0.985 to 0.996 for the TFs, compared to the DL-CNN of 0.694 to 0.964. These experimental outcomes indicate that the RF classifier performed comparably to the DL-CNN classification module and the extraction of tissue-energy specific characteristic features dramatically improved AUC value. Conclusions: The feature extraction module is more important than the feature classification module. Extraction of tissue-energy specific characteristic features is more important than extraction of image abstractive and characteristic features.

Optimization of ultracentrifugation-based method to enhance the purity and proteomic profiling depth of plasma-derived extracellular vesicles and particles.

Circulating extracellular vesicles and particles (EVPs) are being investigated as potential biomarkers for early cancer detection, prognosis, and disease monitoring. However, the suboptimal purity of EVPs isolated from peripheral blood plasma has posed a challenge of in-depth analysis of the EVP proteome. Here, we compared the effectiveness of different methods for isolating EVPs from healthy donor plasma, including ultracentrifugation (UC)-based protocols, phosphatidylserine-Tim4 interaction-based affinity capture (referred to as "PS"), and several commercial kits. Modified UC methods with an additional UC washing or size exclusion chromatography step substantially improved EVP purity and enabled the detection of additional proteins via proteomic mass spectrometry, including many plasma membrane and cytoplasmic proteins involved in vesicular regulation pathways. This improved performance was reproduced in cancer patient plasma specimens, resulting in the identification of a greater number of differentially expressed EVP proteins, thus expanding the range of potential biomarker candidates. However, PS and other commercial kits did not outperform UC-based methods in improving plasma EVP purity. PS yielded abundant contaminating proteins and a biased enrichment for specific EVP subsets, thus unsuitable for proteomic profiling of plasma EVPs. Therefore, we have optimized UC-based protocols for circulating EVP isolation, which enable further in-depth proteomic analysis for biomarker discovery.

Latent Diffusion Models with Image-Derived Annotations for Enhanced AI-Assisted Cancer Diagnosis in Histopathology.

Artificial Intelligence (AI)-based image analysis has immense potential to support diagnostic histopathology, including cancer diagnostics. However, developing supervised AI methods requires large-scale annotated datasets. A potentially powerful solution is to augment training data with synthetic data. Latent diffusion models, which can generate high-quality, diverse synthetic images, are promising. However, the most common implementations rely on detailed textual descriptions, which are not generally available in this domain. This work proposes a method that constructs structured textual prompts from automatically extracted image features. We experiment with the PCam dataset, composed of tissue patches only loosely annotated as healthy or cancerous. We show that including image-derived features in the prompt, as opposed to only healthy and cancerous labels, improves the Fréchet Inception Distance (FID) by 88.6. We also show that pathologists find it challenging to detect synthetic images, with a median sensitivity/specificity of 0.55/0.55. Finally, we show that synthetic data effectively train AI models.

Factors Associated With Continuous Use of a Cancer Education Metaverse Platform: Mixed Methods Study.

BACKGROUND: Early detection of cancer and provision of appropriate treatment can increase the cancer cure rate and reduce cancer-related deaths. Early detection requires improving the cancer screening quality of each medical institution and enhancing the capabilities of health professionals through tailored education in each field. However, during the COVID-19 pandemic, regional disparities in educational infrastructure emerged, and educational accessibility was restricted. The demand for remote cancer education services to address these issues has increased, and in this study, we considered medical metaverses as a potential means of meeting these needs. In 2022, we used Metaverse Educational Center, developed for the virtual training of health professionals, to train radiologic technologists remotely in mammography positioning. OBJECTIVE: This study aims to investigate the user experience of the Metaverse Educational Center subplatform and the factors associated with the intention for continuous use by focusing on cases of using the subplatform in a remote mammography positioning training project. METHODS: We conducted a multicenter, cross-sectional survey between July and December 2022. We performed a descriptive analysis to examine the Metaverse Educational Center user experience and a logistic regression analysis to clarify factors closely related to the intention to use the subplatform continuously. In addition, a supplementary open-ended question was used to obtain feedback from users to improve Metaverse Educational Center. RESULTS: Responses from 192 Korean participants (male participants: n=16, 8.3%; female participants: n=176, 91.7%) were analyzed. Most participants were satisfied with Metaverse Educational Center (178/192, 92.7%) and wanted to continue using the subplatform in the future (157/192, 81.8%). Less than half of the participants (85/192, 44.3%) had no difficulty in wearing the device. Logistic regression analysis results showed that intention for continuous use was associated with satisfaction (adjusted odds ratio 3.542, 95% CI 1.037-12.097; P=.04), immersion (adjusted odds ratio 2.803, 95% CI 1.201-6.539; P=.02), and no difficulty in wearing the device (adjusted odds ratio 2.020, 95% CI 1.004-4.062; P=.049). However, intention for continuous use was not associated with interest (adjusted odds ratio 0.736, 95% CI 0.303-1.789; P=.50) or perceived ease of use (adjusted odds ratio 1.284, 95% CI 0.614-2.685; P=.51). According to the qualitative feedback, Metaverse Educational Center was useful in cancer education, but the experience of wearing the device and the types and qualities of the content still need to be improved. CONCLUSIONS: Our results demonstrate the positive user experience of Metaverse Educational Center by focusing on cases of using the subplatform in a remote mammography positioning training project. Our results also suggest that improving users' satisfaction and immersion and ensuring the lack of difficulty in wearing the device may enhance their intention for continuous use of the subplatform.