UNASSIGNED: The COVID-19 pandemic had a substantial impact on cancer services. The aim of our study was to evaluate the recovery of endoscopic activity and cancer detection after the COVID-19 pandemic.
UNASSIGNED: Endoscopic data from January 2019 to December 2020 were retrospectively collected to assess the endoscopic activity and cancer detection during the COVID-19 peak period (February 2020) and the post-COVID-19 peak period (March to July 2020).
UNASSIGNED: The COVID-19 pandemic almost brought endoscopic activity and cancer detection to a standstill. Diagnostic procedure and endoscopic resection showed the greatest reduction. With the decline in COVID-19 infections, endoscopic activity gradually returned to previous level in July. However, the detection rate of gastric cancer resumed in September, whereas colorectal cancer resumed in August. The monthly detection rates of gastric and colorectal cancers decreased from their initial peaks of 2.98 % and 6.45 %, respectively, and finally were even lower than the average in 2019. Similarly, the mean age of patients who received endoscopy also declined as the detection rates resumed. The increasing colonoscopies allowed the missing colorectal cancer patients to be caught up. In contrast, it was expected that 6.69 % of gastric cancer patients were missed and did not receive needed endoscopy.
UNASSIGNED: The recovery of cancer detection occurred later than that of endoscopic activity, especially for gastric cancer. Older people were vulnerable to the continuous impact of COVID-19 pandemic than young people for seeking medical services. Urgent efforts are required to recover and maintain cancer services before subsequent waves of the COVID-19 pandemic.

Guided surgery has demonstrated significant improvements in patient outcomes in some disease processes. Interest in this field has led to substantial growth in the technologies under investigation. Most likely no single technology will prove to be \"best,\" and combinations of macro- and microscale guidance-using radiological imaging navigation, probes (activatable, perfusion, and molecular-targeted; large- and small-molecule), autofluorescence, tissue intrinsic optical properties, bioimpedance, and other characteristics-will offer patients and surgeons the greatest opportunity for high-success/low-morbidity medical interventions. Problems are arising, however, from the lack of valid testing formats; surgical training simulators suffer the same problems. Small animal models do not accurately recreate human anatomy, especially in terms of tissue volume. Large animal models are expensive and have difficulty replicating many pathological states, particularly when molecular specificity for individual cancers is required. Furthermore, the sheer number of technologies and the potential for synergistic combination leads to exponential growth of testing requirements that is unrealistic for in vivo testing. Therefore, critical need exists to expand the ex vivo/in vitro testing platforms available to investigators and, once validated, a need to increase the acceptance of these methods for funding and regulatory endpoints. Herein is a review of the available ex vivo/in vitro testing formats for guided surgery, a review of their advantages/disadvantages, and consideration for how our field may safely and more swiftly move forward through stronger adoption of these testing and validation methods.

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.

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.

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Prostate cancer diagnosis and treatment relies on precise MRI lesion segmentation, a challenge notably for small (<15 mm) and intermediate (15-30 mm) lesions. Our study introduces ProLesA-Net, a multi-channel 3D deep-learning architecture with multi-scale squeeze and excitation and attention gate mechanisms. Tested against six models across two datasets, ProLesA-Net significantly outperformed in key metrics: Dice score increased by 2.2%, and Hausdorff distance and average surface distance improved by 0.5 mm, with recall and precision also undergoing enhancements. Specifically, for lesions under 15 mm, our model showed a notable increase in five key metrics. In summary, ProLesA-Net consistently ranked at the top, demonstrating enhanced performance and stability. This advancement addresses crucial challenges in prostate lesion segmentation, enhancing clinical decision making and expediting treatment processes.

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.

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.

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.

Aptamers are short oligonucleotides with single-stranded regions or peptides that recently started to transform the field of diagnostics. Their unique ability to bind to specific target molecules with high affinity and specificity is at least comparable to many traditional biorecognition elements. Aptamers are synthetically produced, with a compact size that facilitates deeper tissue penetration and improved cellular targeting. Furthermore, they can be easily modified with various labels or functional groups, tailoring them for diverse applications. Even more uniquely, aptamers can be regenerated after use, making aptasensors a cost-effective and sustainable alternative compared to disposable biosensors. This review delves into the inherent properties of aptamers that make them advantageous in established diagnostic methods. Furthermore, we will examine some of the limitations of aptamers, such as the need to engage in bioinformatics procedures in order to understand the relationship between the structure of the aptamer and its binding abilities. The objective is to develop a targeted design for specific targets. We analyse the process of aptamer selection and design by exploring the current landscape of aptamer utilisation across various industries. Here, we illuminate the potential advantages and applications of aptamers in a range of diagnostic techniques, with a specific focus on quartz crystal microbalance (QCM) aptasensors and their integration into the well-established ELISA method. This review serves as a comprehensive resource, summarising the latest knowledge and applications of aptamers, particularly highlighting their potential to revolutionise diagnostic approaches.