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BACKGROUND & AIMS: Computer-Aided Diagnosis (CADx) assists endoscopists in differentiating between neoplastic and non-neoplastic polyps during colonoscopy. This study aimed to evaluate the impact of polyp location (proximal vs. distal colon) on the diagnostic performance of CADx for ≤5mm polyps. METHODS: We searched for studies evaluating the performance of real-time CADx alone (i.e., independently of endoscopist judgement) for predicting the histology of colorectal polyps ≤5mm. The primary endpoints were CADx sensitivity and specificity in the proximal and distal colon. Secondary outcomes were the negative predictive value (NPV), positive predictive value (PPV), and the accuracy of the CADx alone. Distal colon was limited to the rectum and sigmoid. RESULTS: We included 11 studies for analysis with a total of 7,782 <5mm polyps. CADx specificity was significantly lower in the proximal colon compared to the distal colon (62% versus 85%; Risk ratio (RR): 0.74 [95% CI: 0.72-0.84]). Conversely, sensitivity was similar (89% vs 87% (EC-1); RR: 1.00 [95% CI: 0.97-1.03]. The NPV (64% versus 93%; RR: 0.71 [95% CI: 0.64-0.79]) and accuracy (81% vs 86%; RR: 0.95 [95% CI: 0.91-0.99]) were significantly lower in the proximal than distal colon, while PPV was higher in the proximal colon (87% vs 76%; RR: 1.11 [95% CI: 1.06-1.17]). CONCLUSION: The diagnostic performance of CADx for polyps in the proximal colon is inadequate, exhibiting significantly lower specificity compared to its performance for distal polyps. While current CADx systems are suitable for use in the distal colon, they should not be employed for proximal polyps until more performant systems are developed specifically for these lesions.

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BACKGROUND: Gastrointestinal tumors, as one of the most common cancers worldwide, pose a significant threat to human health. In this context, the advent of fluorescence probe technology has offered new perspectives and methods for the diagnosis and surgical treatment of gastrointestinal tumors. However, there is currently a lack of systematic bibliometric analysis on the research concerning gastrointestinal cancer and fluorescence probes. METHOD: This study retrieved and comprehensively analyzed 1816 documents from the Web of Science database using the Cite Space tool, exploring the spatiotemporal distribution, author and subject category distribution, research themes, and keywords in this field. RESULTS: As of February 3, 2024, a total of 1816 records were retrieved, encompassing nine document types. Original research papers dominated the dataset, accounting for 89.922 %, followed by review articles at 6.773 %. We conducted a comprehensive analysis from various perspectives including countries, authors, institutions, keywords, journals, and references. Our findings reveal a strengthening trend in research on gastrointestinal cancer and fluorescent probes since 2010, with primary focus on drug delivery, endoscopy techniques, and genomic hybridization. CONCLUSION: In recent years, there has been a growing interest in the design, application, and quantitative analysis techniques of fluorescent probes, marking a notable frontier in this field. Our research findings offer fundamental insights and aid in identifying potential collaborators for future endeavors in this area.

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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.

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Background and objectives Achieving accurate real-time optical diagnoses of colorectal polyps with high-confidence predictions is crucial for appropriate decision-making in daily practice. The dual-focus (DF) magnification mode helps endoscopists scrutinize subtle features of polyp surfaces and vessel patterns. This prospective study aimed to evaluate the impact of DF imaging on enhancing the rate of high-confidence narrow-band imaging (NBI)-based optical diagnosis. Methods Consecutive adult patients who underwent colonoscopy and had small colorectal polyps (<10 mm) were enrolled between September 2022 and May 2023. The optical diagnosis of each polyp was evaluated during colonoscopy in two stages by the same endoscopist, utilizing NBI with DF magnification (NDB-DF). A confidence level was assigned to each prediction. High confidence was indicated by clinical judgment when a polyp exhibited distinctive features associated solely with one histological subtype and lacked characteristics of any other subtype. All procedures were carried out with a prototype 190 series Exera III NBI system (Olympus Corporation, Tokyo, Japan) with DF magnification. Results The study included 413 patients with 623 polyps, comprising 483 ≤ 5 mm and 140 measuring 6-9 mm. The majority were low-grade adenomas (343 lesions), with 17 identified as high-grade adenomas, and none characterized as deep submucosal invasive carcinomas. NBI-DF significantly improved the rate of high-confidence optical diagnoses compared to NBI for both ≤ 5 mm polyps (93.1% vs. 87.5%, p < 0.0001) and 6-9 mm polyps (97.9% vs. 94.2%, p = 0.03). Furthermore, DF significantly facilitated the assessment of microvessel and surface pattern criteria (p < 0.01). Conclusion DF magnification markedly enhanced the rate of high-confidence NBI-based optical predictions for small colorectal polyps. This technique demonstrates the potential for improving the diagnostic yield in real-time optical diagnosis of colorectal polyps in the Vietnamese setting.

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BACKGROUND: Gastric cancer (GC) is associated with a significant global health burden and high mortality rates when diagnosed at later stages. The diagnosis often occurs at advanced stages when treatment options are limited and less effective. Early detection strategies are crucial to improving survival rates and outcomes for patients. Blue laser imaging (BLI) is an image-enhanced endoscopy technique that utilizes white light and narrow-band light to detect pathological changes in the mucosal architecture. This study aims at investigating the diagnostic performance of BLI for the detection of GC. METHODS: A comprehensive search was conducted across multiple databases from inception until March 2023. Studies assessing the diagnostic efficacy of BLI for GC detection were included. The sensitivity, specificity and accuracy of BLI were calculated using pooled proportions and 95% confidence intervals (CI) with a random-effects model. Heterogeneity among the included studies was assessed using the I2 statistic. RESULTS: Six studies were included in the pooled analysis. There were 708 patients with 380 GC lesions. Most of the lesions involved the lower two-thirds of the stomach. The pooled performance metrics of BLI for GC detection were as follows: sensitivity of 91.9% (95% CI 83.3-96.3%; I2 = 82.3%), specificity of 93.4% (95% CI 82.0-97.8%; I2 = 87.9%) and accuracy of 95.4% (95% CI 72.6-99.8%; I2 = 73.6%). CONCLUSION: BLI demonstrates high diagnostic efficacy for the detection of GC. BLI can be a valuable tool in clinical practice. However, large-scale, randomized controlled studies are needed to further establish the role of BLI in routine clinical practice for GC detection.

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BACKGROUND & AIMS: Artificial intelligence (AI)-based optical diagnosis systems (CADx) have been developed to allow pathology prediction of colorectal polyps during colonoscopies. However, CADx systems have not yet been validated for autonomous performance. Therefore, we conducted a trial comparing autonomous AI to AI-assisted human (AI-H) optical diagnosis. METHODS: We performed a randomized noninferiority trial of patients undergoing elective colonoscopies at 1 academic institution. Patients were randomized into (1) autonomous AI-based CADx optical diagnosis of diminutive polyps without human input or (2) diagnosis by endoscopists who performed optical diagnosis of diminutive polyps after seeing the real-time CADx diagnosis. The primary outcome was accuracy in optical diagnosis in both arms using pathology as the gold standard. Secondary outcomes included agreement with pathology for surveillance intervals. RESULTS: A total of 467 patients were randomized (238 patients/158 polyps in the autonomous AI group and 229 patients/179 polyps in the AI-H group). Accuracy for optical diagnosis was 77.2% (95% confidence interval [CI], 69.7-84.7) in the autonomous AI group and 72.1% (95% CI, 65.5-78.6) in the AI-H group (P = .86). For high-confidence diagnoses, accuracy for optical diagnosis was 77.2% (95% CI, 69.7-84.7) in the autonomous AI group and 75.5% (95% CI, 67.9-82.0) in the AI-H group. Autonomous AI had statistically significantly higher agreement with pathology-based surveillance intervals compared to AI-H (91.5% [95% CI, 86.9-96.1] vs 82.1% [95% CI, 76.5-87.7]; P = .016). CONCLUSIONS: Autonomous AI-based optical diagnosis exhibits noninferior accuracy to endoscopist-based diagnosis. Both autonomous AI and AI-H exhibited relatively low accuracy for optical diagnosis; however, autonomous AI achieved higher agreement with pathology-based surveillance intervals. (ClinicalTrials.gov, Number NCT05236790).

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OBJECTIVE: To assess whether narrow band imaging (NBI) detects fields of cancerisation around suspicious lesions in the upper aerodigestive tract, which were undetected by white light imaging (WLI). METHODS: In 96 patients with laryngeal and pharyngeal lesions suspicious for malignancy, 206 biopsies were taken during laryngoscopy: 96 biopsies of suspicious lesions detected by both WLI and NBI (WLI+/NBI+), 60 biopsies adjacent mucosa only suspicious with NBI (WLI-/NBI+), and 46 biopsies of NBI and WLI unsuspicious mucosa (WLI-/NBI-) as negative controls. Optical diagnosis according to the Ni-classification was compared with histopathology. RESULTS: Signs of (pre)malignancy were found in 88% of WLI+/NBI+ biopsies, 32% of WLI-/NBI+ biopsies and 0% in WLI-/NBI- (p < .001). In 58% of the WLI-/NBI+ mucosa any form of dysplasia or carcinoma was detected. CONCLUSION: The use of additional NBI led to the detection of (pre)malignancy in 32% of the cases, that would have otherwise remained undetected with WLI alone. This highlights the potential of NBI as a valuable adjunct to WLI in the identification of suspicious lesions in the upper aerodigestive tract.

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Minimally-invasive diagnosis and therapy have gradually become the trend and research hotspot of current medical applications. The integration of intraoperative diagnosis and treatment is a development important direction for real-time detection, minimally-invasive diagnosis and therapy to reduce mortality and improve the quality of life of patients, so called minimally-invasive theranostics (MIT). Light is an important theranostic tool for the treatment of cancerous tissues. Light-mediated minimally-invasive theranostics (LMIT) is a novel evolutionary technology that integrates diagnosis and therapeutics for the less invasive treatment of diseased tissues. Intelligent theranostics would promote precision surgery based on the optical characterization of cancerous tissues. Furthermore, MIT also requires the assistance of smart medical devices or robots. And, optical multimodality lay a solid foundation for intelligent MIT. In this review, we summarize the important state-of-the-arts of optical MIT or LMIT in oncology. Multimodal optical image-guided intelligent treatment is another focus. Intraoperative imaging and real-time analysis-guided optical treatment are also systemically discussed. Finally, the potential challenges and future perspectives of intelligent optical MIT are discussed.

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This review article provides a comprehensive overview of the evolving techniques in image-enhanced endoscopy (IEE) for the characterization of colorectal polyps, and the potential of artificial intelligence (AI) in revolutionizing the diagnostic accuracy of endoscopy. We discuss the historical use of dye-spray and virtual chromoendoscopy for the characterization of colorectal polyps, which are now being replaced with more advanced technologies. Specifically, we focus on the application of AI to create a "virtual biopsy" for the detection and characterization of colorectal polyps, with potential for replacing histopathological diagnosis. The incorporation of AI has the potential to provide an evolutionary learning system that aids in the diagnosis and management of patients with the best possible outcomes. A detailed analysis of the literature supporting AI-assisted diagnostic techniques for the detection and characterization of colorectal polyps, with a particular emphasis on AI's characterization mechanism, is provided. The benefits of AI over traditional IEE techniques, including the reduction in human error in diagnosis, and its potential to provide an accurate diagnosis with similar accuracy to the gold standard are presented. However, the need for large-scale testing of AI in clinical practice and the importance of integrating patient data into the diagnostic process are acknowledged. In conclusion, the constant evolution of IEE technology and the potential for AI to revolutionize the field of endoscopy in the future are presented.

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BACKGROUND: Accurate diagnosis of breast cancer (BC) plays a crucial role in clinical pathology analysis and ensuring precise surgical margins to prevent recurrence. METHODS: Laser-induced fluorescence (LIF) technology offers high sensitivity to tissue biochemistry, making it a potential tool for noninvasive BC identification. In this study, we utilized hyperspectral (HS) imaging data of stimulated BC specimens to detect malignancies based on altered fluorescence characteristics compared to normal tissue. Initially, we employed a HS camera and broadband spectrum light to assess the absorbance of BC samples. Notably, significant absorbance differences were observed in the 440-460 nm wavelength range. Subsequently, we developed a specialized LIF system for BC detection, utilizing a low-power blue laser source at 450 nm wavelength for ten BC samples. RESULTS: Our findings revealed that the fluorescence distribution of breast specimens, which carries molecular-scale structural information, serves as an effective marker for identifying breast tumors. Specifically, the emission at 561 nm exhibited the greatest variation in fluorescence signal intensity for both tumor and normal tissue, serving as an optical predictive biomarker. To enhance BC identification, we propose an advanced image classification technique that combines image segmentation using contour mapping and K-means clustering (K-mc, K = 8) for HS emission image data analysis. CONCLUSIONS: This exploratory work presents a potential avenue for improving "in-vivo" disease characterization using optical technology, specifically our LIF technique combined with the advanced K-mc approach, facilitating early tumor diagnosis in BC.

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The sudden outbreak of the COVID-19 pandemic led to a huge concern globally because of the astounding increase in mortality rates worldwide. The medical imaging computed tomography technique, whole-genome sequencing, and electron microscopy are the methods generally used for the screening and identification of the SARS-CoV-2 virus. The main aim of this review is to emphasize the capabilities of various optical techniques to facilitate not only the timely and effective diagnosis of the virus but also to apply its potential toward therapy in the field of virology. This review paper categorizes the potential optical biosensors into the three main categories, spectroscopic-, nanomaterial-, and interferometry-based approaches, used for detecting various types of viruses, including SARS-CoV-2. Various classifications of spectroscopic techniques such as Raman spectroscopy, near-infrared spectroscopy, and fluorescence spectroscopy are discussed in the first part. The second aspect highlights advances related to nanomaterial-based optical biosensors, while the third part describes various optical interferometric biosensors used for the detection of viruses. The tremendous progress made by lab-on-a-chip technology in conjunction with smartphones for improving the point-of-care and portability features of the optical biosensors is also discussed. Finally, the review discusses the emergence of artificial intelligence and its applications in the field of bio-photonics and medical imaging for the diagnosis of COVID-19. The review concludes by providing insights into the future perspectives of optical techniques in the effective diagnosis of viruses.

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BACKGROUND & AIMS: This study examined the additional value of magnifying chromoendoscopy (MCE) on magnifying narrow-band imaging endoscopy (M-NBI) in the optical diagnosis of colorectal polyps. METHODS: A multicenter prospective study was conducted at 9 facilities in Japan and Germany. Patients with colorectal polyps scheduled for resection were included. Optical diagnosis was performed by M-NBI first, followed by MCE. Both diagnoses were made in real time. MCE was performed on all type 2B lesions classified according to the Japan NBI Expert Team classification and other lesions at the discretion of endoscopists. The diagnostic accuracy and confidence of M-NBI and MCE for colorectal cancer (CRC) with deep invasion (≥T1b) were compared on the basis of histologic findings after resection. RESULTS: In total, 1173 lesions were included between February 2018 and December 2020, with 654 (5 hyperplastic polyp/sessile serrated lesion, 162 low-grade dysplasia, 403 high-grade dysplasia, 97 T1 CRCs, and 32 ≥T2 CRCs) examined using MCE after M-NBI. In the diagnostic accuracy for predicting CRC with deep invasion, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for M-NBI were 63.1%, 94.2%, 61.6%, 94.5%, and 90.2%, respectively, and for MCE they were 77.4%, 93.2%, 62.5%, 96.5%, and 91.1%, respectively. The sensitivity was significantly higher in MCE (P < .001). However, these additional values were limited to lesions with low confidence in M-NBI or the ones diagnosed as ≥T1b CRC by M-NBI. CONCLUSIONS: In this multicenter prospective study, we demonstrated the additional value of MCE on M-NBI. We suggest that additional MCE be recommended for lesions with low confidence or the ones diagnosed as ≥T1b CRC. Trials registry number: UMIN000031129.

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OBJECTIVE: Assessment of the anatomical colorectal segment of polyps during colonoscopy is important for treatment and follow-up strategies, but is largely operator dependent. This feasibility study aimed to assess whether, using images of a magnetic endoscope imaging (MEI) positioning device, a deep learning approach can be useful to objectively divide the colorectum into anatomical segments. METHODS: Models based on the VGG-16 based convolutional neural network architecture were developed to classify the colorectum into anatomical segments. These models were pre-trained on ImageNet data and further trained using prospectively collected data of the POLAR study in which endoscopists were using MEI (3930 still images and 90,151 video frames). Five-fold cross validation with multiple runs was used to evaluate the overall diagnostic accuracies of the models for colorectal segment classification (divided into a 5-class and 2-class colorectal segment division). The colorectal segment assignment by endoscopists was used as the reference standard. RESULTS: For the 5-class colorectal segment division, the best performing model correctly classified the colorectal segment in 753 of the 1196 polyps, corresponding to an overall accuracy of 63%, sensitivity of 63%, specificity of 89% and kappa of 0.47. For the 2-class colorectal segment division, 1112 of the 1196 polyps were correctly classified, corresponding to an accuracy of 93%, sensitivity of 93%, specificity of 90% and kappa of 0.82. CONCLUSION: The diagnostic performance of a deep learning approach for colorectal segment classification based on images of a MEI device is yet suboptimal (clinicaltrials.gov: NCT03822390).

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Rheumatologists in Europe and the USA increasingly rely on fluorescence optical imaging (FOI, Xiralite) for the diagnosis of inflammatory diseases. Those include rheumatoid arthritis, psoriatic arthritis, and osteoarthritis, among others. Indocyanine green (ICG)-based FOI allows visualization of impaired microcirculation caused by inflammation in both hands in one examination. Thousands of patients are now documented and most literature focuses on inflammatory arthritides, which affect synovial joints and their related structures, making it a powerful tool in the diagnostic process of early undifferentiated arthritis and rheumatoid arthritis. However, it has become gradually clear that this technique has the potential to go even further than that. FOI allows visualization of other types of tissues. This means that FOI can also support the diagnostic process of vasculopathies, myositis, collagenoses, and other connective tissue diseases. This work summarizes the most prominent imaging features found in FOI examinations of inflammatory diseases, outlines the underlying anatomical structures, and introduces a nomenclature for the features and, thus, supports the idea that this tool is a useful part of the imaging repertoire in rheumatology clinical practice, particularly where other imaging methods are not easily available.

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BACKGROUND & AIMS: Artificial Intelligence (AI) could support cost-saving strategies for colonoscopy because of its accuracy in the optical diagnosis of colorectal polyps. However, AI must meet predefined criteria to be implemented in clinical settings. METHODS: An approved computer-aided diagnosis (CADx) module for differentiating between adenoma and nonadenoma in unmagnified white-light colonoscopy was used in a consecutive series of colonoscopies. For each polyp, CADx output and subsequent endoscopist diagnosis with advanced imaging were matched against the histology gold standard. The primary outcome was the negative predictive value (NPV) of CADx for adenomatous histology for ≤5-mm rectosigmoid lesions. We also calculated the NPV for AI-assisted endoscopist predictions, and agreement between CADx and histology-based postpolypectomy surveillance intervals according to European and American guidelines. RESULTS: Overall, 544 polyps were removed in 162 patients, of which 295 (54.2%) were ≤5-mm rectosigmoid histologically verified lesions. CADx diagnosis was feasible in 291 of 295 (98.6%), and the NPV for ≤5-mm rectosigmoid lesions was 97.6% (95% CI, 94.1%-99.1%). There were 242 of 295 (82%) lesions that were amenable for a leave-in-situ strategy. Based on CADx output, 212 of 544 (39%) would be amenable to a resect-and-discard strategy, resulting in a 95.6% (95% CI, 90.8%-98.0%) and 95.9% (95% CI, 89.8%-98.4%) agreement between CADx- and histology-based surveillance intervals according to European and American guidelines, respectively. A similar NPV (97.6%; 95% CI, 94.8%-99.1%) for ≤5-mm rectosigmoids was achieved by AI-assisted endoscopists assessing polyps with electronic chromoendoscopy, with a CADx-concordant diagnosis in 97.2% of cases. CONCLUSIONS: In this study, CADx without advanced imaging exceeded the benchmarks required for optical diagnosis of colorectal polyps. CADx could help implement cost-saving strategies in colonoscopy by reducing the burden of polypectomy and/or pathology. CLINICALTRIALS: gov registration number: NCT04884581.

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OBJECTIVES: Raman spectroscopy has been used to discriminate human breast cancer and its different tumor molecular subtypes (luminal A, luminal B, HER2, and triple-negative) from normal tissue in surgical specimens. MATERIALS AND METHODS: Breast cancer and normal tissue samples from 31 patients were obtained by surgical resection and submitted for histopathology. Before anatomopathological processing, the samples had been submitted to Raman spectroscopy (830 nm, 25 mW excitation laser parameters). In total, 424 Raman spectra were obtained. Principal component analysis (PCA) was used in an exploratory analysis to unveil the compositional differences between the tumors and normal tissues. Discriminant models were developed to distinguish the different cancer subtypes by means of partial least squares (PLS) regression. RESULTS: PCA vectors showed spectral features referred to the biochemical constitution of breast tissues, such as lipids, proteins, amino acids, and carotenoids, where lipids were decreased and proteins were increased in breast tumors. Despite the small spectral differences between the different subtypes of tumor and normal tissues, the discriminant model based on PLS was able to discriminate the spectra of the breast tumors from normal tissues with an accuracy of 97.3%, between luminal and nonluminal subtypes with an accuracy of 89.9%, between nontriple-negative and triple-negative with an accuracy of 94.7%, and each molecular subtype with an accuracy of 73.0%. CONCLUSION: PCA could reveal the compositional difference between tumors and normal tissues, and PLS could discriminate the Raman spectra of breast tissues regarding the molecular subtypes of cancer, being a useful tool for cancer diagnosis.

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BACKGROUND: Post-polypectomy surveillance intervals are currently determined based on pathology results. AIM: To evaluate a polyp-based resect and discard model that assigns surveillance intervals based solely on polyp number and size. METHODS: Patients undergoing elective colonoscopies at the Montreal University Medical Center were enrolled prospectively. The polyp-based strategy was used to assign the next surveillance interval using polyp size and number. Surveillance intervals were also assigned using optical diagnosis for small polyps (< 10 mm). The primary outcome was surveillance interval agreement between the polyp-based model, optical diagnosis, and the pathology-based reference standard using the 2020 United States Multi-Society Task Force guidelines. Secondary outcomes included the proportion of reduction in required histopathology evaluations and proportion of immediate post-colonoscopy recommendations provided to patients. RESULTS: Of 944 patients (mean age 62.6 years, 49.3% male, 933 polyps) were enrolled. The surveillance interval agreement for the polyp-based strategy was 98.0% [95% confidence interval (CI): 0.97-0.99] compared with pathology-based assignment. Optical diagnosis-based intervals achieved 95.8% (95%CI: 0.94-0.97) agreement with pathology. When using the polyp-based strategy and optical diagnosis, the need for pathology assessment was reduced by 87.8% and 70.6%, respectively. The polyp-based strategy provided 93.7% of patients with immediate surveillance interval recommendations vs 76.1% for optical diagnosis. CONCLUSION: The polyp-based strategy achieved almost perfect surveillance interval agreement compared with pathology-based assignments, significantly reduced the number of required pathology evaluations, and provided most patients with immediate surveillance interval recommendations.

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Zebrafish is a well-established animal model for developmental and disease studies. Its optical transparency at early developmental stages allows in vivo tissues visualization. Interaction of polarized light with these tissues provides information on their structure and properties. This approach is effective for muscle tissue analysis due to its birefringence. To enable real-time Mueller-matrix characterization of unanesthetized fish, we assembled a microscope for single-shot Mueller-matrix imaging. First, we performed a continuous observation of 48 species within the period of 2 to 96 hpf and measured temporal dependencies of the polarization features in different tissues. These measurements show that hatching was accompanied by a sharp change in the angle and degree of linearly polarized light after interaction with muscles. Second, we analyzed nine species with skeletal disorders and demonstrated that the spatial distribution of light depolarization features clearly indicated them. Obtained results demonstrated that real-time Mueller-matrix imaging is a powerful tool for label-free monitoring zebrafish embryos.

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The aim of this prospective clinical study was to establish and verify an adaptation for axial length (AL) measurement in silicone oil (SO)-filled pseudophakic eyes with a Scheimpflug and partial coherence interferometry (PCI)-based biometer. The AL was measured with a Pentacam AXL (OCULUS Optikgeräte GmbH, Wetzler, Germany) and IOLMaster 700 (Carl Zeiss Meditec, Jena, Germany). The coefficients of variation (CoV) and the mean systematic difference (95% confidence interval (CI)) between the devices were calculated. After implementing a setting for measuring AL in tamponaded eyes with a Pentacam based on data of 29 eyes, another 12 eyes were examined for verification. The mean AL obtained with the Pentacam was 25.53 ± 1.94 mm (range: 21.70 to 30.76 mm), and with IOLMaster, 24.73 ± 1.97 mm (ranged 20.84 to 29.92 mm), resulting in a mean offset of 0.80 ± 0.08 mm (95% CI: 0.77, 0.83 mm), p < 0.001. The AL values of both devices showed a strong linear correlation (r = 0.999). Verification data confirmed good agreement, with a statistically and clinically non-significant mean difference of 0.02 ± 0.04 (95% CI: -0.01, 0.05) mm, p = 0.134. We implemented a specific adaptation for obtaining reliable AL values in SO-filled eyes with the Pentacam AXL.