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Triple-negative breast cancer (TNBC) is a subtype of breast cancer with a poor prognosis and limited treatment options. This study evaluates the prognostic value of stromal markers in TNBC, focusing on the tumor-stroma ratio (TSR) and overall stroma ratio (OSR) in whole slide images (WSI), as well as the expression of type-I collagen, type-III collagen, and fibrillin-1 on tissue microarrays (TMAs), using both visual assessment and digital image analysis (DIA). A total of 101 female TNBC patients, primarily treated with surgery between 2005 and 2016, were included. We found that high visual OSR correlates with worse overall survival (OS), advanced pN categories, lower stromal tumor-infiltrating lymphocyte count (sTIL), lower mitotic index, and patient age (p < 0.05). TSR showed significant connections to the pN category and mitotic index (p < 0.01). High expression levels of type-I collagen (>45%), type-III collagen (>30%), and fibrillin-1 (>20%) were linked to significantly worse OS (p = 0.004, p = 0.013, and p = 0.005, respectively) and progression-free survival (PFS) (p = 0.028, p = 0.025, and p = 0.002, respectively), validated at the mRNA level. Our results highlight the importance of stromal characteristics in promoting tumor progression and metastasis and that targeting extracellular matrix (ECM) components may offer novel therapeutic strategies. Furthermore, DIA can be more accurate and objective in evaluating TSR, OSR, and immunodetected stromal markers than traditional visual examination.

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Synthesis of novel agro-industrial wastes/sodium alginate/bovine gelatin-based polysaccharide hydrogel beads, micromeritic/morphometric characteristics of the prepared formulations, greenhouse trials using controlled-release microencapsulated fertilizers, and acute fish toxicity testing were conducted simultaneously for the first time within the scope of an integrated research. In the present analysis, for the first time, 16 different morphometric features, and 32 disinct plant growth traits of the prepared composite beads were explored in detail within the framework of a comprehensive digital image analysis. The hydrogel beads composed of 19 different agro-industrial wastes/materials were successfully synthesized using the ionotropic external gelation technique and CaCl2 as cross-linker. According to micromeritic characteristics, the ionotropically cross-linked beads exhibited 77.86 ± 3.55 % yield percentage and 2.679 ± 0.397 mm average particle size. The dried microbeads showed a good swelling ratio (270.02 ± 80.53 %) and had acceptable flow properties according to Hausner's ratio (1.136 ± 0.028), Carr's index (11.94 ± 2.17 %), and angle of repose (25.03° ± 5.33°) values. The settling process of the prepared microbeads was observed in the intermediate flow regime, as indicated by the average particle Reynolds numbers (169.17 ± 82.81). Experimental findings and non-parametric statistical tests reveal that dried fertilizer matrices demonstrated noteworthy performance on the cultivation of red hot chili pepper plant (Capsicum annuum var. fasciculatum) according to the results of greenhouse trials. Surface morphologies of the best-performing fertilizer matrices were also characterized by Scanning Electron Microscopy. Moreover, the static fish bioassay experiment confirmed that no abnormalities and acute toxic reactions occurred in shortfin molly fish (Poecilia sphenops) fed with dried leaves of red hot chili pepper plants grown with formulated fertilizers. This study showcased a pioneering investigation into the synthesis of microcapsules using synthesized hydrogel beads along with digital image processing for bio-waste management and sustainable agro-application.

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This systematic review aims to address the research gap in the performance of computational algorithms for the digital image analysis of HER2 images in clinical settings. While numerous studies have explored various aspects of these algorithms, there is a lack of comprehensive evaluation regarding their effectiveness in real-world clinical applications. We conducted a search of the Web of Science and PubMed databases for studies published from 31 December 2013 to 30 June 2024, focusing on performance effectiveness and components such as dataset size, diversity and source, ground truth, annotation, and validation methods. The study was registered with PROSPERO (CRD42024525404). Key questions guiding this review include the following: How effective are current computational algorithms at detecting HER2 status in digital images? What are the common validation methods and dataset characteristics used in these studies? Is there standardization of algorithm evaluations of clinical applications that can improve the clinical utility and reliability of computational tools for HER2 detection in digital image analysis? We identified 6833 publications, with 25 meeting the inclusion criteria. The accuracy rate with clinical datasets varied from 84.19% to 97.9%. The highest accuracy was achieved on the publicly available Warwick dataset at 98.8% in synthesized datasets. Only 12% of studies used separate datasets for external validation; 64% of studies used a combination of accuracy, precision, recall, and F1 as a set of performance measures. Despite the high accuracy rates reported in these studies, there is a notable absence of direct evidence supporting their clinical application. To facilitate the integration of these technologies into clinical practice, there is an urgent need to address real-world challenges and overreliance on internal validation. Standardizing study designs on real clinical datasets can enhance the reliability and clinical applicability of computational algorithms in improving the detection of HER2 cancer.

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The primary objective of this study was to demonstrate the potential of digital image analysis as a tool to identify microplastic (MP) particles in surface waters and to facilitate their characterisation in terms of 2D and 3D morphology. Digital image analysis preceded by microscopic analysis was used for an exhaustive quantitative and qualitative evaluation of MPs isolated from the Vistula River. Using image processing procedures, 2D and 3D shape descriptors were determined. Principal Component Analysis was used to interpret the relationships between the parameters studied, characterising MP particle geometry, type and colour. This multivariate analysis of the data allowed three or four main factors to be extracted, explaining approximately 90% of the variation in the data characterising MP morphology. It was found that the first principal component for granules, flakes and films was largely represented by strongly correlated with 2D shape descriptors (area, perimeter, equivalent area diameter) and 3D shape descriptors (Corey Shape Factor, Compactness, Dimensionality). Considering the scraps, principal component PC1 was represented by only five of the above descriptors, and the Compactness variable had the largest contribution to principal component PC2. In addition, for granules, flakes and films, a relationship between 2D shape and the colour of their particles could be observed. For the most numerous MP group identified of multicoloured scraps, no such association was found. The results of our study can be used for further multivariate analysis regarding the presence of microplastic floating on the river surface, with a particular focus on particles of secondary origin. This is of key importance for optimising future efforts in conducting small-scale and multidimensional monitoring of and reducing plastics in the aquatic environment.

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AIM: Manual detection and scoring of Ki67 hotspots is difficult and prone to variability, limiting its clinical utility. Automated hotspot detection and scoring by digital image analysis (DIA) could improve the assessment of the Ki67 hotspot proliferation index (PI). This study compared the clinical performance of Ki67 hotspot detection and scoring DIA algorithms based on virtual dual staining (VDS) and deep learning (DL) with manual Ki67 hotspot PI assessment. METHODS: Tissue sections of 135 consecutive invasive breast carcinomas were immunohistochemically stained for Ki67. Two DIA algorithms, based on VDS and DL, automatically determined the Ki67 hotspot PI. For manual assessment; two independent observers detected hotspots and calculated scores using a validated scoring protocol. RESULTS: Automated hotspot detection and assessment by VDS and DL could be performed in 73% and 100% of the cases, respectively. Automated hotspot detection by VDS and DL led to higher Ki67 hotspot PIs (mean 39.6% and 38.3%, respectively) compared to manual consensus Ki67 PIs (mean 28.8%). Comparing manual consensus Ki67 PIs with VDS Ki67 PIs revealed substantial correlation (r = 0.90), while manual consensus versus DL Ki67 PIs demonstrated high correlation (r = 0.95). CONCLUSION: Automated Ki67 hotspot detection and analysis correlated strongly with manual Ki67 assessment and provided higher PIs compared to manual assessment. The DL-based algorithm outperformed the VDS-based algorithm in clinical applicability, because it did not depend on virtual alignment of slides and correlated stronger with manual scores. Use of a DL-based algorithm may allow clearer Ki67 PI cutoff values, thereby improving the clinical usability of Ki67.

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AIMS: In this proof-of-concept study, we propose a new method for automated digital quantification of PRAME (PReferentially expressed Antigen of MElanoma) as a diagnostic aid to distinguish between benign and malignant melanocytic lesions. The proposed method utilizes immunohistochemical virtual double nuclear staining for PRAME and SOX10 to precisely identify the melanocytic cells of interest, which is combined with digital image analyse to quantify a PRAME-index. METHODS: Our study included 10 compound nevi, 3 halo nevi, and 10 melanomas. Tissue slides were stained with PRAME, scanned, the cover glass removed, stained with SOX10, scanned again, and finally analysed digitally. The digitally quantified PRAME-index was compared with a manual qualitative assessment by a dermatopathologist using the standard PRAME-scoring system. RESULTS: The digitally quantified PRAME-index showed a sensitivity of 70 % and a specificity of 100 % for separating melanomas from benign lesions. The manual qualitative PRAME-score showed a sensitivity of 60 % and a specificity of 100 %. Comparing the two methods using ROC-analyses, our digital quantitative method (AUC: 0.931, 95 % CI: 0.834;1.00, SD: 0.050) remains on par with the manual qualitative method (AUC: 0.877, 95 % CI: 0.725;1.00, SD: 0.078). CONCLUSION: We found our novel digital quantitative method was at least as precise at classifying lesions as benign or malignant as the current manual qualitative assessment. Our method has the advantages of being operator-independent, objective, and replicable. Furthermore, our method can easily be implemented in an already digitalized pathology department. Given the small cohort size, more studies are to be done to validate our findings.

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Colorants, especially synthetic colorants, play a crucial role in enhancing the aesthetic qualities of food owing to their cost-effectiveness and stability against environmental factors. Ensuring the safe and regulated use of colorants is essential for maintaining consumer trust in food safety. Various preparation and analytical technologies, which are continuously undergoing improvement, are currently used to quantify of synthetic colorants in food products. This paper reviews recent developments in analytical techniques for synthetic food colorants, detection and compares the operational principles, advantages, and disadvantages of each technology. Additionally, it also explores advancements in these technologies, discussing several invaluable tools of analysis, such as high-performance liquid chromatography, liquid chromatography-tandem mass spectrometry, electrochemical sensors, digital image analysis, near-infrared spectroscopy, and surface-enhanced Raman spectroscopy. This comprehensive overview aims to provide valuable insights into current progress and research in the field of food colorant analysis.

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This study is the first to compare the determination of the Ki-67 index in pituitary neuroendocrine tumors (PitNET)/pituitary adenomas by pathologists with a computerized method (Cognition MasterSuite from VMScope, Berlin, Germany). PitNET/pituitary adenomas often show a low proliferation index. Observer variability is high, especially when estimating in this low percentage range. A more reliable determination would be possible using the four-eyes principle, but this cannot be realized continuously; thus, digital image analysis is a promising solution. In the study, there was clear agreement between the Ki-67 estimate by two experienced pathologists and the determination with the aid of digital image analysis. The digital image analysis system is excellent for determining the proliferation rate of PitNET/pituitary adenomas and can therefore be used to determine the "third" and "fourth eye".

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We aimed to develop deep learning (DL) models to detect protein expression in immunohistochemically (IHC) stained tissue-sections, and to compare their accuracy and performance with manually scored clinically relevant proteins in common cancer types. Five cancer patient cohorts (colon, two prostate, breast, and endometrial) were included. We developed separate DL models for scoring IHC-stained tissue-sections with nuclear, cytoplasmic, and membranous staining patterns. For training, we used images with annotations of cells with positive and negative staining from the colon cohort stained for Ki-67 and PMS2 (nuclear model), the prostate cohort 1 stained for PTEN (cytoplasmic model) and ß-catenin (membranous model). The nuclear DL model was validated for MSH6 in the colon, MSH6 and PMS2 in the endometrium, Ki-67 and CyclinB1 in prostate, and oestrogen and progesterone receptors in the breast cancer cohorts. The cytoplasmic DL model was validated for PTEN and Mapre2, and the membranous DL model for CD44 and Flotillin1, all in prostate cohorts. When comparing the results of manual and DL scores in the validation sets, using manual scores as the ground truth, we observed an average correct classification rate of 91.5 % (76.9-98.5 %) for the nuclear model, 85.6 % (73.3-96.6 %) for the cytoplasmic model, and 78.4 % (75.5-84.3 %) for the membranous model. In survival analyses, manual and DL scores showed similar prognostic impact, with similar hazard ratios and p-values for all DL models. Our findings demonstrate that DL models offer a promising alternative to manual IHC scoring, providing efficiency and reproducibility across various data sources and markers.

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Background and Objective: An accurate Ki-67 labeling index assessment is critical for managing a few tumors, like breast carcinomas and neuroendocrine tumors. We aimed to determine the degree of agreement between digital image analysis (DIA) and eye-rolling assessment (EE) and DIA and manual count (MC) for Ki-67 LI scoring. Methods: A total of 120 cases (both tru-cut biopsies and resected specimens) were selected during the study period from the institutional database, wherein the Ki-67 labeling index was performed. The selected cases were divided into two groups, i.e., breast neoplasms and other neoplasms. The correlation between DIA and EE and DIA and MC for Ki-67 LI scoring was calculated in both groups. Results: A total of 113 cases were analyzed for Ki-67 LI by three different methods (EE, MC, and DIA); 7 cases were rejected due to poor image quality. Ki-67 LI scoring by DIA and EE was highly correlated in both study groups with a Spearman's rank correlation coefficient of 0.809 (P=0.01) and 0.904 (P=0.01), respectively. Correlation between DIA and MC methods was also found to be almost perfect in both study groups with a Spearman's rank correlation coefficient of 0.974 (P=0.01) and 0.955 (P=0.01), respectively. Conclusion: ImmunoRatio is a free web-based digital image analysis application that can be used for Ki-67 LI assessment with considerable reliability and reproducibility. Yet, it carries a few limitations and demands a careful approach and final confirmation by an expert.

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This study addressed the need for a more accessible and efficient method of analyzing phenolic content in vegetable oils. The research aimed to develop a method that could be widely adopted by both researchers and industry professionals, ultimately revolutionizing the way phenolic content in vegetable oils is analyzed. This study developed a method of determining the total phenolic content (TPC) in vegetable oils using smartphone image analysis in the RGB color model. The method employed a gallic acid calibration solution and demonstrated exceptional determination coefficients for the RGB colors. The R-red color was selected as the basis for the analyses, and the method was statistically equivalent to standard UV/Vis spectrophotometry. The highest TPC was determined in hemp and olive oils, while the lowest was found in rice bran, grapeseed, and macadamia nut oils. This study concluded that smartphone image analysis, mainly using the R component of the RGB color model, was a superior alternative to traditional spectrophotometric methods for determining the TPC in vegetable oils. This innovative approach could revolutionize phenolic content analysis by providing researchers and industry professionals with a cost-effective, safe, and efficient tool. The estimated limit of detection (LOD) of 1.254 mg L-1 and limit of quantification (LOQ) of 3.801 mg L-1 further confirmed the reliability and comparability of the method. With these findings, it was expected that the method would be widely adopted in the future.

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A quantitative method for acid-base titrations in paper-based devices (PADs) is described to analyze acetic acid in vinegar samples. In this work, two different types of PADs were developed: a device for individual spot testing and a microfluidic device. Digital colorimetry was used as the detection method, and the images were acquired using a smartphone and a homemade box with LED lights for controlled image acquisition. Titration curves were built with just eight points, using the R channel based on the gradual color transition from red to blue of litmus, a natural indicator. The endpoint was accurately determined by second derivative calculations. Both systems were applied to fifteen vinegar samples of different types, and good concentration results were obtained in comparison to the reference method. The proposed methodology is simple, fast, environmentally friendly, and surpasses the need for calibration curve construction. Moreover, the subjective endpoint identification is eliminated, and the method was automated to provide a high throughput workflow, suitable for quality control processes and real-time measurements.

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PURPOSE: Quantification of Ki67 in breast cancer is a well-established prognostic and predictive marker, but inter-laboratory variability has hampered its clinical usefulness. This study compares the prognostic value and reproducibility of Ki67 scoring using four automated, digital image analysis (DIA) methods and two manual methods. METHODS: The study cohort consisted of 367 patients diagnosed between 1990 and 2004, with hormone receptor positive, HER2 negative, lymph node negative breast cancer. Manual scoring of Ki67 was performed using predefined criteria. DIA Ki67 scoring was performed using QuPath and Visiopharm® platforms. Reproducibility was assessed by the intraclass correlation coefficient (ICC). ROC curve survival analysis identified optimal cutoff values in addition to recommendations by the International Ki67 Working Group and Norwegian Guidelines. Kaplan-Meier curves, log-rank test and Cox regression analysis assessed the association between Ki67 scoring and distant metastasis (DM) free survival. RESULTS: The manual hotspot and global scoring methods showed good agreement when compared to their counterpart DIA methods (ICC > 0.780), and good to excellent agreement between different DIA hotspot scoring platforms (ICC 0.781-0.906). Different Ki67 cutoffs demonstrate significant DM-free survival (p < 0.05). DIA scoring had greater prognostic value for DM-free survival using a 14% cutoff (HR 3.054-4.077) than manual scoring (HR 2.012-2.056). The use of a single cutoff for all scoring methods affected the distribution of prediction outcomes (e.g. false positives and negatives). CONCLUSION: This study demonstrates that DIA scoring of Ki67 is superior to manual methods, but further study is required to standardize automated, DIA scoring and definition of a clinical cut-off.

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The aim of this research was to optimize the production process of fermented gluten-free quinoa bread. To this end, the effect of different hydrocolloids on the technological, fermentative, and nutritional properties of quinoa-based gluten-free doughs and breads was evaluated. For this purpose, 3% of four different hydrocolloids (sodium alginate, k-carrageenan, xanthan gum, and hydroxypropyl methylcellulose (HPMC)) were used in gluten-free doughs composed of 50% quinoa flour, 20% rice flour, and 30% potato starch. The rheological and fermentative properties of the doughs were evaluated, as well as the chemical composition, specific volume, crust and crumb color, and alveolar structure profile of gluten-free breads. The results highlighted the differences in dough rheology during mixing and fermentation of the doughs. In particular, HPMC showed a good gas retention (93%) during the fermentation of quinoa dough by registering the highest maximum dough development height (Hm). The gluten-free quinoa breads obtained were characterized by significantly different quality parameters (p < 0.05). The use of 3% HPMC resulted in breads with the lowest baking loss, the highest volume, and the most open crumb structure.

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Non-muscle-invasive papillary urothelial carcinoma (NMIPUC) of the urinary bladder is the most common type of bladder cancer. Intravesical Bacille Calmette-Guerin (BCG) immunotherapy is applied in patients with a high risk of recurrence and progression of NMIPUC to muscle-invasive disease. However, the tumor relapses in about 30% of patients despite the treatment, raising the need for better risk stratification. We explored the potential of spatial distributions of immune cell subtypes (CD20, CD11c, CD163, ICOS, and CD8) within the tumor microenvironment to predict NMIPUC recurrence following BCG immunotherapy. Based on analyses of digital whole-slide images, we assessed the densities of the immune cells in the epithelial-stromal interface zone compartments and their distribution, represented by an epithelial-stromal interface density ratio (IDR). While the densities of any cell type did not predict recurrence, a higher IDR of CD11c (HR: 0.0012, p-value = 0.0002), CD8 (HR: 0.0379, p-value = 0.005), and ICOS (HR: 0.0768, p-value = 0.0388) was associated with longer recurrence-free survival (RFS) based on the univariate Cox regression. The history of positive repeated TUR (re-TUR) (HR: 4.93, p-value = 0.0001) and T1 tumor stage (HR: 2.04, p-value = 0.0159) were associated with shorter RFS, while G3 tumor grade according to the 1973 WHO classification showed borderline significance (HR: 1.83, p-value = 0.0522). In a multivariate analysis, the two models with a concordance index exceeding 0.7 included the CD11c IDR in combination with either a history of positive re-TUR or tumor stage. We conclude that the CD11c IDR is the most informative predictor of NMIPUC recurrence after BCG immunotherapy. Our findings highlight the importance of assessment of the spatial distribution of immune cells in the tumor microenvironment.

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In this work, a multiplexed colorimetric strategy was initiated for simultaneous and fast visualization of dyes using low-cost and easy-to-prepare indicator papers as sorbents. Response surface methodology (RSM) was employed to model statistically and optimize the process variables for dyes extraction and colorimetric assays. Multiplexed colorimetry was realized by virtue of synchronous color alignments from different dimensions of multiple dyes co-stained colorimetric cards under RSM-optimized conditions, and smartphone-based image analysis was subsequently performed from different modes to double-check the credibility of colorimetric assays. As concept-to-proof trials, simultaneous visualization of dyes in both beverages and simulated dye effluents was experimentally proved with results highly matched to HPLC or spiked amounts at RSM-predicted staining time as short as 50 s ∼3 min, giving LODs as low as 0.97 ± 0.22/0.18 ± 0.08 µg/mL (tartrazine/brilliant blue) for multiplexed colorimetry, which much lower than those obtained by single colorimetry. Since this is the first case to propose such a RSM-guided multiplexed colorimetric concept, it will provide a reference for engineering of other all-in-one devices which can realize synchronous visualization applications within limited experimental steps.

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Given the widespread use of whole slide imaging (WSI) for primary pathological diagnosis, we evaluated its utility in assessing histological grade and biomarker expression (ER, PR, HER2, and Ki67) compared to conventional light microscopy (CLM). In addition, we explored the utility of digital image analysis (DIA) for assessing biomarker expression. Three breast pathologists assessed the Nottingham combined histological grade, its components, and biomarker expression through the immunohistochemistry of core needle biopsy samples obtained from 101 patients with breast cancer using CLM, WSI, and DIA. There was no significant difference in variance between the WSI and CLM agreement rates for the Nottingham grade and its components and biomarker expression. Nuclear pleomorphism emerged as the most variable histologic component in intra- and inter-observer agreement (kappa ≤ 0.577 and kappa ≤ 0.394, respectively). The assessment of biomarker expression using DIA achieved an enhanced kappa compared to the inter-observer agreement. Compared to each observer's assessment, DIA exhibited an improved kappa coefficient for the expression of most biomarkers with CLM and WSI. Using WSI to assess prognostic and predictive factors, including histological grade and biomarker expression in breast cancer, is acceptable. Furthermore, incorporating DIA to assess biomarker expression shows promise for substantially enhancing scoring reproducibility.

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Managing patients with kidney allografts largely depends on biopsy diagnosis which is based on semiquantitative assessments of rejection features and extent of acute and chronic changes within the renal parenchyma. Current methods lack reproducibility while digital image data-driven computational models enable comprehensive and quantitative assays. In this study we aimed to develop a computational method for automated assessment of histopathology transformations within the tubulointerstitial compartment of the renal cortex. Whole slide images of modified Picrosirius red-stained biopsy slides were used for the training (n = 852) and both internal (n = 172) and external (n = 94) tests datasets. The pipeline utilizes deep learning segmentations of renal tubules, interstitium, and peritubular capillaries from which morphometry features were extracted. Seven indicators were selected for exploring the intrinsic spatial interactions within the tubulointerstitial compartment. A principal component analysis revealed two independent factors which can be interpreted as representing chronic and acute tubulointerstitial injury. A K-means clustering classified biopsies according to potential phenotypes of combined acute and chronic transformations of various degrees. We conclude that multivariate analyses of tubulointerstitial morphometry transformations enable extraction of and quantification of acute and chronic components of injury. The method is developed for renal allograft biopsies; however, the principle can be applied more broadly for kidney pathology assessment.

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BACKGROUND: Developing disposable paper-based devices has positively impacted analytical science, particularly in developing countries. Some benefits of those devices include their versatility, affordability, environmentally friendly, and the possibility of being integrated with portable electrochemical or colorimetric detectors. Paper-based analytical devices (PADs) comprising circular zones and microfluidic networks have been successfully employed in the analytical chemistry reign. However, the combination of the stencil-printing method and alternative binder has not been satisfactorily explored for fabricating colorimetric paper devices. RESULTS: We developed PADs exploring the stencil printing approach and glass varnish as the hydrophobic chemical agent. As a proof-of-concept, the colorimetric assay of salivary α-amylase (sAA) was performed in saliva samples. Through the scanning electron microscopy measurements, it was possible to indicate satisfactory definitions between native fibers and barrier, and that the measured values for the channel width revealed suitable fidelity (R2 = 0.99) with the nominal widths (ranging from 400 to 5000 µm). The proposed hydrophobic barrier exhibited excellent chemical resistance. The analytical applicability for detecting sAA revealed linear behavior in the range from 2 to 12 U mL-1 (R2 = 0.99), limit of detection of 0.75 U mL-1, reproducibility (RSD ≤2.4%), recovery experiments ranged from 89 to 108% and AGREE response (0.86). In addition, the colorimetric analysis of sAA in four different saliva samples demonstrated levels ranging from 202 to 2080 U mL-1, which enabled monitoring the absence and presence of periodontitis. SIGNIFICANCE: This report has presented the first use of a self-adhesive mask and glass varnish for creating circular zones and microfluidic architectures on paper without using thermic or UV curing treatments. Also, the proposed analytical methodology for detecting sAA exhibited suitable ecological impact considering the AGREE tool. We believe the proposed fabrication of paper devices emerges as a novel, simple, high-fidelity microfluidic channel and portable analytical approach for colorimetric sensing.

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PURPOSE: The recent findings from the DESTINY-Breast04 trial highlighted the clinical importance of distinguishing between HER2 immunohistochemistry (IHC) scores 0 and 1 + in metastatic breast cancer (BC). However, pathologist interpretation of HER2 IHC scoring is subjective, and standardized methodology is needed. We evaluated the consistency of HER2 IHC scoring among pathologists and the accuracy of digital image analysis (DIA) in interpreting HER2 IHC staining in cases of HER2-low BC. METHODS: Fifty whole-slide biopsies of BC with HER2 IHC staining were evaluated, comprising 25 cases originally reported as IHC score 0 and 25 as 1 +. These slides were digitally scanned. Six pathologists with breast expertise independently reviewed and scored the scanned images, and DIA was applied. Agreement among pathologists and concordance between pathologist scores and DIA results were statistically analyzed using Kendall coefficient of concordance (W) tests. RESULTS: Substantial agreement among at least five of the six pathologists was found for 18 of the score 0 cases (72%) and 15 of the score 1 + cases (60%), indicating excellent interobserver agreement (W = 0.828). DIA scores were highly concordant with pathologist scores in 96% of cases (47/49), indicating excellent concordance (W = 0.959). CONCLUSION: Although breast subspecialty pathologists were relatively consistent in evaluating BC with HER2 IHC scores of 0 and 1 +, DIA may be a reliable supplementary tool to enhance the standardization and quantification of HER2 IHC assessment, especially in challenging cases where results may be ambiguous (i.e., scores 0-1 +). These findings hold promise for improving the accuracy and consistency of HER2 testing.

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