Artificial intelligence-based real-time histopathology of gastric cancer using confocal laser endomicroscopy.

There has been a persistent demand for an innovative modality in real-time histologic imaging, distinct from the conventional frozen section technique. We developed an artificial intelligence-driven real-time evaluation model for gastric cancer tissue using confocal laser endomicroscopic system. The remarkable performance of the model suggests its potential utilization as a standalone modality for instantaneous histologic assessment and as a complementary tool for pathologists' interpretation.

Clinical Validation of Artificial Intelligence-Powered PD-L1 Tumor Proportion Score Interpretation for Immune Checkpoint Inhibitor Response Prediction in Non-Small Cell Lung Cancer.

PURPOSE: Evaluation of PD-L1 tumor proportion score (TPS) by pathologists has been very impactful but is limited by factors such as intraobserver/interobserver bias and intratumor heterogeneity. We developed an artificial intelligence (AI)-powered analyzer to assess TPS for the prediction of immune checkpoint inhibitor (ICI) response in advanced non-small cell lung cancer (NSCLC). MATERIALS AND METHODS: The AI analyzer was trained with 393,565 tumor cells annotated by board-certified pathologists for PD-L1 expression in 802 whole-slide images (WSIs) stained by 22C3 pharmDx immunohistochemistry. The clinical performance of the analyzer was validated in an external cohort of 430 WSIs from patients with NSCLC. Three pathologists performed annotations of this external cohort, and their consensus TPS was compared with AI-based TPS. RESULTS: In comparing PD-L1 TPS assessed by AI analyzer and by pathologists, a significant positive correlation was observed (Spearman coefficient = 0.925; P < .001). The concordance of TPS between AI analyzer and pathologists according to TPS ≥50%, 1%-49%, and <1% was 85.7%, 89.3%, and 52.4%, respectively. In median progression-free survival (PFS), AI-based TPS predicted prognosis in the TPS 1%-49% or TPS <1% group better than the pathologist's reading, with the TPS ≥50% group as a reference (hazard ratio [HR], 1.49 [95% CI, 1.19 to 1.86] v HR, 1.36 [95% CI, 1.08 to 1.71] for TPS 1%-49% group, and HR, 2.38 [95% CI, 1.69 to 3.35] v HR, 1.62 [95% CI, 1.23 to 2.13] for TPS <1% group). CONCLUSION: PD-L1 TPS assessed by AI analyzer correlates with that of pathologists, with clinical performance also being comparable when referenced to PFS. The AI model can accurately predict tumor response and PFS of ICI in advanced NSCLC via assessment of PD-L1 TPS.

Inflamed immune phenotype predicts favorable clinical outcomes of immune checkpoint inhibitor therapy across multiple cancer types.

BACKGROUND: The inflamed immune phenotype (IIP), defined by enrichment of tumor-infiltrating lymphocytes (TILs) within intratumoral areas, is a promising tumor-agnostic biomarker of response to immune checkpoint inhibitor (ICI) therapy. However, it is challenging to define the IIP in an objective and reproducible manner during manual histopathologic examination. Here, we investigate artificial intelligence (AI)-based immune phenotypes capable of predicting ICI clinical outcomes in multiple solid tumor types. METHODS: Lunit SCOPE IO is a deep learning model which determines the immune phenotype of the tumor microenvironment based on TIL analysis. We evaluated the correlation between the IIP and ICI treatment outcomes in terms of objective response rates (ORR), progression-free survival (PFS), and overall survival (OS) in a cohort of 1,806 ICI-treated patients representing over 27 solid tumor types retrospectively collected from multiple institutions. RESULTS: We observed an overall IIP prevalence of 35.2% and significantly more favorable ORRs (26.3% vs 15.8%), PFS (median 5.3 vs 3.1 months, HR 0.68, 95% CI 0.61 to 0.76), and OS (median 25.3 vs 13.6 months, HR 0.66, 95% CI 0.57 to 0.75) after ICI therapy in IIP compared with non-IIP patients, respectively (p<0.001 for all comparisons). On subgroup analysis, the IIP was generally prognostic of favorable PFS across major patient subgroups, with the exception of the microsatellite unstable/mismatch repair deficient subgroup. CONCLUSION: The AI-based IIP may represent a practical, affordable, clinically actionable, and tumor-agnostic biomarker prognostic of ICI therapy response across diverse tumor types.

Real-time Histological Evaluation of Gastric Cancer Tissue by Using a Confocal Laser Endomicroscopic System.

BACKGROUND/AIM: The need for instant histological evaluation of fresh tissue, especially in cancer treatment, remains paramount. The conventional frozen section technique has inherent limitations, prompting the exploration of alternative methods. A recently developed confocal laser endomicroscopic system provides real-time imaging of the tissue without the need for glass slide preparation. Herein, we evaluated its applicability in the histologic evaluation of gastric cancer tissues. MATERIALS AND METHODS: A confocal laser endomicroscopic system (CLES) with a Lissajous pattern laser scanning, was developed. Fourteen fresh gastric cancer tissues and the same number of normal gastric tissues were obtained from advanced gastric cancer patients. Fluorescein sodium was used for staining. Five pathologists interpreted 100 endomicroscopic images and decided their histologic location and the presence of cancer. Following the review of matched hematoxylin and eosin (H&E) slides, their performance was evaluated with another 100 images. RESULTS: CLES images mirrored gastric tissue histology. Pathologists were able to detect the histologic location of the images with 65.7% accuracy and differentiate cancer tissue from normal with 74.7% accuracy. The sensitivity and specificity of cancer detection were 71.9% and 76.1%. Following the review of matched H&E images, the accuracy of identifying the histologic location was increased to 92.8% (p<0.0001), and that of detecting cancer tissue was also increased to 90.9% (p<0.001). The sensitivity and specificity of cancer detection were enhanced to 89.1% and 93.2% (p<0.0001). CONCLUSION: High-quality histological images were immediately acquired by the CLES. The operator training enabled the accurate detection of cancer and histologic location raising its potential applicability as a real-time tissue imaging modality.

Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine.

Breast cancer is a significant cause of cancer-related mortality in women worldwide. Early and precise diagnosis is crucial, and clinical outcomes can be markedly enhanced. The rise of artificial intelligence (AI) has ushered in a new era, notably in image analysis, paving the way for major advancements in breast cancer diagnosis and individualized treatment regimens. In the diagnostic workflow for patients with breast cancer, the role of AI encompasses screening, diagnosis, staging, biomarker evaluation, prognostication, and therapeutic response prediction. Although its potential is immense, its complete integration into clinical practice is challenging. Particularly, these challenges include the imperatives for extensive clinical validation, model generalizability, navigating the "black-box" conundrum, and pragmatic considerations of embedding AI into everyday clinical environments. In this review, we comprehensively explored the diverse applications of AI in breast cancer care, underlining its transformative promise and existing impediments. In radiology, we specifically address AI in mammography, tomosynthesis, risk prediction models, and supplementary imaging methods, including magnetic resonance imaging and ultrasound. In pathology, our focus is on AI applications for pathologic diagnosis, evaluation of biomarkers, and predictions related to genetic alterations, treatment response, and prognosis in the context of breast cancer diagnosis and treatment. Our discussion underscores the transformative potential of AI in breast cancer management and emphasizes the importance of focused research to realize the full spectrum of benefits of AI in patient care.

Pulsed-dye laser as an effective treatment for recalcitrant granulomatous rosacea and a potential regulator of CXCL9 expression.

Granulomatous rosacea (GR) is a rare and distinct variant of rosacea. We report three cases of recalcitrant GR successfully treated with pulsed-dye laser (PDL) and provide experimental evidence supporting its potential as a treatment option. PDL treatment demonstrated remarkable efficacy in the three clinical cases, despite their resistance to conventional therapies. Chemokine ligand 9 (CXCL9), a key chemokine involved in inflammation and granuloma formation, was found to be increased in skin sections from all three patients. In vitro experiments using human monocytes and dermal fibroblasts demonstrated that PDL treatment significantly reduced CXCL9 expression in fibroblasts. These findings suggest that PDL may modulate CXCL9 secretion in fibroblasts, potentially limiting the recruitment of immune cells to the lesion. Although further research is needed to fully understand the precise mechanisms underlying the role of CXCL9 in GR, PDL may be a promising therapeutic approach for refractory GR.

Deep learning model improves tumor-infiltrating lymphocyte evaluation and therapeutic response prediction in breast cancer.

Tumor-infiltrating lymphocytes (TILs) have been recognized as key players in the tumor microenvironment of breast cancer, but substantial interobserver variability among pathologists has impeded its utility as a biomarker. We developed a deep learning (DL)-based TIL analyzer to evaluate stromal TILs (sTILs) in breast cancer. Three pathologists evaluated 402 whole slide images of breast cancer and interpreted the sTIL scores. A standalone performance of the DL model was evaluated in the 210 cases (52.2%) exhibiting sTIL score differences of less than 10 percentage points, yielding a concordance correlation coefficient of 0.755 (95% confidence interval [CI], 0.693-0.805) in comparison to the pathologists' scores. For the 226 slides (56.2%) showing a 10 percentage points or greater variance between pathologists and the DL model, revisions were made. The number of discordant cases was reduced to 116 (28.9%) with the DL assistance (p < 0.001). The DL assistance also increased the concordance correlation coefficient of the sTIL score among every two pathologists. In triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients who underwent the neoadjuvant chemotherapy, the DL-assisted revision notably accentuated higher sTIL scores in responders (26.8 ± 19.6 vs. 19.0 ± 16.4, p = 0.003). Furthermore, the DL-assistant revision disclosed the correlation of sTIL-high tumors (sTIL ≥ 50) with the chemotherapeutic response (odd ratio 1.28 [95% confidence interval, 1.01-1.63], p = 0.039). Through enhancing inter-pathologist concordance in sTIL interpretation and predicting neoadjuvant chemotherapy response, here we report the utility of the DL-based tool as a reference for sTIL scoring in breast cancer assessment.

Artificial Intelligence in the Pathology of Gastric Cancer.

Recent advances in artificial intelligence (AI) have provided novel tools for rapid and precise pathologic diagnosis. The introduction of digital pathology has enabled the acquisition of scanned slide images that are essential for the application of AI. The application of AI for improved pathologic diagnosis includes the error-free detection of potentially negligible lesions, such as a minute focus of metastatic tumor cells in lymph nodes, the accurate diagnosis of potentially controversial histologic findings, such as very well-differentiated carcinomas mimicking normal epithelial tissues, and the pathological subtyping of the cancers. Additionally, the utilization of AI algorithms enables the precise decision of the score of immunohistochemical markers for targeted therapies, such as human epidermal growth factor receptor 2 and programmed death-ligand 1. Studies have revealed that AI assistance can reduce the discordance of interpretation between pathologists and more accurately predict clinical outcomes. Several approaches have been employed to develop novel biomarkers from histologic images using AI. Moreover, AI-assisted analysis of the cancer microenvironment showed that the distribution of tumor-infiltrating lymphocytes was related to the response to the immune checkpoint inhibitor therapy, emphasizing its value as a biomarker. As numerous studies have demonstrated the significance of AI-assisted interpretation and biomarker development, the AI-based approach will advance diagnostic pathology.

Simultaneous establishment of pancreatic cancer organoid and cancer-associated fibroblast using a single-pass endoscopic ultrasound-guided fine-needle biopsy specimen.

Considering the critical roles of cancer-associated fibroblasts (CAFs) in pancreatic cancer, recent studies have attempted to incorporate stromal elements into organoid models to recapitulate the tumor microenvironment. This study aimed to evaluate the feasibility of patient-derived organoid (PDO) and CAF cultures by using single-pass endoscopic ultrasound-guided fine-needle biopsy (EUS-FNB) samples from prospectively enrolled pancreatic cancer patients. The obtained samples were split into two portions for PDO and CAF cultures. PDOs and CAFs were cultured successfully in 54.4% (31/57) and 47.4% (27/57) of the cases, respectively. Both components were established in 21 cases (36.8%). Various clinicopathologic factors, including the tumor size, tumor location, clinical stage, histologic subtype, and tumor differentiation, did not influence the PDO establishment. Instead, the presence of necrosis in tumor samples was associated with initial PDO generation but no further propagation beyond passage 5 (P = 0.024). The "poorly cohesive cell carcinoma pattern" also negatively influenced the PDO establishment (P = 0.018). Higher stromal proportion in tumor samples was a decisive factor for successful CAF culture (P = 0.005). Our study demonstrated that the coestablishment of PDOs and CAFs is feasible even with a single-pass EUS-FNB sample, implying an expanding role of endoscopists in future precision medicine.

Cancer-associated fibroblast-derived secreted phosphoprotein 1 contributes to resistance of hepatocellular carcinoma to sorafenib and lenvatinib.

BACKGROUND: Cancer-associated fibroblasts (CAFs) play an important role in the induction of chemo-resistance. This study aimed to clarify the mechanism underlying CAF-mediated resistance to two tyrosine kinase inhibitors (TKIs), sorafenib and lenvatinib, and to identify a novel therapeutic target for overcoming TKI resistance in hepatocellular carcinoma (HCC). METHODS: We performed a systematic integrative analysis of publicly available gene expression datasets and whole-transcriptome sequencing data from 9 pairs of CAFs and para-cancer fibroblasts isolated from human HCC and para-tumor tissues, respectively, to identify key molecules that might induce resistance to TKIs. We then performed in vitro and in vivo experiments to validate selected targets and related mechanisms. The associations of plasma secreted phosphoprotein 1 (SPP1) expression levels before sorafenib/lenvatinib treatment with progression-free survival (PFS) and overall survival (OS) of 54 patients with advanced HCC were evaluated using Kaplan-Meier and Cox regression analysis. RESULTS: Bioinformatic analysis identified CAF-derived SPP1 as a candidate molecule driving TKI resistance. SPP1 inhibitors reversed CAF-induced TKI resistance in vitro and in vivo. CAF-derived SPP1 activated rapidly accelerated fibrosarcoma (RAF)/mitogen-activated protein kinase (MAPK) and phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) through the integrin-protein kinase C-alpha (PKCα) signaling pathway and promoted epithelial-to-mesenchymal transition (EMT). A high plasma SPP1 level before TKI treatment was identified as an independent predictor of poor PFS (P = 0.026) and OS (P = 0.047) in patients with advanced HCC after TKI treatment. CONCLUSIONS: CAF-derived SPP1 enhances TKI resistance in HCC via bypass activation of oncogenic signals and EMT promotion. Its inhibition represents a promising therapeutic strategy against TKI resistance in HCC. Moreover, plasma SPP1 level before TKI treatment represents a potential biomarker for treatment response prediction.

Effects of Oxygen-Containing Functional Groups on the Electrochemical Performance of Activated Carbon for EDLCs.

Activated carbon (AC) is used in commercial electric double-layer capacitors (EDLC) as electrode active material owing to its favorable properties. However, oxygen functional groups (OFGs) present in AC reduce the lifespan of EDLCs. Thus, we investigated the correlation between the OFGs in AC and their electrochemical characteristics. Samples were prepared by heat-treating commercial AC at 300 °C-900 °C for 1 h under two gas atmospheres (N2 and 4% H2/N2 mixed gas). The textural properties were studied, and the reduction characteristics of AC under Ar and H2/Ar mixed gas atmospheres were investigated. Additionally, changes in the OFGs with respect to the heat-treatment conditions were examined via X-ray photoelectron spectroscopy. The specific surface areas of AC-N and AC-H were 2220-2040 and 2220-2090 m2/g, respectively. Importantly, the samples treated in hydrogen gas exhibited a higher yield than those treated in nitrogen while maintaining their pore characteristics. Additionally, the electrochemical performance of the AC was significantly enhanced after the reduction process; the specific capacitance increased from 62.1 F/g to 81.6 F/g (at 0.1 A/g). Thus, heat treatment in hydrogen gas improves the electrochemical performance of EDLCs without destroying the pore characteristics of AC.

EUS-guided tissue acquisition using a novel torque technique is comparable with that of the fanning technique for solid pancreatic lesions: A multicenter randomized trial.

BACKGROUND: The torque and fanning techniques allow for multiple areas within pancreatic lesions to be targeted using different maneuvers and can, hence, enhance diagnostic outcomes. We compared the diagnostic performance of endoscopic ultrasound-guided fine-needle biopsy (EUS-FNB) for pancreatic masses using the torque and fanning techniques. METHODS: This multicenter randomized trial enrolled a total of 160 consecutive patients who underwent EUS-FNB for solid pancreatic tumors using either the torque or fanning technique. Three passes were permitted for each lesion, and the technique sequence was randomly assigned as either torque first or fanning first with the standard technique as a reference. RESULTS: The median quality score of the histological samples was significantly higher in the torque and fanning group than in the standard group (p < .001). Furthermore, the torque technique provided improved sensitivity of 93.38% and accuracy of 94.30%. The standard technique provided diagnostic sensitivity of 68.84% and accuracy of 72.96%, while the fanning technique showed sensitivity of 91.85% and accuracy of 93.04%. CONCLUSIONS: The new torque technique enables the acquisition of better-quality samples and can potentially increase the diagnostic outcomes in the EUS-FNB of pancreatic solid masses, with the same recommendations as those for the fanning technique.

Estrogen-responsive cancer-associated fibroblasts promote invasive property of gastric cancer in a paracrine manner via CD147 production.

Estrogen signaling has been extensively studied, especially in cancers that express estrogen receptor alpha (ERα). However, little is known regarding the effect of estrogen on cancer-associated fibroblasts (CAFs). Here, we explored the role of estrogen signaling of CAFs in gastric cancer (GC) progression. We investigated the phenotypic changes in CAFs upon 17ß-estradiol (E2) treatment using ERα-negative/positive CAFs, and the conditioned media (CM) collected from these were compared with regard to cancer cell proliferation, migration, and invasion. A paracrine factor was found using a cytokine array and was confirmed using qRT-PCR, western blotting, and enzyme-linked immunosorbent assays. ERα-CD147-matrix metalloproteinase (MMP) axis was confirmed by knockdown experiments using specific siRNAs. We found that a subset of CAFs expressed ERα. ERα-positive CAFs were responsive to E2, inducing ERα expression in a dose-dependent manner. Although E2 did not induce the proliferation of ERα-positive CAFs, the CM from E2-bound ERα-positive CAFs significantly promoted cancer cell migration and invasion. Cytokine array revealed that CD147 was induced in ERα-positive CAFs upon E2 treatment; this was mediated via ERα. Increased CD147 upregulated MMP2 and MMP9 in CAFs, and also influenced cancer cells in a paracrine manner to increase MMPs and CD147 in cancer cells. High CD147 expression in tumor tissue was associated with a worse prognosis in GC patients. Our data suggest that estrogen signaling activation in CAFs and the byproduct CD147 are among the critical mediators between the interplay of CAFs and cancer cells to facilitate cancer progression.

Artificial Intelligence-Powered Whole-Slide Image Analyzer Reveals a Distinctive Distribution of Tumor-Infiltrating Lymphocytes in Neuroendocrine Neoplasms.

Despite the importance of tumor-infiltrating lymphocytes (TIL) and PD-L1 expression to the immune checkpoint inhibitor (ICI) response, a comprehensive assessment of these biomarkers has not yet been conducted in neuroendocrine neoplasm (NEN). We collected 218 NENs from multiple organs, including 190 low/intermediate-grade NENs and 28 high-grade NENs. TIL distribution was derived from Lunit SCOPE IO, an artificial intelligence (AI)-powered hematoxylin and eosin (H&E) analyzer, as developed from 17,849 whole slide images. The proportion of intra-tumoral TIL-high cases was significantly higher in high-grade NEN (75.0% vs. 46.3%, p = 0.008). The proportion of PD-L1 combined positive score (CPS) ≥ 1 case was higher in high-grade NEN (85.7% vs. 33.2%, p < 0.001). The PD-L1 CPS ≥ 1 group showed higher intra-tumoral, stromal, and combined TIL densities, compared to the CPS < 1 group (7.13 vs. 2.95, p < 0.001; 200.9 vs. 120.5, p < 0.001; 86.7 vs. 56.1, p = 0.004). A significant correlation was observed between TIL density and PD-L1 CPS (r = 0.37, p < 0.001 for intra-tumoral TIL; r = 0.24, p = 0.002 for stromal TIL and combined TIL). AI-powered TIL analysis reveals that intra-tumoral TIL density is significantly higher in high-grade NEN, and PD-L1 CPS has a positive correlation with TIL densities, thus showing its value as predictive biomarkers for ICI response in NEN.

Artificial intelligence-powered programmed death ligand 1 analyser reduces interobserver variation in tumour proportion score for non-small cell lung cancer with better prediction of immunotherapy response.

BACKGROUND: Manual evaluation of programmed death ligand 1 (PD-L1) tumour proportion score (TPS) by pathologists is associated with interobserver bias. OBJECTIVE: This study explored the role of artificial intelligence (AI)-powered TPS analyser in minimisation of interobserver variation and enhancement of therapeutic response prediction. METHODS: A prototype model of an AI-powered TPS analyser was developed with a total of 802 non-small cell lung cancer (NSCLC) whole-slide images. Three independent board-certified pathologists labelled PD-L1 TPS in an external cohort of 479 NSCLC slides. For cases of disagreement between each pathologist and the AI model, the pathologists were asked to revise the TPS grade (<1%, 1%-49% and ≥50%) with AI assistance. The concordance rates among the pathologists with or without AI assistance and the effect of the AI-assisted revision on clinical outcome upon immune checkpoint inhibitor (ICI) treatment were evaluated. RESULTS: Without AI assistance, pathologists concordantly classified TPS in 81.4% of the cases. They revised their initial interpretation by using the AI model for the disagreement cases between the pathologist and the AI model (N = 91, 93 and 107 for each pathologist). The overall concordance rate among the pathologists was increased to 90.2% after the AI assistance (P < 0.001). A reduction in hazard ratio for overall survival and progression-free survival upon ICI treatment was identified in the TPS subgroups after the AI-assisted TPS revision. CONCLUSION: The AI-powered TPS analyser assistance improves the pathologists' consensus of reading and prediction of the therapeutic response, raising a possibility of standardised approach for the accurate interpretation.

miR-4742-5p promotes invasiveness of gastric cancer via targeting Rab43: An in vitro study.

miRNA (miR)-4742-5p is a recently identified microRNA regarding progression and metastasis in gastric cancer (GC). However, the biological function of this novel miRNA is largely unknown. We identified that the miR-4742-5p expression level was variably increased in GC cell lines. Suppression of miR-4742-5p using miR-inhibitor reduced the proliferation, migration, and invasion of GC cells with high miR-4742-5p expression, whereas overexpression of miR-4742-5p-mimic enhanced the aforementioned properties in GC cells with low miR-4742-5p expression. miR-4742-5p expression induced the decreases of Zo-1 and E-cadherin expression as well as the increases of vimentin and N-cadherin expression, leading to epithelial-mesenchymal transition (EMT) of cancer cells. RNA sequencing results indicated Ras-related GTP-binding protein 43 (Rab43) as a potential target gene. We identified that the expression of Rab43 is associated with activation of AKT and nuclear factor-kappa B (NF-κB) which are key oncogenic pathways in cancer cells. Our results demonstrate a new component in GC progression, promising a potential therapeutic strategy.