Collision Tumor of the Ovary: Adult Granulosa Cell Tumor and Mesonephric-like Adenocarcinoma.

Collision tumors of the ovaries are rare, with only a few reports in the literature. Adult granulosa cell tumors are a relatively common primary tumor component of previously reported collision tumors. The combination of serous and mucinous tumors with adult granulosa cell tumors has been reported in several cases. On the other hand, mesonephric-like adenocarcinomas are rare neoplasms that commonly arise in the uterine corpus and ovaries. In this report, we present the case of a collision tumor composed of an adult granulosa cell tumor and mesonephric-like adenocarcinoma of the ovary in a 63-year-old woman. The initial magnetic resonance imaging findings showed a cystic mass with an internal hemorrhage, which suggested an adult granulosa cell tumor, and a solid mass with different enhancements. Microscopically, the tumor had two distinct components: An adult granulosa cell tumor and a mesonephric-like adenocarcinoma. Recognizing collision tumors consisting of slow-growing and aggressive tumors may prove beneficial in future diagnostic and treatment strategies.

Improved Diagnostic Accuracy of Thyroid Fine-Needle Aspiration Cytology with Artificial Intelligence Technology.

Background: Artificial intelligence (AI) is increasingly being applied in pathology and cytology, showing promising results. We collected a large dataset of whole slide images (WSIs) of thyroid fine-needle aspiration cytology (FNA), incorporating z-stacking, from institutions across the nation to develop an AI model. Methods: We conducted a multicenter retrospective diagnostic accuracy study using thyroid FNA dataset from the Open AI Dataset Project that consists of digitalized images samples collected from 3 university hospitals and 215 Korean institutions through extensive quality check during the case selection, scanning, labeling, and reviewing process. Multiple z-layer images were captured using three different scanners and image patches were extracted from WSIs and resized after focus fusion and color normalization. We pretested six AI models, determining Inception ResNet v2 as the best model using a subset of dataset, and subsequently tested the final model with total datasets. Additionally, we compared the performance of AI and cytopathologists using randomly selected 1031 image patches and reevaluated the cytopathologists' performance after reference to AI results. Results: A total of 10,332 image patches from 306 thyroid FNAs, comprising 78 malignant (papillary thyroid carcinoma) and 228 benign from 86 institutions were used for the AI training. Inception ResNet v2 achieved highest accuracy of 99.7%, 97.7%, and 94.9% for training, validation, and test dataset, respectively (sensitivity 99.9%, 99.6%, and 100% and specificity 99.6%, 96.4%, and 90.4% for training, validation, and test dataset, respectively). In the comparison between AI and human, AI model showed higher accuracy and specificity than the average expert cytopathologists beyond the two-standard deviation (accuracy 99.71% [95% confidence interval (CI), 99.38-100.00%] vs. 88.91% [95% CI, 86.99-90.83%], sensitivity 99.81% [95% CI, 99.54-100.00%] vs. 87.26% [95% CI, 85.22-89.30%], and specificity 99.61% [95% CI, 99.23-99.99%] vs. 90.58% [95% CI, 88.80-92.36%]). Moreover, after referring to the AI results, the performance of all the experts (accuracy 96%, 95%, and 96%, respectively) and the diagnostic agreement (from 0.64 to 0.84) increased. Conclusions: These results suggest that the application of AI technology to thyroid FNA cytology may improve the diagnostic accuracy as well as intra- and inter-observer variability among pathologists. Further confirmatory research is needed.

Immunoglobulin G4-related disease mimicking an appendiceal neoplasm: a case report.

Immunoglobulin G4-related disease (IgG4-RD) is a fibroinflammatory condition characterized by chronic activation of the immune system and a tendency to form tumorous lesions. IgG4-RD is frequently characterized by the presence of tumor-like masses affecting multiple organs and is easily mistaken for a malignant neoplasm. However, IgG4-RD affecting the appendix is extremely rare, with only seven cases reported previously. We report the case of a woman in her early 60s who presented with insidious abdominal pain and radiological findings mimicking appendiceal neoplasms. After diagnosing appendiceal neoplasms, surgery was performed. The patient had a serum IgG4 concentration of <1.35 g/L, which did not satisfy one of the three revised comprehensive diagnostic criteria for IgG4-RD. A pathological examination was conducted, and the patient was diagnosed with appendiceal IgG4-RD. To the best of our knowledge, there have been no previously reported cases of IgG4-RD affecting the appendix in patients with low serum IgG4 concentrations. This report may prove beneficial for the future understanding of IgG4-RD and for the revision of diagnostic and treatment strategies.

Poly (ADP-ribose) polymerase-1 (PARP-1) is a good prognostic marker for pancreatic/periampullary cancers.

BACKGROUND: Periampullary cancer (PAC) is highly aggressive with no effective adjuvant therapy or prognostic markers. Recently, poly (ADP-ribose) polymerases-1 (PARP-1) have emerged as a target in solid cancers, and its relationship with epithelial-mesenchymal transition (EMT) has been observed. However, the relationship between PARP-1 and EMT in PAC has not explored well. METHODS: We assessed the prognostic significance of PARP1 in 190 PACs patients and correlated it with EMT markers, including FGF8, FGFR4, MMP2, MMP3, Snail, and ZEB1. Immunohistochemistry for PARP-1 and EMT markers was performed using a tissue microarray. RESULTS: PARP-1 and FGF8 expression were associated with better survival unlike other solid cancers (P = 0.006 and P = 0.003), and MMP3 and ZEB1 expression were associated with poor prognosis in multivariate and survival analyses (P = 0.009 and P < 0.001). In addition, PARP-1 is related negatively to Snail but not related with other EMT markers, implying an independent mechanism between PARP-1 and EMT in PACs. PARP-1 and FGF8 are independent good survival markers in PACs unlike other solid cancers. CONCLUSIONS: PARP-1 and FGF8 in PACs could not be related to the EMT pathway but must be rather understood in light of similar cancer-protective roles. Further studies are required on EMT-associated immune markers in PACs.

Artificial-Intelligence-Assisted Detection of Metastatic Colorectal Cancer Cells in Ascitic Fluid.

Ascites cytology is a cost-effective test for metastatic colorectal cancer (CRC) in the abdominal cavity. However, metastatic carcinoma of the peritoneum is difficult to diagnose based on biopsy findings, and ascitic aspiration cytology has a low sensitivity and specificity and a high inter-observer variability. The aim of the present study was to apply artificial intelligence (AI) to classify benign and malignant cells in ascites cytology patch images of metastatic CRC using a deep convolutional neural network. Datasets were collected from The OPEN AI Dataset Project, a nationwide cytology dataset for AI research. The numbers of patch images used for training, validation, and testing were 56,560, 7068, and 6534, respectively. We evaluated 1041 patch images of benign and metastatic CRC in the ascitic fluid to compare the performance of pathologists and an AI algorithm, and to examine whether the diagnostic accuracy of pathologists improved with the assistance of AI. This AI method showed an accuracy, a sensitivity, and a specificity of 93.74%, 87.76%, and 99.75%, respectively, for the differential diagnosis of malignant and benign ascites. The diagnostic accuracy and sensitivity of the pathologist with the assistance of the proposed AI method increased from 86.8% to 90.5% and from 73.3% to 79.3%, respectively. The proposed deep learning method may assist pathologists with different levels of experience in diagnosing metastatic CRC cells of ascites.

Two Cases of Lymph Node Metastasis Found in Differentiated, Small-Sized Gastric Adenocarcinomas: Did Tumor Budding Play a Critical Role?

Background: Endoscopic resection (ER) is a minimally invasive therapeutic approach for early gastric cancer (EGC), particularly for cases with a low risk of lymph node metastasis (LNM). Tumor budding (TB) has gained attention as a potential prognostic indicator for LNM in EGC. Case Presentation: We report two cases-a 73-year-old and an 81-year-old male patient-who presented with gastric adenocarcinoma. Both patients had small-sized, differentiated, and intramucosal adenocarcinomas. However, high-grade TBs per high-power field under ×200 magnification at the invasive front and LNMs were found in both cases. Conclusions: These cases conformed to the post-ER observation guidelines of the current treatment protocol, yet demonstrated LNMs. We found that TB could serve as an effective prognostic marker for LNM compared to traditional risk factors. The aim of this study is to re-examine the ability of TB to predict LNM in EGC, thereby providing an impetus for reconsideration and potential revision of the current treatment guidelines for EGC.

Deep Learning-Based Computational Cytopathologic Diagnosis of Metastatic Breast Carcinoma in Pleural Fluid.

A Pleural effusion cytology is vital for treating metastatic breast cancer; however, concerns have arisen regarding the low accuracy and inter-observer variability in cytologic diagnosis. Although artificial intelligence-based image analysis has shown promise in cytopathology research, its application in diagnosing breast cancer in pleural fluid remains unexplored. To overcome these limitations, we evaluate the diagnostic accuracy of an artificial intelligence-based model using a large collection of cytopathological slides, to detect the malignant pleural effusion cytology associated with breast cancer. This study includes a total of 569 cytological slides of malignant pleural effusion of metastatic breast cancer from various institutions. We extracted 34,221 augmented image patches from whole-slide images and trained and validated a deep convolutional neural network model (DCNN) (Inception-ResNet-V2) with the images. Using this model, we classified 845 randomly selected patches, which were reviewed by three pathologists to compare their accuracy. The DCNN model outperforms the pathologists by demonstrating higher accuracy, sensitivity, and specificity compared to the pathologists (81.1% vs. 68.7%, 95.0% vs. 72.5%, and 98.6% vs. 88.9%, respectively). The pathologists reviewed the discordant cases of DCNN. After re-examination, the average accuracy, sensitivity, and specificity of the pathologists improved to 87.9, 80.2, and 95.7%, respectively. This study shows that DCNN can accurately diagnose malignant pleural effusion cytology in breast cancer and has the potential to support pathologists.

Uterine Cervical Angioleiomyoma Mimicking Squamous Cell Carcinoma.

Angioleiomyoma, a rare variant of leiomyoma, is a benign tumor of mesenchymal origin. Angioleiomyomas of the female urogenital tract are extremely rare, with only six cases of uterine cervical angioleiomyoma previously reported in the literature. In this case study, we report on a 49-year-old female patient who presented with menorrhagia whose initial magnetic resonance imaging (MRI) findings suggested cervical squamous cell carcinoma (SCC). However, following the hysterectomy, histological examination confirmed the lesion to be angioleiomyoma. To the best of our knowledge, there have been no previously reported cases of angioleiomyomas presenting with MRI findings that are suggestive of uterine SCC. Recognizing that angioleiomyomas can mimic uterine malignancies on MRI may prove beneficial for future diagnostic and treatment strategies.

Recent application of artificial intelligence on histopathologic image-based prediction of gene mutation in solid cancers.

PURPOSE: Evaluation of genetic mutations in cancers is important because distinct mutational profiles help determine individualized drug therapy. However, molecular analyses are not routinely performed in all cancers because they are expensive, time-consuming and not universally available. Artificial intelligence (AI) has shown the potential to determine a wide range of genetic mutations on histologic image analysis. Here, we assessed the status of mutation prediction AI models on histologic images by a systematic review. METHODS: A literature search using the MEDLINE, Embase and Cochrane databases was conducted in August 2021. The articles were shortlisted by titles and abstracts. After a full-text review, publication trends, study characteristic analysis and comparison of performance metrics were performed. RESULTS: Twenty-four studies were found mostly from developed countries, and their number is increasing. The major targets were gastrointestinal, genitourinary, gynecological, lung and head and neck cancers. Most studies used the Cancer Genome Atlas, with a few using an in-house dataset. The area under the curve of some of the cancer driver gene mutations in particular organs was satisfactory, such as 0.92 of BRAF in thyroid cancers and 0.79 of EGFR in lung cancers, whereas the average of all gene mutations was 0.64, which is still suboptimal. CONCLUSION: AI has the potential to predict gene mutations on histologic images with appropriate caution. Further validation with larger datasets is still required before AI models can be used in clinical practice to predict gene mutations.

Intratumoral Budding in Pretreatment Biopsies, among Tumor Microenvironmental Components, Can Predict Prognosis and Neoadjuvant Therapy Response in Colorectal Adenocarcinoma.

Background and Objectives: The prediction of the prognosis and effect of neoadjuvant therapy is vital for patients with advanced or unresectable colorectal carcinoma (CRC). Materials and Methods: We investigated several tumor microenvironment factors, such as intratumoral budding (ITB), desmoplastic reaction (DR), and Klintrup-Mäkinen (KM) inflammation grade, and the tumor-stroma ratio (TSR) in pretreatment biopsy samples (PBSs) collected from patients with advanced or unresectable CRC. A total of 85 patients with 74 rectal carcinomas and 11 colon cancers treated at our hospital were enrolled; 66 patients had curative surgery and 19 patients received palliative treatment. Results: High-grade ITB was associated with recurrence (p = 0.002), death (p = 0.034), and cancer-specific death (p = 0.034). Immature DR was associated with a higher grade of clinical tumor-node-metastasis stage (cTNM) (p = 0.045), cN category (p = 0.045), and cM category (p = 0.046). The KM grade and TSR were not related to any clinicopathological factors. High-grade ITB had a significant relationship with tumor regression in patients who received curative surgery (p = 0.049). Conclusions: High-grade ITB in PBSs is a potential unfavorable prognostic factor for patients with advanced CRC. Immature DR, TSR, and KM grade could not predict prognosis or therapy response in PBSs.

Recent Applications of Artificial Intelligence from Histopathologic Image-Based Prediction of Microsatellite Instability in Solid Cancers: A Systematic Review.

Cancers with high microsatellite instability (MSI-H) have a better prognosis and respond well to immunotherapy. However, MSI is not tested in all cancers because of the additional costs and time of diagnosis. Therefore, artificial intelligence (AI)-based models have been recently developed to evaluate MSI from whole slide images (WSIs). Here, we aimed to assess the current state of AI application to predict MSI based on WSIs analysis in MSI-related cancers and suggest a better study design for future studies. Studies were searched in online databases and screened by reference type, and only the full texts of eligible studies were reviewed. The included 14 studies were published between 2018 and 2021, and most of the publications were from developed countries. The commonly used dataset is The Cancer Genome Atlas dataset. Colorectal cancer (CRC) was the most common type of cancer studied, followed by endometrial, gastric, and ovarian cancers. The AI models have shown the potential to predict MSI with the highest AUC of 0.93 in the case of CRC. The relatively limited scale of datasets and lack of external validation were the limitations of most studies. Future studies with larger datasets are required to implicate AI models in routine diagnostic practice for MSI prediction.

Tumor Budding as a Marker for Poor Prognosis and Epithelial-Mesenchymal Transition in Lung Cancer: A Systematic Review and Meta-Analysis.

Introduction: Currently, tumor budding (TB) is considered to predict the prognosis of patients. The prognostic significance of TB has also been explored in patients with lung cancer, but has not been fully clarified. In the present meta-analysis, we evaluated the prognostic significance, clinicopathological value, and relationship with epithelial-mesenchymal transition (EMT) of TB in lung cancer. Methods: The MEDLINE, EMBASE, and Cochrane databases were searched up to July 7, 2021, for the relevant articles that showed the relationship between TB and prognosis in patients with lung cancer. For statistical analysis, we used pooled hazard ratios (HRs) with their corresponding 95% confidence intervals (CIs) to assess the correlation between high-grade TB expression and overall survival (OS), disease-free survival (DFS), progression-free survival (PFS), clinicopathological factors, and EMT markers. Results: A total of 3,784 patients from 10 independent studies were included in the statistical analysis. Our results indicated that high-grade TB was significantly associated with poor OS [HR 1.64 (95% CI, 1.43-1.87)] and DFS [HR 1.65 (95% CI, 1.22-2.25)]. In terms of clinicopathological characteristics, high-grade TB was associated with larger tumor size, higher T and N stage, pleural invasion, vascular invasion, lymphatic invasion, and severe nuclear atypia. Interestingly, smoking showed significant association with high-grade TB, despite the fact that previous studies could not show a significant relationship between them. Furthermore, through our systematic analysis, high-grade TB showed a significant relationship with EMT markers. Conclusion: Our findings indicate that high-grade TB is associated with a worse prognosis in patients with lung cancer. TB evaluation should be implemented in routine pathological diagnosis, which may guide the patient's treatment.

Current Trend of Artificial Intelligence Patents in Digital Pathology: A Systematic Evaluation of the Patent Landscape.

The integration of digital pathology (DP) with artificial intelligence (AI) enables faster, more accurate, and thorough diagnoses, leading to more precise personalized treatment. As technology is advancing rapidly, it is critical to understand the current state of AI applications in DP. Therefore, a patent analysis of AI in DP is required to assess the application and publication trends, major assignees, and leaders in the field. We searched five major patent databases, namely, those of the USPTO, EPO, KIPO, JPO, and CNIPA, from 1974 to 2021, using keywords such as DP, AI, machine learning, and deep learning. We discovered 6284 patents, 523 of which were used for trend analyses on time series, international distribution, top assignees; word cloud analysis; and subject category analyses. Patent filing and publication have increased exponentially over the past five years. The United States has published the most patents, followed by China and South Korea (248, 117, and 48, respectively). The top assignees were Paige.AI, Inc. (New York City, NY, USA) and Siemens, Inc. (Munich, Germany) The primary areas were whole-slide imaging, segmentation, classification, and detection. Based on these findings, we expect a surge in DP and AI patent applications focusing on the digitalization of pathological images and AI technologies that support the vital role of pathologists.

Tumor Budding in Gynecologic Cancer as a Marker for Poor Survival: A Systematic Review and Meta-Analysis of the Perspectives of Epithelial-Mesenchymal Transition.

This study aimed to assess the prognostic significance, assessment methods, and molecular features of tumor budding (TB). A literature search of Medline, EMBASE, Cochrane Library, and eleven cohort studies (seven cervical and four endometrial cancers) was conducted. Three assessment methods for TB involving 2009 patients were collected and constituted in the analysis. Our meta-analysis showed that TB was a marker of poor survival, regardless of the cancer origin site or assessment method (overall survival: hazard ratio [HR], 2.40; 95% confidence interval [CI], 1.82-3.17; disease-free survival: HR, 3.32; 95% CI, 2.46-4.48). In endometrial cancers, TB is associated with the epithelial-mesenchymal transition, microvessel density, and decreased hormone receptor expression. Thus, we suggest TB as a poor prognostic marker for all gynecologic cancers.

Calculated Tumor-Associated Neutrophils Are Associated with the Tumor-Stroma Ratio and Predict a Poor Prognosis in Advanced Gastric Cancer.

The tumor-associated neutrophils (TANs) value and tumor-stroma ratio (TSR) are promising prognostic parameters in the tumor microenvironment. We aimed to evaluate the prognostic role and relationship of TANs and TSR in gastric cancer. Our study comprised 157 patients who underwent gastrectomy for advanced gastric cancer. TANs were assessed by immunohistochemical staining (CD15 and CD66b) and were analyzed with an image analyzer. TANs have been known to have different functional subpopulations of N1 (anti-tumor) and N2 (pro-tumor). We developed "calculated TANs with pro-tumor function (cN2; CD15 minus CD66b)". The TSR was evaluated using hematoxylin and eosin staining. High-grade CD15-positive, cN2 in the tumor center, and TSR were significantly related to poor disease-free survival (DFS). TSR and cN2 were independent prognostic factors for DFS (hazard ratio (HR) = 2.614; p = 0.001, HR = 3.976; p = 0.002) and cN2 in the tumor center showed a positive correlation with TSR (R = 0.179, p = 0.025). While CD66b stained both N1 and N2, CD15 detected most of N2. Combining both markers revealed a novel cN2, which was an independent marker of poor prognosis. The transformation from N1 to N2 predominantly occurred in the tumor center, and was associated with TSR.

Prognostic value of Dickkopf-1 and ß-catenin expression according to the antitumor immunity of CD8-positive tumor-infiltrating lymphocytes in biliary tract cancer.

The role of ß-catenin and Dickkopf-1 (DKK1) is dependent on the specific immunobiology of T cell inflammation in biliary tract cancer (BTC). We aimed to analyze the role of DKK1 or ß-catenin as a prognostic factor in BTC, and determine the clinical associations of ß-catenin and DKK1 with CD8+ tumor-infiltrating lymphocytes (TIL). We used data from The Cancer Genome Atlas Research Network and the clinicopathological data of 145 patients with BTC who had undergone primary radical resection between 2006 and 2016. CD8+ TIL expression was a significant predictor of favorable overall survival (OS) and relapse-free survival (RFS) (median OS, 34.9 months in high-TIL, 16.7 months in low-TIL, P < 0.0001 respectively; median RFS, 27.1 months in high-TIL, 10.0 months in low-TIL, P < 0.0001 respectively). In the high-CD8+ TIL BTC group, the tumor expression of ß-catenin and DKK1 had a significant negative impact on either OS or RFS. In the low-TIL BTC group, there were no differences according to ß-catenin and DKK1 expression. Cox regression multivariate analysis demonstrated that CD8+ TIL and ß-catenin retained significant association with OS. Among patients with resected BTC, the ß-catenin and DKK1 protein and high CD8+ TIL levels were associated with poor and good clinical outcomes, respectively.

Identification of Survival-Specific Genes in Clear Cell Renal Cell Carcinoma Using a Customized Next-Generation Sequencing Gene Panel.

PURPOSE: Although mutations are associated with carcinogenesis, little is known about survival-specific genes in clear cell renal cell carcinoma (ccRCC). We developed a customized next-generation sequencing (NGS) gene panel with 156 genes. The purpose of this study was to investigate whether the survival-specific genes we found were present in Korean ccRCC patients, and their association with clinicopathological findings. MATERIALS AND METHODS: DNA was extracted from the formalin-fixed, paraffin-embedded tissue of 22 ccRCC patients. NGS was performed using our survival-specific gene panel with an Illumina MiSeq. We analyzed NGS data and the correlations between mutations and clinicopathological findings and also compared them with data from the Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) and Renal Cell Cancer-European Union (RECA-EU). RESULTS: We found a total of 100 mutations in 37 of the 156 genes (23.7%) in 22 ccRCC patients. Of the 37 mutated genes, 11 were identified as clinicopathologically significant. Six were novel survival-specific genes (ADAMTS10, CARD6, NLRP2, OBSCN, SECISBP2L, and USP40), and five were top-ranked mutated genes (AKAP9, ARID1A, BAP1, KDM5C, and SETD2). Only CARD6 was validated as an overall survival-specific gene in this Korean study (p = 0.04, r = -0.441), TCGA-KIRC cohort (p = 0.0003), RECA-EU (p = 0.0005). The 10 remaining gene mutations were associated with clinicopathological findings; disease-free survival, mortality, nuclear grade, sarcomatoid component, N-stage, sex, and tumor size. CONCLUSIONS: We discovered 11 survival-specific genes in ccRCC using data from TCGA-KIRC, RECA-EU, and Korean patients. We are the first to find a correlation between CARD6 and overall survival in ccRCC. The 11 genes, including CARD6, NLRP2, OBSCN, and USP40, could be useful diagnostic, prognostic, and therapeutic markers in ccRCC.

High Poly(ADP-Ribose) Polymerase Expression Does Relate to Poor Survival in Solid Cancers: A Systematic Review and Meta-Analysis.

Poly (ADP-ribose) polymerase (PARP) is a DNA damage repair protein, and its inhibitors have shown promising results in clinical trials. The prognostic significance of PARP is inconsistent in studies of various cancers. In the present study, we conducted a systematic review and meta-analysis to reveal the prognostic and clinicopathological significance of PARP expression in multiple solid cancers. We searched the MEDLINE, EMBASE, and Cochrane databases for relevant research articles published from 2005 to 2021. The pooled hazard ratio (HR) with confidence interval (CI) was calculated to investigate the relationship between PARP expression and survival in multiple solid cancers. In total, 10,667 patients from 31 studies were included. A significant association was found between higher PARP expression and overall survival (OS) (HR = 1.54, 95% CI = 1.34-1.76, p < 0.001), disease-free survival (DFS) (HR = 1.15, 95% CI = 1.10-1.21, p < 0.001), and progression-free survival (PFS) (HR = 1.05, 95% CI = 1.03-1.08, p < 0.001). Subgroup analyses showed that PARP overexpression was significantly related to poor OS in patients with breast cancers (HR = 1.38, 95% CI = 1.28-1.49, p < 0.001), ovary cancers (HR = 1.21, 95% CI = 1.10-1.33, p = 0.001), lung cancers (HR = 2.11, 95% CI = 1.29-3.45, p = 0.003), and liver cancers (HR = 3.29, 95% CI = 1.94-5.58, p < 0.001). Regarding ethnicity, Asian people have almost twice their worst survival rate compared to Caucasians. The pooled odds ratio analysis showed a significant relationship between higher PARP expression and larger tumour size, poor tumour differentiation, lymph node metastasis, distant metastasis, higher TNM stage and lymphovascular invasion, and positive immunoreactivity for Ki-67, BRCA1, and BRCA2. In addition, nuclear expression assessed by the QS system using Abcam and Santa Cruz Biotechnology seems to be the most commonly used and reproducible IHC method for assessing PARP expression. This meta-analysis revealed that higher PARP expression was associated with a worse OS, DFS, and PFS in patients with solid cancers. Moreover, inhibition of this pathway through its specific inhibitors may extend the survival of patients with higher PARP expression.

DeepRePath: Identifying the Prognostic Features of Early-Stage Lung Adenocarcinoma Using Multi-Scale Pathology Images and Deep Convolutional Neural Networks.

The prognosis of patients with lung adenocarcinoma (LUAD), especially early-stage LUAD, is dependent on clinicopathological features. However, its predictive utility is limited. In this study, we developed and trained a DeepRePath model based on a deep convolutional neural network (CNN) using multi-scale pathology images to predict the prognosis of patients with early-stage LUAD. DeepRePath was pre-trained with 1067 hematoxylin and eosin-stained whole-slide images of LUAD from the Cancer Genome Atlas. DeepRePath was further trained and validated using two separate CNNs and multi-scale pathology images of 393 resected lung cancer specimens from patients with stage I and II LUAD. Of the 393 patients, 95 patients developed recurrence after surgical resection. The DeepRePath model showed average area under the curve (AUC) scores of 0.77 and 0.76 in cohort I and cohort II (external validation set), respectively. Owing to low performance, DeepRePath cannot be used as an automated tool in a clinical setting. When gradient-weighted class activation mapping was used, DeepRePath indicated the association between atypical nuclei, discohesive tumor cells, and tumor necrosis in pathology images showing recurrence. Despite the limitations associated with a relatively small number of patients, the DeepRePath model based on CNNs with transfer learning could predict recurrence after the curative resection of early-stage LUAD using multi-scale pathology images.

Modified Tumor Budding as a Better Predictor of Lymph Node Metastasis in Early Gastric Cancer: Possible Real-World Applications.

Endoscopic resection (ER) is a minimally invasive treatment for early gastric cancer (EGC) with a low risk of lymph node metastasis (LNM). Recently, tumor budding (TB) has emerged as a potential predictor of LNM in EGC. We assessed the clinical significance of modified TB (mTB) that excludes the signet ring cell component and compared several TB assessment methods. Two hundred and eighty-nine patients with EGC at Uijeongbu St. Mary's Hospital from 2010 to 2021 were enrolled. In univariate analysis, age, size, depth of invasion, tumor type, histologic type, Lauren classification, lymphatic invasion, venous invasion, poorly differentiated carcinoma ("not otherwise specified" predominant), and TB were significantly associated with LNM. Multivariate regression analysis showed that mTB (difference area under the curve [dAUC] = 0.085 and 0.087) was superior to TB (dAUC = 0.054 and 0.057) in predicting LNM. In addition, total TB counts on representative slide sections (dAUC = 0.087 and 0.057) in assessing TB and mTB and the ITBCC method (dAUC = 0.085) in mTB were superior to the presence or absence method (dAUC = 0.042 and 0.029). The mTB significantly increases LNM prediction ability, which can provide important information for patients with EGC.