Systemic inflammatory markers derived from peripheral blood cell, such as the neutrophil-lymphocyte ratio (NLR), derived neutrophil-lymphocyte ratio (dNLR), platelet-lymphocyte ratio (PLR) and lymphocyte-monocyte ratio (LMR), have been demonstrated as prognostic markers in several types of malignancy. Here, we investigated and compared the association between systemic inflammatory markers and survival and developed a prognostic nomogram in breast cancer patients. We reviewed the clinical and pathological records of 661 patients diagnosed with invasive breast carcinoma between 1993 and 2011. The NLR, dNLR, PLR and LMR in the immediate preoperative period were assessed. We analyzed the relationship between these inflammatory markers and clinicopathologic variables, disease-specific survival (DSS), and disease-free survival (DFS) in patients. A nomogram was developed to predict 3- and 5-year DSS for breast cancer. In the univariate analysis, high NLR, dNLR, PLR and low LMR were all significantly associated with poor DSS and DFS. In the multivariate analysis, only the PLR (HR 3.226, 95% CI 1.768–5.885 for DSS and HR 1.824, 95% CI 1.824–6.321 for DFS) was still identified as an independent predictor of outcomes. A subgroup analysis revealed that the PLR was the sole independent marker predicting poor DSS in patients with lymph node metastasis (HR 2.294, 95% CI 1.102–4.777) and with luminal subtype (HR 4.039, 95% CI 1.905–8.562). The proposed nomogram, which includes the PLR, shows good accuracy in predicting DSS with a concordance index of 0.82. PLR is an indicator of systemic inflammation as a part of the host immune response. As an independent prognostic factor, an elevated preoperative PLR is superior to the NLR, dNLR, and LMR in predicting clinical outcomes in patients with breast cancer. Moreover, the nomogram incorporating the PLR could accurately predict individualized survival probability in breast cancer.

Background: An increasing number of studies show that genetic markers can aid in refining prognostic information and predicting the benefit from systemic therapy. Our goal was to develop a high throughput, cost-effective and simple methodology for the detection of clinically relevant hot spot mutations in colon cancer. Methods: The Maldi-Tof mass spectrometry platform and OncoCarta panel from Sequenom were used to profile 239 colon cancers and 39 metastatic lymph nodes from NSABP clinical trial C-07 utilizing routinely processed FFPET (formalin-fixed paraffin-embedded tissue). Results: Among the 238 common hot-spot cancer mutations in 19 genes interrogated by the OncoCarta panel, mutations were detected in 7 different genes at 26 different nucleotide positions in our colon cancer samples. Twenty-four assays that detected mutations in more than 1% of the samples were reconfigured into a new multiplexed panel, termed here as ColoCarta. Mutation profiling was repeated on 32 mutant samples using ColoCarta and the results were identical to results with OncoCarta, demonstrating that this methodology was reproducible. Further evidence demonstrating the validity of the data was the fact that the mutation frequencies of the most common colon cancer mutations were similar to the COSMIC (Catalog of Somatic Mutations in Cancer) database. The frequencies were 43.5% for KRAS, 20.1% for PIK3CA, and 12.1% for BRAF. In addition, infrequent mutations in NRAS, AKT1, ABL1, and MET were detected. Mutation profiling of metastatic lymph nodes and their corresponding primary tumors showed that they were 89.7% concordant. All mutations found in the lymph nodes were also found in the corresponding primary tumors, but in 4 cases a mutation was present in the primary tumor only. Conclusions: This study describes a high throughput technology that can be used to interrogate DNAs isolated from routinely processed FFPET and identifies the specific mutations that are common to colon cancer. The development of this technology and the ColoCarta panel may provide a mechanism for rapid screening of mutations in clinically relevant genes like KRAS, PIK3CA, and BRAF. Trial Registration: ClinicalTrials.gov: NSABP C-07: NCT00004931

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.

MicroRNA (miRNA) expression is known to be deregulated in cervical carcinomas. However, no data is available about the miRNA expression pattern for the minimal deviation adenocarcinoma (MDA) of uterine cervix. We sought to detect deregulated miRNAs in MDA in an attempt to find the most dependable miRNA or their combinations to understand their tumorigenesis pathway and to identify diagnostic or prognostic biomarkers. We also investigated the association between those miRNAs and their target genes, especially Notch1 and Notch2. We evaluated miRNA expression profiles via miRNA microarray and validated them using.real-time PCR assays with 24 formalin-fixed, paraffin-embedded tissue blocks of MDA and 11 normal proliferative endocervical tissues as control. Expression for Notch1 and 2 was assessed by immunohistochemistry. MiRNA-135a-3p, 192-5p, 194-5p, and 494 were up-regulated, whereas miR-34b-5p, 204-5p, 299-5p, 424-5p, and 136-3p were down-regulated in MDA compared with normal proliferative endocervical tissues (all P <0.05). Considering the second-order Akaike Information Criterion consisting of likelihood ratio and number of parameters, miR-34b-5p showed the best discrimination power among the nine candidate miRNAs. A combined panel of miR-34b-5p and 194-5p was the best fit model to discriminate between MDA and control, revealing 100% sensitivity and specificity. Notch1 and Notch2, respective target genes of miR-34b-5p and miR-204-5p, were more frequently expressed in MDA than in control (63% vs. 18%; 52% vs. 18%, respectively, P <0.05). MiR-34b-5p expression level was higher in Notch1-negative samples compared with Notch1-positive ones (P <0.05). Down-regulated miR-494 was associated with poor patient survival (P = 0.036). MDA showed distinctive expression profiles of miRNAs, Notch1, and Notch2 from normal proliferative endocervical tissues. In particular, miR-34b-5p and 194-5p might be used as diagnostic biomarkers and miR-494 as a prognostic predictor for MDA. The miR-34b-5p/Notch1 pathway as well as Notch2 might be important oncogenic contributors to MDA.

The hedgehog (Hh) signaling pathway is known to play a critical role in various malignancies, but its clinicopathologic role in breast cancer is yet to be established. Tissue microarray blocks from 334 cases of breast cancer were prepared. The expression of six Hh signaling proteins including sonic hedgehog (Shh), patched (Ptch), smoothened (Smo), and the glioma-associated oncogene (Gli)-1, Gli-2, and Gli-3 were analyzed immunohistochemically. The expression of Hh signaling proteins was significantly correlated with some prognostic factors including the correlation of lymph node metastasis with the expression of Shh (p=0.001) and Ptch (p=0.064), the correlation of the stages with Shh and Gli-3 expression (p=0.007 and p=0.024, respectively), the correlation of the nuclear grade with the Smo (p=0.004) and Gli-3 (p=0.000), and the correlation of the histologic grade with the Ptch (p=0.016), Smo (p=0.007), and Gli-3 (p=0.000). The Shh, Ptch, Smo, Gli-1, and Gli-2 expression was significantly different between the phenotypes (p=0.000, p=0.001, p=0.004, p=0.039, and p=0.031, respectively). Gli-2 expression was correlated with a worse overall survival outcome (p=0.012). Hh pathway activation is correlated with a more aggressive clinical behavior in breast carcinomas. The comparison of phenotypes suggested that the Hh pathway may be a useful therapeutic target for breast carcinoma. Patients with Gli-2 expression had a significantly lower overall survival rate and, therefore, it showed promise as a prognostic marker.

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.