Pyrroline-5-carboxylate reductase (PYCR) is pivotal in converting pyrroline-5-carboxylate (P5C) to proline, the final step in proline synthesis. Three isoforms, PYCR1, PYCR2, and PYCR3, existed and played significant regulatory roles in tumor initiation and progression. In this study, we first assessed the molecular and immune characteristics of PYCRs by a pan-cancer analysis, especially focusing on their prognostic relevance. Then, a kidney renal clear cell carcinoma (KIRC)-specific prognostic model was established, incorporating pathomics features to enhance predictive capabilities. The biological functions and regulatory mechanisms of PYCR1 and PYCR2 were investigated by in vitro experiments in renal cancer cells. The PYCRs\' expressions were elevated in diverse tumors, correlating with unfavorable clinical outcomes. PYCRs were enriched in cancer signaling pathways, significantly correlating with immune cell infiltration, tumor mutation burden (TMB), and microsatellite instability (MSI). In KIRC, a prognostic model based on PYCR1 and PYCR2 was independently validated statistically. Leveraging features from H&E-stained images, a pathomics feature model reliably predicted patient prognosis. In vitro experiments demonstrated that PYCR1 and PYCR2 enhanced the proliferation and migration of renal carcinoma cells by activating the mTOR pathway, at least in part. This study underscores PYCRs\' pivotal role in various tumors, positioning them as potential prognostic biomarkers and therapeutic targets, particularly in malignancies like KIRC. The findings emphasize the need for a broader exploration of PYCRs\' implications in pan-cancer contexts.

The purpose of this investigation is to develop and initially assess a quantitative image analysis scheme that utilizes histopathological images to predict the treatment effectiveness of bevacizumab therapy in ovarian cancer patients. As a widely accessible diagnostic tool, histopathological slides contain copious information regarding underlying tumor progression that is associated with tumor prognosis. However, this information cannot be readily identified by conventional visual examination. This study utilizes novel pathomics technology to quantify this meaningful information for treatment effectiveness prediction. Accordingly, a total of 9828 features were extracted from segmented tumor tissue, cell nuclei, and cell cytoplasm, which were categorized into geometric, intensity, texture, and subcellular structure features. Next, the best performing features were selected as the input for SVM (support vector machine)-based prediction models. These models were evaluated on an open dataset containing a total of 78 patients and 288 whole slides images. The results indicated that the sufficiently optimized, best-performing model yielded an area under the receiver operating characteristic (ROC) curve of 0.8312. When examining the best model\'s confusion matrix, 37 and 25 cases were correctly predicted as responders and non-responders, respectively, achieving an overall accuracy of 0.7848. This investigation initially validated the feasibility of utilizing pathomics techniques to predict tumor responses to chemotherapy at an early stage.

UNASSIGNED: Pathomics has emerged as a promising biomarker that could facilitate personalized immunotherapy in lung cancer. It is essential to elucidate the global research trends and emerging prospects in this domain.
UNASSIGNED: The annual distribution, journals, authors, countries, institutions, and keywords of articles published between 2018 and 2023 were visualized and analyzed using CiteSpace and other bibliometric tools.
UNASSIGNED: A total of 109 relevant articles or reviews were included, demonstrating an overall upward trend; The terms \"deep learning\", \"tumor microenvironment\", \"biomarkers\", \"image analysis\", \"immunotherapy\", and \"survival prediction\", etc. are hot keywords in this field.
UNASSIGNED: In future research endeavors, advanced methodologies involving artificial intelligence and pathomics will be deployed for the digital analysis of tumor tissues and the tumor microenvironment in lung cancer patients, leveraging histopathological tissue sections. Through the integration of comprehensive multi-omics data, this strategy aims to enhance the depth of assessment, characterization, and understanding of the tumor microenvironment, thereby elucidating a broader spectrum of tumor features. Consequently, the development of a multimodal fusion model will ensue, enabling precise evaluation of personalized immunotherapy efficacy and prognosis for lung cancer patients, potentially establishing a pivotal frontier in this domain of investigation.

UNASSIGNED: This study aimed to develop quantitative feature-based models from histopathological images to assess aurora kinase A (AURKA) expression and predict the prognosis of patients with lung adenocarcinoma (LUAD).
UNASSIGNED: A dataset of patients with LUAD was derived from the cancer genome atlas (TCGA) with information on clinical characteristics, RNA sequencing and histopathological images. The TCGA-LUAD cohort was randomly divided into training (n = 229) and testing (n = 98) sets. We extracted quantitative image features from histopathological slides of patients with LUAD using computational approaches, constructed a predictive model for AURKA expression in the training set, and estimated their predictive performance in the test set. A Cox proportional hazards model was used to assess whether the pathomic scores (PS) generated by the model independently predicted LUAD survival.
UNASSIGNED: High AURKA expression was an independent risk factor for overall survival (OS) in patients with LUAD (hazard ratio = 1.816, 95 % confidence intervals = 1.257-2.623, P = 0.001). The model based on histopathological image features had significant predictive value for AURKA expression: the area under the curve of the receiver operating characteristic curve in the training set and validation set was 0.809 and 0.739, respectively. Decision curve analysis showed that the model had clinical utility. Patients with high PS and low PS had different survival rates (P = 0.019). Multivariate analysis suggested that PS was an independent prognostic factor for LUAD (hazard ratio = 1.615, 95 % confidence intervals = 1.071-2.438, P = 0.022).
UNASSIGNED: Pathomics models based on machine learning can accurately predict AURKA expression and the PS generated by the model can predict LUAD prognosis.

OBJECTIVE: Radical surgery, the first-line treatment for patients with hepatocellular cancer (HCC), faces the dilemma of high early recurrence rates and the inability to predict effectively. We aim to develop and validate a multimodal model combining clinical, radiomics, and pathomics features to predict the risk of early recurrence.
METHODS: We recruited HCC patients who underwent radical surgery and collected their preoperative clinical information, enhanced computed tomography (CT) images, and whole slide images (WSI) of hematoxylin and eosin (H & E) stained biopsy sections. After feature screening analysis, independent clinical, radiomics, and pathomics features closely associated with early recurrence were identified. Next, we built 16 models using four combination data composed of three type features, four machine learning algorithms, and 5-fold cross-validation to assess the performance and predictive power of the comparative models.
RESULTS: Between January 2016 and December 2020, we recruited 107 HCC patients, of whom 45.8% (49/107) experienced early recurrence. After analysis, we identified two clinical features, two radiomics features, and three pathomics features associated with early recurrence. Multimodal machine learning models showed better predictive performance than bimodal models. Moreover, the SVM algorithm showed the best prediction results among the multimodal models. The average area under the curve (AUC), accuracy (ACC), sensitivity, and specificity were 0.863, 0.784, 0.731, and 0.826, respectively. Finally, we constructed a comprehensive nomogram using clinical features, a radiomics score and a pathomics score to provide a reference for predicting the risk of early recurrence.
CONCLUSIONS: The multimodal models can be used as a primary tool for oncologists to predict the risk of early recurrence after radical HCC surgery, which will help optimize and personalize treatment strategies.

BACKGROUND: Papillary thyroid carcinoma (PTC) is globally prevalent and associated with an increased risk of lymph node metastasis (LNM). The role of cancer-associated fibroblasts (CAFs) in PTC remains unclear.
METHODS: We collected postoperative pathological hematoxylin-eosin (HE) slides from 984 included patients with PTC to analyze the density of CAF infiltration at the invasive front of the tumor using QuPath software. The relationship between CAF density and LNM was assessed. Single-cell RNA sequencing (scRNA-seq) data from GSE193581 and GSE184362 datasets were integrated to analyze CAF infiltration in PTC. A comprehensive suite of in vitro experiments, encompassing EdU labeling, wound scratch assays, Transwell assays, and flow cytometry, were conducted to elucidate the regulatory role of CD36+CAF in two PTC cell lines, TPC1 and K1.
RESULTS: A significant correlation was observed between high fibrosis density at the invasive front of the tumor and LNM. Analysis of scRNA-seq data revealed metastasis-associated myoCAFs with robust intercellular interactions. A diagnostic model based on metastasis-associated myoCAF genes was established and refined through deep learning methods. CD36 positive expression in CAFs can significantly promote the proliferation, migration, and invasion abilities of PTC cells, while inhibiting the apoptosis of PTC cells.
CONCLUSIONS: This study addresses the significant issue of LNM risk in PTC. Analysis of postoperative HE pathological slides from a substantial patient cohort reveals a notable association between high fibrosis density at the invasive front of the tumor and LNM. Integration of scRNA-seq data comprehensively analyzes CAF infiltration in PTC, identifying metastasis-associated myoCAFs with strong intercellular interactions. In vitro experimental results indicate that CD36 positive expression in CAFs plays a promoting role in the progression of PTC. Overall, these findings provide crucial insights into the function of CAF subset in PTC metastasis.

UNASSIGNED: TNFRSF4 plays a significant role in cancer progression, especially in hepatocellular carcinoma (HCC). This study aims to investigate the prognostic value of TNFRSF4 expression in patients with HCC and to develop a predictive pathomics model for its expression.
UNASSIGNED: A cohort of patients with HCC retrieved from the TCGA database was analyzed using RNA-seq analysis to determine TNFRSF4 expression and its impact on overall survival (OS). Additionally, hematoxylin-eosin staining analysis was performed to construct a pathomics model for predicting TNFRSF4 expression. Then, pathway enrichment analysis was conducted, immune checkpoint markers were investigated, and immune cell infiltration was examined to explore the underlying biological mechanism of the pathomics score.
UNASSIGNED: TNFRSF4 expression was significantly higher in tumor tissues than in normal tissues. TNFRSF4 expression also exhibited significant correlations with various clinical variables, including pathologic stage III/IV and R1/R2/RX residual tumor. Furthermore, elevated TNFRSF4 expression was associated with unfavorable OS. Interestingly, in the subgroup analysis, elevated TNFRSF4 expression was identified as a significant risk factor for OS in male patients. The newly developed pathomics model successfully predicted TNFRSF4 expression with good performance and revealed a significant association between high pathomics scores and worse OS. In male patients, high pathomics scores were also associated with a higher risk of mortality. Moreover, pathomics scores were also involved in specific hallmarks, immune-related characteristics, and apoptosis-related genes in HCC, such as epithelial-mesenchymal transition, Tregs, and BAX expression.
UNASSIGNED: Our findings suggest that TNFRSF4 expression and the newly devised pathomics scores hold potential as prognostic markers for OS in patients with HCC. Additionally, gender influenced the association between these markers and patient outcomes.

OBJECTIVE: To develop and validate a pathomics signature for predicting the outcomes of Primary Central Nervous System Lymphoma (PCNSL).
METHODS: In this study, 132 whole-slide images (WSIs) of 114 patients with PCNSL were enrolled. Quantitative features of hematoxylin and eosin (H&E) stained slides were extracted using CellProfiler. A pathomics signature was established and validated. Cox regression analysis, receiver operating characteristic (ROC) curves, Calibration, decision curve analysis (DCA), and net reclassification improvement (NRI) were performed to assess the significance and performance.
RESULTS: In total, 802 features were extracted using a fully automated pipeline. Six machine-learning classifiers demonstrated high accuracy in distinguishing malignant neoplasms. The pathomics signature remained a significant factor of overall survival (OS) and progression-free survival (PFS) in the training cohort (OS: HR 7.423, p < 0.001; PFS: HR 2.143, p = 0.022) and independent validation cohort (OS: HR 4.204, p = 0.017; PFS: HR 3.243, p = 0.005). A significantly lower response rate to initial treatment was found in high Path-score group (19/35, 54.29%) as compared to patients in the low Path-score group (16/70, 22.86%; p < 0.001). The DCA and NRI analyses confirmed that the nomogram showed incremental performance compared with existing models. The ROC curve demonstrated a relatively sensitive and specific profile for the nomogram (1-, 2-, and 3-year AUC = 0.862, 0.932, and 0.927, respectively).
CONCLUSIONS: As a novel, non-invasive, and convenient approach, the newly developed pathomics signature is a powerful predictor of OS and PFS in PCNSL and might be a potential predictive indicator for therapeutic response.

UNASSIGNED: Patients with advanced prostate cancer (PCa) often develop castration-resistant PCa (CRPC) with poor prognosis. Prognostic information obtained from multiparametric magnetic resonance imaging (mpMRI) and histopathology specimens can be effectively utilized through artificial intelligence (AI) techniques. The objective of this study is to construct an AI-based CRPC progress prediction model by integrating multimodal data.
UNASSIGNED: Data from 399 patients diagnosed with PCa at three medical centers between January 2018 and January 2021 were collected retrospectively. We delineated regions of interest (ROIs) from 3 MRI sequences viz, T2WI, DWI, and ADC and utilized a cropping tool to extract the largest section of each ROI. We selected representative pathological hematoxylin and eosin (H&E) slides for deep-learning model training. A joint combined model nomogram was constructed. ROC curves and calibration curves were plotted to assess the predictive performance and goodness of fit of the model. We generated decision curve analysis (DCA) curves and Kaplan-Meier (KM) survival curves to evaluate the clinical net benefit of the model and its association with progression-free survival (PFS).
UNASSIGNED: The AUC of the machine learning (ML) model was 0.755. The best deep learning (DL) model for radiomics and pathomics was the ResNet-50 model, with an AUC of 0.768 and 0.752, respectively. The nomogram graph showed that DL model contributed the most, and the AUC for the combined model was 0.86. The calibration curves and DCA indicate that the combined model had a good calibration ability and net clinical benefit. The KM curve indicated that the model integrating multimodal data can guide patient prognosis and management strategies.
UNASSIGNED: The integration of multimodal data effectively improves the prediction of risk for the progression of PCa to CRPC.

OBJECTIVE: This retrospective observational study aims to develop and validate artificial intelligence (AI) pathomics models based on pathological Hematoxylin-Eosin (HE) slides and pathological immunohistochemistry (Ki67) slides for predicting the pathological staging of colorectal cancer. The goal is to enable AI-assisted accurate pathological staging, supporting healthcare professionals in making efficient and precise staging assessments.
METHODS: This study included a total of 267 colorectal cancer patients (training cohort: n = 213; testing cohort: n = 54). Logistic regression algorithms were used to construct the models. The HE image features were used to build the HE model, the Ki67 image features were used for the Ki67 model, and the combined model included features from both the HE and Ki67 images, as well as tumor markers (CEA, CA724, CA125, and CA242). The predictive results of the HE model, Ki67 model, and tumor markers were visualized through a nomogram. The models were evaluated using ROC curve analysis, and their clinical value was estimated using decision curve analysis (DCA).
RESULTS: A total of 260 deep learning features were extracted from HE or Ki67 images. The AUC for the HE model and Ki67 model in the training cohort was 0.885 and 0.890, and in the testing cohort, it was 0.703 and 0.767, respectively. The combined model and nomogram in the training cohort had AUC values of 0.907 and 0.926, and in the testing cohort, they had AUC values of 0.814 and 0.817. In clinical DCA, the net benefit of the Ki67 model was superior to the HE model. The combined model and nomogram showed significantly higher net benefits compared to the individual HE model or Ki67 model.
CONCLUSIONS: The combined model and nomogram, which integrate pathomics multi-modal data and clinical-pathological variables, demonstrated superior performance in distinguishing between Stage I-II and Stage III colorectal cancer. This provides valuable support for clinical decision-making and may improve treatment strategies and patient prognosis. Furthermore, the use of immunohistochemistry (Ki67) slides for pathomics modeling outperformed HE slide, offering new insights for future pathomics research.