BACKGROUND: Lung adenocarcinoma is an important pathohistologic subtype of non-small cell lung cancer (NSCLC). Invasive non-mucinous pulmonary adenocarcinomas (INMA) tend to have a poor prognosis due to their significant heterogeneity and diverse histologic components. Establishing a histologic grading system for INMA is crucial for evaluating its malignancy. In 2021, the International Association for the Study of Lung Cancer (IASLC) proposed that a new histological grading system could better stratify the prognosis of INMA patients. The aim of this study was to establish a visualized nomogram model to predict INMA IASLC grading preoperatively by means of dual-energy computed tomography (DECT), fractal dimension (FD), clinical features and conventional CT parameters. METHODS: A total of 112 patients with INMA who underwent preoperative DECT were retrospectively enrolled from March 2021 to January 2025. Patients were categorized into low-intermediate grade and high grade groups based on IASLC grading. The clinical characteristics and conventional CT parameters, including baseline features, biochemical markers, and serum tumor markers, were collected. DECT-derived parameters, including iodine concentration (IC), effective atomic number (eff-Z), and normalized IC (NIC), were collected and determined as NIC ratio (NICr) and fractal dimension (FD). Univariate analysis was employed to compare differences in conventional characteristics and DECT parameters between the two groups. Variables demonstrating statistical significance were subsequently incorporated into a multivariate Logistic regression analysis. A nomogram model integrating clinical data, conventional CT parameters, and DECT parameters was developed to identify independent predictors for IASLC grading of INMA. The discriminatory performance of the model was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS: Multivariate analysis identified smoking history [odds ratio (OR)=2.848, P=0.041], lobulation sign (OR=2.163, P=0.004), air bronchogram (OR=7.833, P=0.005), eff-Z in arterial phase (OR=4.266, P<0.001), and IC in arterial phase (OR=1.290, P=0.012) as independent and significant predictors for IASLC grading of INMA. The nomogram model constructed based on these indicators demonstrated optimal predictive performance, achieving an area under the curve (AUC) of 0.804 (95%CI: 0.725-0.883), with specificity and sensitivity of 85.3% and 65.7%, respectively. CONCLUSIONS The nomogram model based on clinical features, imaging features and spectral CT parameters have a large potential for application in the preoperative noninvasive assessment of INMA IASLC grading.
Humans ; Nomograms ; Female ; Male ; Middle Aged ; Tomography, X-Ray Computed/methods* ; Lung Neoplasms/pathology* ; Aged ; Retrospective Studies ; Adenocarcinoma of Lung/pathology* ; Neoplasm Grading ; Adult

OBJECTIVE: The aim of this study is to investigate the risk factors of prognosis in patients with early-onset prostate cancer treated without surgery. A nomogram will be constructed and validated to predict overall survival (OS) of patients with early-onset prostate cancer treated without surgery. METHODS: The clinical data was obtained from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database on prostate cancer patients aged 18－55 years who were treated without surgery between 2010 and 2015. The clinical data set was divided into training set and validation set according to 7∶3 ratio, including age, race, marital status, Gleason score, prostate specific antigen (PSA) and other 8 factors. And significant variables were screened by univariate Cox regression analysis. Multivariate Cox regression analysis was used to identify the influence factors. Stepwise regression method was used to select the most influential factors on the total OS, and R software was used to build a nomogram model. The accuracy and prediction ability of the model were verified by drawing receiver operating characteristic (ROC) and Calibration Plot. The clinical benefit of the model was evaluated by decision curve analysis (DCA). RESULTS: A total of 8 212 patients who met the criteria were randomly assigned to the training set (n=5 752) or validation set (n=2 460), with no statistical difference between the two groups (all P>0.05). Six factors were identified through univariate and multivariate Cox regression analysis including marital status, N stage, M stage, radiotherapy, PSA and Gleason score, which were most closely associated with the OS of prostate cancer patients, and a column graph model was constructed based on these factors. The Consistency index (C-index) of the model in the training set and the verification set were 0.802 and 0.794, respectively. And the apparent diffusion coefficient (AUC) was 0.851, 0.855 and 0.855 for training sets 1, 3 and 5 years, and 0.694, 0.860 and 0.832 for verification sets 1, 3 and 5 years. The calibration chart showed a good agreement between the predicted and actual values of the model. In the analysis of decision curve, the model showed good clinical application value. CONCLUSION The prediction model based on marital status, radiotherapy, M stage, N stage, PSA and Gleason score for early-onset prostate cancer patients without surgical treatment has certain reference value which is expected to become an effective tool for clinicians to treat in future prospective studies on large and multi-center samples.
Humans ; Male ; Prostatic Neoplasms/diagnosis* ; Middle Aged ; Nomograms ; SEER Program ; Prognosis ; Adult ; Prostate-Specific Antigen ; Risk Factors ; Proportional Hazards Models ; Neoplasm Grading ; ROC Curve

OBJECTIVES: Renal cell carcinoma (RCC) is a malignant renal tumor that poses a significant threat to patient health. Accurate preoperative pathological grading plays a crucial role in determining the appropriate treatment for this disease. Currently, deep learning technology has become an important method for pathological grading of RCC. However, existing methods primarily rely on single-phase computed tomography (CT) imaging for analysis and prediction, which has limitations such as missing small lesions, one-sided evaluation, and local focusing issues. Therefore, this study proposes a multi-modal deep learning algorithm that integrates multi-phase enhanced CT images with clinical variable data, aiming to provide a basis for predicting the pathological grading of RCC. METHODS: First, the algorithm took four-phase enhanced CT images from the plain scan, arterial phase, venous phase, and delayed phase, along with clinical variables, as inputs. Then, an embedding encoding module was used to extract heterogeneous information from the clinical variables, and a 3-dimensional (3D) ResNet50 model was employed to capture spatial information from the multi-phase enhanced CT image data. Finally, a Fusion module deeply integrated the feature information from clinical variables and each phase's CT image features, further utilizing a cross-self-attention mechanism to achieve multi-phase feature fusion. This approach comprehensively captures the deep semantic information from the patient data, fully leveraging the complementary advantages of multi-modal and multi-phase data. To validate the effectiveness of the proposed method, a total of 1 229 RCC patients were approved by ethics review were included to train the model. RESULTS: Experimental results demonstrated superior performance compared to traditional radiomics and state-of-the-art deep learning methods, achieving an accuracy of 83.87%, a recall rate of 95.04%, and an F1-score of 82.23%. CONCLUSIONS The proposed algorithm exhibits strong stability and sensitivity, significantly enhancing the predictive performance of RCC pathological grading. It offers a novel approach for accurate RCC diagnosis and personalized treatment planning.
Humans ; Carcinoma, Renal Cell/pathology* ; Deep Learning ; Kidney Neoplasms/diagnostic imaging* ; Tomography, X-Ray Computed/methods* ; Algorithms ; Neoplasm Grading ; Male ; Female ; Middle Aged

An enduring debate in research revolves around the association between elevated endogenous testosterone levels and prostate cancer. This systematic review is intended to assess the present understanding of the role of endogenous testosterone in the diagnosis and treatment of low- and intermediate-risk prostate cancer. Our search strategy was the following: (endogenous testosterone) AND (((low risk) OR (intermediate risk)) AND ((diagnosis) OR (treatment))) AND (prostate cancer); that was applied to PubMed, Web of Science, and Scopus databases to identify pertinent articles. Two investigators performed an independent selection following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The preliminary investigation detected 105 records, and 81 records remained after eliminating duplicates. Following the review of titles and abstracts, 71 articles were excluded. A comprehensive examination of the full text was conducted for 10 articles, excluding 3 of them. After revising the references of eligible articles, other 3 articles were included. We finally identified 10 suitable studies, including three main topics: (1) association between endogenous testosterone and European Association of Urology (EAU) risk classes; (2) association between endogenous testosterone density and the tumor load; and (3) association of endogenous testosterone with tumor upgrading and tumor upstaging. Actual literature about the impact of endogenous testosterone on low- and intermediate-risk prostate cancer is not numerous, but appears to be still conflicting. More investigations are needed to increase the consistency of the literature's results.
Humans ; Male ; Prostatic Neoplasms/metabolism* ; Testosterone/metabolism* ; Risk Factors ; Neoplasm Grading ; Tumor Burden

A crucial aspect of prostate cancer grading, especially in low- and intermediate-risk cancer, is the accurate identification of Gleason pattern 4 glands, which includes ill-formed or fused glands. However, there is notable inconsistency among pathologists in recognizing these glands, especially when mixed with pattern 3 glands. This inconsistency has significant implications for patient management and treatment decisions. Conversely, the recognition of glomeruloid and cribriform architecture has shown higher reproducibility. Cribriform architecture, in particular, has been linked to the worst prognosis among pattern 4 subtypes. Intraductal carcinoma of the prostate (IDC-P) is also associated with high-grade cancer and poor prognosis. Accurate identification, classification, and tumor size evaluation by pathologists are vital for determining patient treatment. This review emphasizes the importance of prostate cancer grading, highlighting challenges like distinguishing between pattern 3 and pattern 4 and the prognostic implications of cribriform architecture and intraductal proliferations. It also addresses the inherent grading limitations due to interobserver variability and explores the potential of computational pathology to enhance pathologist accuracy and consistency.
Humans ; Prostatic Neoplasms/pathology* ; Male ; Neoplasm Grading ; Prognosis ; Observer Variation ; Prostate/pathology* ; Reproducibility of Results

The long-term survival outcomes of radical prostatectomy (RP) in Chinese prostate cancer (PCa) patients are poorly understood. We conducted a single-center, retrospective analysis of patients undergoing RP to study the prognostic value of pathological and surgical information. From April 1998 to February 2022, 782 patients undergoing RP at Queen Mary Hospital of The University of Hong Kong (Hong Kong, China) were included in our study. Multivariable Cox regression analysis and Kaplan-Meier analysis with stratification were performed. The 5-year, 10-year, and 15-year overall survival (OS) rates were 96.6%, 86.8%, and 70.6%, respectively, while the 5-year, 10-year, and 15-year PCa-specific survival (PSS) rates were 99.7%, 98.6%, and 97.8%, respectively. Surgical International Society of Urological Pathology PCa grades (ISUP Grade Group) ≥4 was significantly associated with poorer PSS (hazard ratio [HR] = 8.52, 95% confidence interval [CI]: 1.42-51.25, P = 0.02). Pathological T3 stage was not significantly associated with PSS or OS in our cohort. Lymph node invasion and extracapsular extension might be associated with worse PSS (HR = 20.30, 95% CI: 1.22-336.38, P = 0.04; and HR = 7.29, 95% CI: 1.22-43.64, P = 0.03, respectively). Different surgical approaches (open, laparoscopic, or robotic-assisted) had similar outcomes in terms of PSS and OS. In conclusion, we report the longest timespan follow-up of Chinese PCa patients after RP with different approaches.
Male ; Humans ; Retrospective Studies ; Prostatic Neoplasms/pathology* ; Prostate/pathology* ; Prostatectomy ; Prognosis ; Neoplasm Grading

Clinical staging, Gleason score, and prostate-specific antigen (PSA) have been accepted as factors for evaluating the prognosis of prostate cancer (PCa). With the in-depth study of iron metabolism and the development of multiparametric magnetic resonance imaging technology, we used q-Dixon magnetic resonance imaging (MRI) to measure the iron content of the PCa patients' lesions, and used enzyme-linked immunosorbent assay (ELISA) to measure the iron metabolism indicators in the patients' serum samples, combined with the patients' postoperative clinical data for analysis. We found that the serum indexes were correlated with the T2 star values, International Society of Urological Pathology (ISUP) grade, and pathological classification in PCa patients (all P < 0.001) but not in benign prostatic hyperplasia (BPH) patients (all P > 0.05). The utilization of q-Dixon-based MRI and serum indexes allows the noninvasive measurement of iron content in prostate lesions and the assessment of differential iron metabolism between PCa and BPH, which may be helpful for evaluating the prognosis of PCa.
Male ; Humans ; Prostatic Hyperplasia/pathology* ; Prostate-Specific Antigen ; Prostatic Neoplasms/pathology* ; Prostate/pathology* ; Neoplasm Grading ; Magnetic Resonance Imaging/methods* ; Iron

We performed this study to investigate pathological upgrading from biopsy to prostatectomy and clinicopathological factors associated with grade group (GG) upgrading in patients with International Society of Urological Pathology (ISUP) GG 1 and 2 prostate cancer (PCa) in a Chinese cohort. We included patients diagnosed with PCa with ISUP GG 1 and 2 at biopsy, who underwent RP at our institution. Pre- and postoperative clinical variables were examined. Univariate and multivariate logistic regression analyses were conducted to identify independent factors associated with GG upgrading. Patients in GG upgraded group had higher total prostate-specific antigen (tPSA; median: 14.43 ng ml^-1 vs 10.52 ng ml^-1, P = 0.001) and PSA density (PSAD; median: 0.45 ng ml^-2 vs 0.27 ng ml^-2, P < 0.001) than those in GG nonupgraded group. Patients in upgraded group had a higher ratio for Prostate Imaging-Reporting and Data System (PI-RADS) score >3 (86.4% vs 67.9%, P < 0.001). Those with GG 1 in biopsy were more likely to experience GG upgrading after RP than those with GG 2 (71 vs 54, P = 0.016). Independent preoperative factors predicting GG upgrading were PI-RADS score >3 (odds ratio [OR]: 2.471, 95% confidence interval [CI]: 1.132-5.393; P = 0.023), higher PSAD (P = 0.001), and GG in biopsy (OR: 0.241, 95% CI: 0.123-0.471; P < 0.001). The histopathological analyses of RP specimens revealed that perineural invasion (PNI; OR: 1.839, 95% CI: 1.027-3.490; P = 0.041) was identified as an independent factor associated with GG upgrading. Our results revealed that GG in the biopsy, PSAD, PI-RADS score >3, and PNI were independent factors of GG upgrading. These factors should be considered for patients with ISUP grade ≤2 PCa.
Biopsy ; Humans ; Magnetic Resonance Imaging ; Male ; Neoplasm Grading ; Prostatectomy ; Prostatic Neoplasms ; Retrospective Studies

Objective: To investigate the correlation between clinicopathological features and Miller/Payne (MP) grading system of breast carcinoma after neoadjuvant treatment and to establish novel prediction models. Methods: A total of 1 053 cases of invasive breast carcinoma NOS that undertaken neoadjuvant treatment according to Guidelines of CSCO for Breast Cancer were selected at the Key Laboratory of Carcinogenesis and Translational Research, Peking University Cancer Hospital & Institute from September 2016 to September 2019, and the clinical, pathologic data, MP grading and immunohistochemical staining were evaluated. Statistical analysis was conducted using R software. Several novel computer models on prediction of MP grading were established and validated. Results: Among 1 053 patients who accepted neoadjuvant treatment, 316 patients (316/1 053, 30%) were evaluated as MP5 postoperatively, and 737 patients (737/1 053, 70%) did not meet MP5 level. MP5 had significant association with histological grade, ER and PR expression, HER2 status, Ki-67 index and molecular classification (P<0.05). Univariate/multivariate logistic regression analyses further showed that the above clinicopathological features were also independent influencing factors of MP5 grade; five-fold cross-validation was used to evaluate the performance of the models, and the sensitivity and specificity of different models were obtained. Conclusions: MP grading of invasive breast carcinoma NOS after neoadjuvant treatment is associated with high histological grade, negative ER and PR expression, HER2 positivity, high Ki-67 index and molecular classification, which are independent influence factors. GBM model recommended through comparison can provide some help for clinical diagnosis and treatment.
Breast Neoplasms/pathology* ; Female ; Humans ; Ki-67 Antigen/metabolism* ; Neoadjuvant Therapy ; Neoplasm Grading ; Receptor, ErbB-2/metabolism* ; Receptors, Estrogen/metabolism* ; Receptors, Progesterone/metabolism*

BACKGROUND: Various prediction tools have been developed to predict biochemical recurrence (BCR) after radical prostatectomy (RP); however, few of the previous prediction tools used serum prostate-specific antigen (PSA) nadir after RP and maximum tumor diameter (MTD) at the same time. In this study, a nomogram incorporating MTD and PSA nadir was developed to predict BCR-free survival (BCRFS). METHODS: A total of 337 patients who underwent RP between January 2010 and March 2017 were retrospectively enrolled in this study. The maximum diameter of the index lesion was measured on magnetic resonance imaging (MRI). Cox regression analysis was performed to evaluate independent predictors of BCR. A nomogram was subsequently developed for the prediction of BCRFS at 3 and 5 years after RP. Time-dependent receiver operating characteristic (ROC) curve and decision curve analyses were performed to identify the advantage of the new nomogram in comparison with the cancer of the prostate risk assessment post-surgical (CAPRA-S) score. RESULTS: A novel nomogram was developed to predict BCR by including PSA nadir, MTD, Gleason score, surgical margin (SM), and seminal vesicle invasion (SVI), considering these variables were significantly associated with BCR in both univariate and multivariate analyses (P < 0.05). In addition, a basic model including Gleason score, SM, and SVI was developed and used as a control to assess the incremental predictive power of the new model. The concordance index of our model was slightly higher than CAPRA-S model (0.76 vs. 0.70, P = 0.02) and it was significantly higher than that of the basic model (0.76 vs. 0.66, P = 0.001). Time-dependent ROC curve and decision curve analyses also demonstrated the advantages of the new nomogram. CONCLUSIONS PSA nadir after RP and MTD based on MRI before surgery are independent predictors of BCR. By incorporating PSA nadir and MTD into the conventional predictive model, our newly developed nomogram significantly improved the accuracy in predicting BCRFS after RP.
Humans ; Male ; Neoplasm Grading ; Neoplasm Recurrence, Local/surgery* ; Nomograms ; Prognosis ; Prostate-Specific Antigen ; Prostatectomy ; Prostatic Neoplasms/surgery* ; Retrospective Studies ; Seminal Vesicles