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

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

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

The lung is the second most common site of neuroendocrine tumors (NETs). Typical and atypical carcinoids are low-grade NETs of the lung. These rare tumors have received little attention and education is needed for treating physicians. The article describes the classifcation of lung NETs, the epidemiology and pathological characteristics. When lung NETs are diagnosed at an early stage, surgical intervention is often curative. For advanced lung NETs patients, different treatment methods including chemotherapy, somatostatin analogs, m-TOR inhibition, peptide receptor radioligand therapy, and biologic systemic therapy are discussed. The conclusions are generally extrapolated from the outcome of extra-pulmonary carcinoids. Prospective randomized well-designed trials are urgently needed to inform current recommendations on systemic treatment.
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Disease-Free Survival ; Drug Therapy ; methods ; Humans ; Lung ; drug effects ; radiation effects ; surgery ; Lung Neoplasms ; pathology ; surgery ; therapy ; Neoplasm Grading ; Neuroendocrine Tumors ; pathology ; surgery ; therapy ; Outcome Assessment (Health Care) ; Radiotherapy ; methods

In order to solve the pathological grading of hepatocellular carcinomas (HCC) which depends on biopsy or surgical pathology invasively, a quantitative analysis method based on radiomics signature was proposed for pathological grading of HCC in non-contrast magnetic resonance imaging (MRI) images. The MRI images were integrated to predict clinical outcomes using 328 radiomics features, quantifying tumour image intensity, shape and text, which are extracted from lesion by manual segmentation. Least absolute shrinkage and selection operator (LASSO) were used to select the most-predictive radiomics features for the pathological grading. A radiomics signature, a clinical model, and a combined model were built. The association between the radiomics signature and HCC grading was explored. This quantitative analysis method was validated in 170 consecutive patients (training dataset: = 125; validation dataset, = 45), and cross-validation with receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was employed as the prediction metric. Through the proposed method, AUC was 0.909 in training dataset and 0.800 in validation dataset, respectively. Overall, the prediction performances by radiomics features showed statistically significant correlations with pathological grading. The results showed that radiomics signature was developed to be a significant predictor for HCC pathological grading, which may serve as a noninvasive complementary tool for clinical doctors in determining the prognosis and therapeutic strategy for HCC.
Carcinoma, Hepatocellular ; diagnostic imaging ; Humans ; Liver Neoplasms ; diagnostic imaging ; Magnetic Resonance Imaging ; Neoplasm Grading ; methods ; ROC Curve

Previous studies investigating prostate cancer (PCa) features in younger men have reported conflicting findings. This study aimed to investigate pathologic outcomes and biochemical recurrence (BCR) status in younger men who underwent radical prostatectomy (RP) for PCa. Records of 2057 patients who underwent RP at Seoul National University Bundang Hospital (Seongnam, Korea) between 2006 and 2015 were reviewed; patients were divided according to age into the younger and older groups (men aged ≤50 and >50 years, respectively). Postoperative BCR status and functional outcomes and clinicopathologic features were compared between both groups. All analyses were repeated after propensity score matching. Younger men were more likely to have low-risk disease (P < 0.001), lower pathologic Gleason score (P < 0.001) and pathologic stages (P < 0.001) than older men. The pathologic Gleason score (P = 0.002) and rates of extracapsular extension (P = 0.004) were lower in younger men after propensity score matching. In multivariate analysis, age at RP was not an independent predictor of BCR-free survival after RP (P = 0.669). Moreover, at 1 year after RP, younger men with preoperative 5-item International Index of Erectile Function score ≥22 (n = 228) showed more favorable results for urinary continence (defined as nonuse of pads daily) (99.4% vs 95%, P = 0.009) and erections sufficient for vaginal intercourse (81.8% vs 55.5%, P = 0.001). Younger men had more favorable clinicopathologic features at RP than their older counterparts. Although age was not an independent predictor of BCR status outcome, younger men had better functional outcomes following RP.
Age Factors ; Disease-Free Survival ; Humans ; Male ; Middle Aged ; Neoplasm Grading ; Prognosis ; Propensity Score ; Prostatectomy/methods* ; Prostatic Neoplasms/surgery* ; Retrospective Studies ; Treatment Outcome

Our previous study found that plate factor-4 variant (CXCL4L1) was downregulated in the serum of patients with prostate cancer (PCa). The aim of the present study was to investigate the prognostic value of CXCL4L1 in PCa. In total, 213 PCa patients treated with radical prostatectomy were enrolled and peripheral blood samples of all patients were collected. Expression of serum CXCL4L1 in patients with different tumor stages and grades were measured by enzyme-linked immunosorbent assay (ELISA). The Kaplan-Meier method was applied to estimate the progression to castration-resistant prostate cancer (CRPC), metastasis, biochemical recurrence (BCR)-free survival, and overall survival (OS). Prognostic factors for BCR-free survival and OS were determined by univariate and multivariate analyses using the Cox proportional hazards regression model. The expression of CXCL4L1 was significantly lower in PCa patients with advanced pathological tumor stage, high-grade Gleason score, and metastasis. Moreover, downregulation of CXCL4L1 not only strongly correlated with aggressive clinicopathological features, but also predicted tumor progression and unfavorable outcomes. Finally, multivariate Cox regression analyses identified CXCL4L1 as an independent prognostic factor for both BCR-free survival (hazard ratio [HR]: 2.03, 95% confidence interval [CI]: 1.26-3.27; P = 0.004) and OS (HR: 2.26, 95% CI: 1.07-4.79; P = 0.033). In conclusion, our results indicate that CXCL4L1 might serve as a novel and promising prognostic biomarker for patients with PCa and potential therapeutic target in the future.
Adenocarcinoma/surgery* ; Aged ; Disease Progression ; Disease-Free Survival ; Down-Regulation ; Humans ; Male ; Middle Aged ; Neoplasm Grading ; Platelet Factor 4/blood* ; Prognosis ; Prostate/surgery* ; Prostatectomy/methods* ; Prostatic Neoplasms/surgery* ; Survival Rate

Risk prediction models including the Prostate Health Index (phi) for prostate cancer have been well established and evaluated in the Western population. The aim of this study is to build phi-based risk calculators in a prostate biopsy population and evaluate their performance in predicting prostate cancer (PCa) and high-grade PCa (Gleason score ≥7) in the Chinese population. We developed risk calculators based on 635 men who underwent initial prostate biopsy. Then, we validated the performance of prostate-specific antigen (PSA), phi, and the risk calculators in an additional observational cohort of 1045 men. We observed that the phi-based risk calculators (risk calculators 2 and 4) outperformed the PSA-based risk calculator for predicting PCa and high-grade PCa in the training cohort. In the validation study, the area under the receiver operating characteristic curve (AUC) for risk calculators 2 and 4 reached 0.91 and 0.92, respectively, for predicting PCa and high-grade PCa, respectively; the AUC values were better than those for risk calculator 1 (PSA-based model with an AUC of 0.81 and 0.82, respectively) (all P < 0.001). Such superiority was also observed in the stratified population with PSA ranging from 2.0 ng ml^-1to 10.0 ng ml^-1. Decision curves confirmed that a considerable proportion of unnecessary biopsies could be avoided while applying phi-based risk calculators. In this study, we showed that, compared to risk calculators without phi, phi-based risk calculators exhibited superior discrimination and calibration for PCa in the Chinese biopsy population. Applying these risk calculators also considerably reduced the number of unnecessary biopsies for PCa.
Aged ; Asian People/statistics & numerical data* ; Biopsy ; China ; Humans ; Male ; Neoplasm Grading ; Prostate/pathology* ; Prostate-Specific Antigen/blood* ; Prostatic Neoplasms/pathology* ; Risk Assessment/methods*

This study compared the diagnostic efficacy of transrectal ultrasound (TRUS)-guided prostate biopsy (TRBx) and transperineal prostate biopsy (TPBx) in patients with suspected prostate cancer (PCa). We enrolled 2962 men who underwent transrectal (n = 1216) or transperineal (n = 1746) systematic 12-core prostate biopsy. Clinical data including age, prostate-specific antigen (PSA) level, and prostate volume (PV) were recorded. To minimize confounding, we performed propensity score-matching analysis. We measured and compared PCa detection rates between TRBx and TPBx, which were stratified by clinical characteristics and Gleason scores. The effects of clinical characteristics on PCa detection rate were assessed by logistic regression. For all patients, TPBx detected a higher proportion of clinically significant PCa (P < 0.001). Logistic regression analyses illustrated that PV had a smaller impact on PCa detection rate of TPBx compared with TRBx. Propensity score-matching analysis showed that the detection rates in TRBx were higher than those in TPBx for patients aged >- 80 years (80.4% vs 56.5%, P = 0.004) and with PSA level 20.1-100.0 ng ml^-1 (80.8% vs 69.1%, P = 0.040). In conclusion, TPBx was associated with a higher detection rate of clinically significant PCa than TRBx was; however, because of the high detection rate at certain ages and PSA levels, biopsy approaches should be optimized according to patents' clinical characteristics.
Adult ; Age Factors ; Aged ; Aged, 80 and over ; Biopsy/methods* ; Humans ; Logistic Models ; Male ; Middle Aged ; Neoplasm Grading ; Perineum ; Propensity Score ; Prostate/pathology* ; Prostate-Specific Antigen/blood* ; Prostatic Neoplasms/pathology* ; Rectum

PURPOSE: The purpose of this study is to compare the radiation therapy (RT) and radical prostatectomy (RP) of high-risk or locally advanced prostate cancer (PC) patients after neoadjuvant hormonal therapy (NHT). MATERIALS AND METHODS: This retrospective study evaluated patients underwent RT (42 patients) or RP (152 patients) after NHT at a single center during 2003–2014. Times to biochemical recurrence (BCR), pelvic local recurrence (PLR), metastasis, clinical painful symptom progression (CPSP), castration-resistant PC (CRPC), and overall survival were compared between the RT and RP groups, after adjustment for TN stage, using the Kaplan-Meier method and log-rank test. RESULTS: Significant inter-group differences were observed for age, Gleason score, initial PSA, and clinical and pathological T stages (all p < 0.05). During a median follow-up of 71.7 months, the overall incidences of BCR, PLR, metastasis, CPSP, CRPC, and death were 49.5%, 16.5%, 8.3%, 7.7%, 7.7%, and 17.5%, respectively. The median times to BCR were 100 months for RT and 36.2 months for RP (p=0.004), although the median times were not reached for the other outcomes (all p>0.05). The independent predictor of CPSP was RP (hazard ratio, 0.291; p=0.013). CONCLUSIONS: Despite significantly different baseline parameters, RP provided better CPSP-free survival than RT among patients with localized high-risk or locally advanced PC.
Follow-Up Studies ; Humans ; Incidence ; Methods ; Neoplasm Grading ; Neoplasm Metastasis ; Prostate ; Prostatectomy ; Prostatic Neoplasms ; Radiotherapy ; Recurrence ; Retrospective Studies ; Risk Factors