Journal of Cancer Research and Clinical Oncology最新文献

Purpose: This study assesses the reliability of deep learning models based on planar whole-body bone scintigraphy for diagnosing Skull base invasion (SBI) in nasopharyngeal carcinoma (NPC) patients.

Methods: In this multicenter study, a deep learning model was developed using data from one center with a 7:3 allocation to training and internal test sets, to diagnose SBI in patients newly diagnosed with NPC using planar whole-body bone scintigraphy. Patients were diagnosed based on a composite reference standard incorporating radiologic and follow-up data. Ten different convolutional neural network (CNN) models were applied to both whole-image and partial-image input modes to determine the optimal model for each analysis. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration, decision curve analysis (DCA), and compared with expert assessments by two nuclear medicine physicians.

Results: The best-performing model using partial-body input achieved AUCs of 0.80 (95% CI: 0.73, 0.86) in the internal test set, 0.84 (95% CI: 0.77, 0.91) in the external cohort, and 0.78 (95% CI: 0.73, 0.83) in the treatment test cohort. Calibration curves and DCA confirmed the models' excellent discrimination, calibration, and potential clinical utility across internal and external datasets. The AUCs of both nuclear medicine physicians were lower than those of the best-performing deep learning model in external test set (AUC: 0.75 vs. 0.77 vs. 0.84).

Conclusion: Deep learning models utilizing partial-body input from planar whole-body bone scintigraphy demonstrate high discriminatory power for diagnosing SBI in NPC patients, surpassing experienced nuclear medicine physicians.

Background: Surgical resection (SR) following transarterial chemoembolization (TACE) is a promising treatment for unresectable hepatocellular carcinoma (uHCC). However, biomarkers for the prediction of postoperative recurrence are needed.

Purpose: To develop and validate a model combining deep learning (DL) and clinical data for early recurrence (ER) in uHCC patients after TACE.

Methods: A total of 511 patients who received SR following TACE were assigned to derivation (n = 413) and validation (n = 98) cohorts. Deep learning features were taken from the largest tumor area in liver MRI. A nomogram using DL signatures and clinical data was made to forecast early recurrence risk in uHCC patients. Model performance was evaluated using area under the curve (AUC).

Results: A total of 2278 subsequences and 31,346 slices multiparametric MRI including contrast-enhanced T1WI, T2WI and DWI were input in the DL model simultaneously. Multivariable analysis identified three independent predictors for the development of the nomogram: tumor number (hazard ratio [HR]:3.42, 95% confidence interval [CI]: 2.75-4.31, P = 0.003), microvascular invasion (HR: 9.21, 6.24-32.14; P < 0.001), and DL scores (HR: 17.46, 95% CI: 12.94-23.57, P < 0.001). The AUC of the nomogram was 0.872 and 0.862 in two cohorts, significantly outperforming single-subsequence-based DL mode and clinical model (all, P < 0.001). The nomogram provided two risk strata for cumulative overall survival in two cohorts, showing significant statistical results (P < 0.001).

Conclusions: The DL-based nomogram is essential to identify patients with uHCC suitable for treatment with SR following TACE and may potentially benefit personalized decision-making.

Background: Gallbladder carcinoma is the most common malignant tumor of the biliary system, and has a poor overall prognosis. Poor prognosis in patients with gallbladder carcinoma is associated with the aggressive nature of the tumor, subtle clinical symptoms, ineffective adjuvant treatment, and lack of reliable biomarkers.

Purpose: Therefore, evaluating the prognostic factors of patients with gallbladder carcinoma can help improve diagnostic and treatment methods, allowing for tailored therapies that could benefit patient survival.

Methods: This article systematically reviews the factors affecting the prognosis of gallbladder carcinoma, with the aim of evaluating prognostic risk in patients.

Conclusion: A comprehensive and in-depth understanding of prognostic indicators affecting patient survival is helpful for assessing patient survival risk and formulating personalized treatment plans.

Purpose: The aim of this study was to determine whether preoperative body mass index (BMI) was associated with postoperative morbidity after laparoscopic liver resection (LLR) for hepatocellular carcinoma (HCC).

Methods: A total of three groups of patients were categorized based on preoperative BMI: low-BMI (≤ 18.4 kg/m²), normal-BMI (18.5-24.9 kg/m²) and high-BMI (≥ 25.0 kg/m²). Baseline clinicopathological characteristics, operative variables, and postoperative 30-day mortality and morbidity were recorded and compared among the three groups. The independent risk factors for postoperative morbidity, including surgical site infection (SSI), were identified using univariate and multivariate analyses.

Results: Among 226 included patients, 20 (8.8%), 122 (54%), and 84 (37.2%) patients had low, normal, and high BMI, respectively. There were no significant differences in postoperative 30-day mortality rates in patients with low BMI and high BMI compared with those with normal BMI (5% and 1.2% vs. 0%, P = 0.141 and P = 0.408, respectively). However, postoperative morbidity rates were significantly higher in patients with low BMI and high BMI compared to those with normal BMI (40% and 32.1% vs. 17.2%, P = 0.032 and P = 0.020, respectively). According to multivariate analysis, both low and high BMI were independent risk factors of increased postoperative morbidity (OR: 5.03, 95% CI: 1.02-25.6, P = 0.047, and OR: 4.53, 95% CI: 1.75-12.8, P = 0.003, respectively). Low and high BMI were also identified as independent risk factors of increased postoperative SSI rates (OR: 6.25, 95% CI: 1.60-23.8, P = 0.007, and OR: 2.89, 95% CI: 1.04-8.77, P = 0.047, respectively).

Conclusion: A higher incidence of postoperative morbidity including SSI after LLR for HCC was found in low-BMI and high-BMI patients compared to normal-BMI patients.

Clinical trials registration: Not applicable because this is a retrospective observational study.

Backgroud: Mediator complex subunit 19 (MED19), a member of the mediator complex, has been demonstrated to involve in tumorigenesis of hepatocellular carcinoma (HCC). However, the regulation mechanisms of MED19, the immune landscape linking MED19 to HCC and its predictive value of immunotherapy treatment in HCC are so far unknown.

Methods: Here, we analyzed data from The Cancer Genome Atlas and other databases to assess the expression of MED19 and its prognosis and therapeutical-targets impact in HCC.

Results: MED19 expression was upregulated in HCC tissues compared to non-tumorous liver tissues and that its upregulation was positively associated with advanced clinicopathology features. The multivariate analysis showed that MED19 was an independent predictor of outcome in HCC. In vitro experiments revealed that MED19 knockdown suppressed hepG2 cells proliferation, colony forming and invasion and induced apoptosis. Furthermore, MED19 inhibition resulted in G0/G1 phase arrest in hepG2 cells. We screened differentially expressed genes between low and high MED19 expression groups. Enrichment analyses showed that these genes were mainly linked to nuclear division and cell cycle. The pattern of tumor-infiltrating immune was demonstrated to be related with MED19 expression in HCC. TIDE analyses showed that patients in the low-expression group presented significantly better immunotherapy. Moreover, we developed a predicted model for HCC patient's prognosis. Receiver operating characteristic analyses revealed that this model processed a favorable performance in predicting the prognosis of HCC patients. Finally, a nomogram was built for predicting survival probability of individual HCC patient.

Conclusion: These findings suggest that MED19 as a novel biomarker that has significant association with immune landscape and immunotherapy response in HCC. The proposed prediction model composed of MED19 and pathological stage has a better role in determining prognosis and stratifying of HCC.

Background: Liver cancer is a significant cause of cancer-related mortality worldwide and requires tailored treatment strategies for different types. However, preoperative accurate diagnosis of the type presents a challenge. This study aims to develop an automatic diagnostic model based on multi-phase contrast-enhanced CT (CECT) images to distinguish between hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and normal individuals.

Methods: We designed a Hierarchical Long Short-Term Memory (H-LSTM) model, whose core components consist of a shared image feature extractor across phases, an internal LSTM for each phase, and an external LSTM across phases. The internal LSTM aggregates features from different layers of 2D CECT images, while the external LSTM aggregates features across different phases. H-LSTM can handle incomplete phases and varying numbers of CECT image layers, making it suitable for real-world decision support scenarios. Additionally, we applied phase augmentation techniques to process multi-phase CECT images, improving the model's robustness.

Results: The H-LSTM model achieved an overall average AUROC of 0.93 (0.90, 1.00) on the test dataset, with AUROC for HCC classification reaching 0.97 (0.93, 1.00) and for ICC classification reaching 0.90 (0.78, 1.00). Comprehensive validation in scenarios with incomplete phases was performed, with the H-LSTM model consistently achieving AUROC values over 0.9.

Conclusion: The proposed H-LSTM model can be employed for classification tasks involving incomplete phases of CECT images in real-world scenarios, demonstrating high performance. This highlights the potential of AI-assisted systems in achieving accurate diagnosis and treatment of liver cancer. H-LSTM offers an effective solution for processing multi-phase data and provides practical value for clinical diagnostics.

Background: Hepatitis B virus (HBV)-related hepatocellular carcinoma (HBV-HCC) has poor prognosis and high mortality rate. Euphorbia helioscopia L. (EHL) is a classic Chinese medicinal herb.

Aim: This study aimed to evaluate in vitro anti-HBV-HCC properties of EHL, and explore it targets in HBV-HCC based on molecular docking.

Methods: The anti-tumor effect of EHL on HBV-HCC was evaluated using the cell viability, migration, invasion, and apoptosis of Hep 3B2.1-7 and HepG2.2.15 cells. Next, network pharmacological analysis was performed to predicted the key targets of EHL against HBV-HCC. Then the prognostic targets, including RAC-alpha serine/threonine-protein kinase (AKT1) and Caspase-3 (CASP3), were verified using molecular docking and rescue experiments.

Results: EHL exhibited inhibitory effects on cell proliferation/migration/invasion and induced cell apoptosis. Network pharmacological analysis proposed 12 active compounds in EHL, which targeted 22 HBV-HCC-related genes. AKT1 and CASP3 were identified to be key targets for EHL against HBV-HCC. AKT1 and CASP3 had prognostic significance in liver cancer. Overexpression of AKT1 and caspase-3 inhibitor can counteract the EHL effect.

Conclusion: EHL can exert anticancer effects on HBV-HCC by inhibiting migration/invasion, and inducing apoptosis, which may be achieved through AKT1 and CASP3.

High-dose methotrexate (HDMTX) is used in the treatment of a range of adult and childhood cancers. Although HDMTX can provide effective anti-tumor activity with an acceptable safety profile for most patients, delayed methotrexate elimination (DME) develops in a minority of patients receiving HDMTX and may be accompanied by renal dysfunction and potentially life-threatening toxicity. A panel of European physicians with experience in the use of HDMTX as well as of glucarpidase convened to develop a series of consensus statements to provide practical guidance on the prevention and treatment of DME, including the use of glucarpidase. Robust implementation of supportive measures including hyperhydration and urine alkalinization emerged as critical in order to reduce the risk of DME with HDMTX treatment, with leucovorin rescue critical in reducing the risk of DME complications. Early recognition of DME is important to promptly implement appropriate treatment including, intensified hydration, high-dose leucovorin and, when appropriate, glucarpidase.

Objectives: This study aimed to compare the combination therapy of transarterial chemoembolization (TACE) and microwave ablation (MWA) with MWA alone in treating liver metastases from colorectal cancer (LMCRC).

Materials and methods: In this retrospective study, a total of 251 patients with unresectable and not to chemotherapy responding LMCRC were included. Group A consisted of 184 patients (104 male and 80 females; mean age: 64 ± 11.4 years) with 442 metastases who received a combination of TACE and MWA. A total of 67 patients (49 male and 18 females; mean age: 63.2 ± 11.8 years) with 173 metastases patients were included in group B, who received only MWA. Parameters assessed were local tumor progression (LTP), hepatic distant tumor progression (hDTP), hepatic progression-free survival (hPFS), and overall survival (OS).

Results: The rate of LTP was 4.9% in group A and 4.5% in group B (p-value: 0.062). The rate of hDTP was 71.7% and 83.6% for groups A and B (p-value: 0.81), respectively. The mean hPFS was 13.8 months (95% CI 10.9-16.8) for group A and 8.1 months (95% CI 6.1-10.1) for group B (p-value: 0.03). The median OS time for group A was 30 months (95% CI 26-34), with 1-, 2-, 3-, and 4-year OS rates of 84.2%, 61.1%, 40.8% and 31.3%, respectively. In group B however, the median OS time was 26 months (95% CI 18-34) with 1-, 2-, 3-, and 4-year OS rates of 82.3%, 53.2%, 34.6% and 28.2%, respectively (p-value: 0.67).

Conclusion: The combination therapy of TACE and MWA is superior to the monotherapy of MWA for LMCRC, especially regarding hDTP, hPFS and OS.