BMC Cancer最新文献

Background: Accurate and comprehensive preoperative staging is one of the most important prognostic factors for the management of esophageal cancer (EC). We aimed to develop and validate predictive models using radiomics from preoperative contrast-enhanced Computed Tomography (CT) images to assess pathological staging in EC patients.

Methods: This study retrospectively included 161 patients who underwent esophagectomy at Sichuan Cancer Hospital from July 2018 to February 2023. Pathological staging outcomes encompassed overall TNM staging, T and N staging, and tumor progressions (vascular invasion and perineural invasion). Radiomics features were extracted from segmented regions of tumors. A radiomic signature (Rad-signature) for each outcome was developed using a fivefold cross-validation least absolute shrinkage and selection operator (LASSO) regression model within the training cohort and subsequently validated in the test cohort for predictive accuracy.

Results: Out of the 851 radiomics features extracted, two were selected to formulate the Rad-signature for each staging outcome. These signatures showed a significant correlation with their respective outcomes in both the training set and the testing set. Furthermore, the Rad-signature exhibited favorable predictive performance for advanced pTNM staging, advanced pT staging, vascular invasion and perineural invasion, with AUC of 0.721 [95%CI, 0.570-0.872], 0.900 [95%CI 0.805-0.995], 0.824 [0.686-0.961], and 0.737 [0.586-0.887], respectively. However, the predictive performance of the Rad-signature for pN staging is moderate (AUC = 0.693 [0.534-0.852]), indicating needs for additional data modalities.

Conclusions: This study established a non-invasive preoperative radiomics model that demonstrated good predictive performance in determining the pTNM staging, pT staging, vascular invasion, and perineural invasion for EC patients. These results could inform personalized treatment strategies and improve outcomes for EC patients.

Background: Metastatic or recurrent bone sarcomas are often associated with an unfavorable prognosis, posing a formidable challenge in extending patients' survival. Currently, regorafenib has shown promise in treating metastatic and recurrent bone sarcomas. However, there is a lack of consensus on its efficacy and safety. This systematic review and meta-analysis aims to consolidate existing data to assess the efficacy and safety of regorafenib in bone sarcomas.

Methods: A comprehensive search strategy utilizing MeSH terms and free-text keywords was employed to systematically search the Embase, PubMed, Web of Science, and Cochrane databases up to May 26, 2024. Randomized controlled trials investigating regorafenib monotherapy for metastatic or recurrent bone sarcomas were included. The primary outcomes of interest were progression-free survival (PFS), overall survival(OS) and adverse events (AEs).

Results: We retrieved 335 articles and included 5 of them. Regorafenib significantly extended PFS-3 months and PFS-6 months in patients with metastatic or recurrent bone sarcomas compared to the control group, exhibiting a favorable odds ratio (OR) of 2.04 (95% CI: 1.21-2.86, P < 0.01) and 1.03 (95% CI: 0.08-1.99, P < 0.05), respectively. However, regorafenib did not improve OS at any observation point compared with the control group(P > 0.05), and the frequency of AEs was higher, with an odds ratio of 1.35 (95% CI: 0.63-2.07, P < 0.01).

Conclusion: Regorafenib emerges as a promising therapeutic option for metastatic or recurrent bone sarcomas, demonstrating certain clinical benefits alongside manageable adverse reactions. Nevertheless, further research is warranted to refine the efficacy and safety profile of regorafenib, particularly in exploring safe dosage ranges or alternative treatment modalities.

Registration number: CRD42024551705.

Background: The introduction of (m)FOLFIRINOX and gemcitabine-nab-paclitaxel has changed the perspective for patients with locally advanced pancreatic cancer (LAPC). Consequently, in experienced centres 23% of patients with LAPC undergo a resection with 5-year overall survival (OS) rates of up to 25%. In the Netherlands, the nationwide resection rate for LAPC remains low at 8%. The PREOPANC-4 program aims for a nationwide implementation of the international multidisciplinary best-practice to improve patient outcome.

Methods: Nationwide program implementing the international multidisciplinary best-practice for LAPC. In the training phase, multidisciplinary and surgical webinars are given by 4 international experts, leading to a clinical protocol, followed by surgical off-site and on-site proctoring sessions. In the implementation phase, the clinical protocol will be implemented in all centres, including a nationwide expert panel (2022-2024). Healthcare professionals will be trained in shared decision-making. Consecutive patients diagnosed with pathology-proven LAPC (i.e., arterial involvement > 90° and/or portomesenteric venous > 270° involvement or occlusion [DPCG criteria]) are eligible. Primary outcomes are median and 5-year OS from diagnosis, resection rate, in-hospital/30-day mortality and major morbidity (i.e., Clavien-Dindo grade ≥ IIIa), and radical resection (R0) rate. Secondary outcomes include quality of life, functioning, side effects, and patients' healthcare satisfaction in all included patients. Outcomes will be compared with patients with borderline resectable pancreatic cancer (BRPC) treated with neoadjuvant FOLFIRINOX in the PREOPANC-2 trial (EudraCT: 2017-002036-17) and a historical cohort of patients with LAPC from the PACAP registry (NCT03513705). The existing prospective LAPC Registry and PACAP PROMs (NCT03513705) will be used for data collection. In qualitative interviews, treatment preferences, values, and experiences of LAPC patients, their relatives, and healthcare professionals will be assessed for the development of shared decision-making supportive tools. It is hypothesized that the program will double the nationwide LAPC resection rate to 16% with major morbidity < 50% and mortality ≤ 5%, and OS following resection similar to that observed in patients with BRPC.

Discussion: The PREOPANC-4 program aims to safely implement the international multidisciplinary best-practice for LAPC leading to benchmark outcomes for both short-term morbidity, mortality, and OS.

Trial registration: PREOPANC-4 program was registered at ClinicalTrials.gov (NCT05524090) on September 1, 2022.

In this study, the influence of glycoproteomic changes, specifically MUC16, on NK cell-mediated immunotherapy response in ovarian cancer is explored. Analysis of glycoprotein data from the CPTAC database identified significant upregulation of MUC16 in ovarian cancer tissues, associated with tumor invasiveness and immune evasion. Experimental findings showed that MUC16 knockdown increased NK cell cytotoxicity, decreased invasiveness, and boosted NK cell activation, while MUC16 overexpression resulted in the opposite effects. In vivo experiments demonstrated that MUC16 knockdown suppressed tumor growth, enhanced NK cell infiltration, and bolstered NK cell activation, underscoring the potential of MUC16 as a target for novel immunotherapy approaches in ovarian cancer treatment.

Background: Amino acid metabolism (AAM) reprogramming plays a crucial role in hepatocellular carcinoma (HCC), but its genetic pathophysiology was not fully elucidated. Therefore, we employed a summary data-based Mendelian randomization (SMR) approach to identify putative causal effects of the AAM-related genes on hepatitis B virus (HBV)-HCC survival via integrating multi-omics data.

Methods: Multivariate Cox proportional hazards regression models were used to evaluate associations between genetic variants of AAM-related genes and overall survival (OS) of HBV-HCC patients (n = 866). Next, we developed a pathway-specific genetic risk score (GRS) comprising variants in the AAM pathway. Subsequently, putative causal SNPs were prioritized using SMR by integrating HBV-HCC OS data with expression quantitative trait loci (eQTLs) and DNA methylation QTLs (mQTLs) from the blood, as well a eQTLs of liver tissues.

Results: We identified 23 independent variants associated with HBV-HCC OS, and the pathway-specific GRS derived from the identified variants was a significant predictor of HBV-HCC OS. The addition of the GRS significantly improved the predictive performance of the 5-year survival model (AUC increased from 72.04% to 84.67%, P < 0.001). By integrating HBV-HCC OS associated with the eQTLs and mQTLs from the blood, we identified a putative causal variant rs2074038 across HBV-HCC OS, ACCS expression, and DNA methylation. Furthermore, the integration of liver eQTL data revealed that increased expression levels of ACCS by rs2074038 were associated with a worse HBV-HCC OS. Mechanistically, bioinformatics annotation and luciferase reporter assays further demonstrated that rs2074038 contributes to HBV-HCC progression by allele-specific regulation of the ACCS expression.

Conclusions: This study identified rs2074038 as a novel functional SNP associated with poor HBV-HCC survival, likely mediated genetic regulation of ACCS expression. These findings suggest that ACCS is a potential therapeutic target and highlight the need for further validation in clinical settings.

Objective: This study aimed to evaluate the predictive value of implementing machine learning models based on ultrasound radiomics and clinicopathological features in the survival analysis of triple-negative breast cancer (TNBC) patients.

Methods and materials: All patients, including retrospective cohort (training cohort, n = 306; internal validation cohort, n = 77) and prospective external validation cohort (n = 82), were diagnosed as locoregional TNBC and underwent pre-intervention sonographic evaluation in this multi-center study. A thorough chart review was conducted for each patient to collect clinicopathological and sonographic features, and ultrasound radiomics features were obtained by PyRadiomics. Deep learning algorithms were utilized to delineate ROIs on ultrasound images. Radiomics analysis pipeline modules were developed for analyzing features. Radiomic scores, clinical scores, and combined nomograms were analyzed to predict 2-year, 3-year, and 5-year overall survival (OS) and disease-free survival (DFS). Receiver operating characteristic (ROC) curves, calibration curves, and decision curves were used to evaluate the prediction performance.

Findings: Both clinical and radiomic scores showed good performance for overall survival and disease-free survival prediction in internal (median AUC of 0.82 and 0.72 respectively) and external validation (median AUC of 0.70 and 0.74 respectively). The combined nomograms had AUCs of 0.80-0.93 and 0.73-0.89 in the internal and external validation, which had best predictive performance in all tasks (p < 0.05), especially for 5-year OS (p < 0.01). For the overall evaluation of six tasks, combined models obtained better performance than clinical and radiomic scores [AUCs of 0.83 (0.73,0.93), 0.81 (0.72,0.93), and 0.70 (0.61,0.85) respectively].

Interpretation: The combined nomograms based on pre-intervention ultrasound radiomics and clinicopathological features demonstrated exemplary performance in survival analysis. The new models may allow us to non-invasively classify TNBC patients with various disease outcome.

Background: Treatment planning systems (TPS) often exclude immobilization devices from optimization and calculation, potentially leading to inaccurate dose estimates. This study employed deep learning methods to automatically segment 3D-printed head and neck immobilization devices and evaluate their dosimetric impact in head and neck VMAT.

Methods: Computed tomography (CT) positioning images from 49 patients were used to train the Mask2Former model to segment 3D-printed headrests and MFIFs. Based on the results, four body structure sets were generated for each patient to evaluate the impact on dose distribution in volumetric modulated arc therapy (VMAT) plans: S (without immobilization devices), S_{_MF} (with MFIFs), S_{_3D} (with 3D-printed headrests), and S_{_3D+MF} (with both). VMAT plans (P, P_{_MF}, P_{_3D}, and P_{_3D+MF}) were created for each structure set. Dose-volume histogram (DVH) data and dose distribution of the four plans were compared to assess the impact of the 3D-printed headrests and MFIFs on target and normal tissue doses. Gafchromic EBT3 film measurements were used for patient-specific verification to validate dose calculation accuracy.

Results: The Mask2Former model achieved a mean average precision (mAP) of 0.898 and 0.895, with a Dice index of 0.956 and 0.939 for the 3D-printed headrest on the validation and test sets, respectively. For the MFIF, the Dice index was 0.980 and 0.981 on the validation and test sets, respectively. Compared to P, P_{_MF} reduced the V_100% for PGTVnx, PGTVnd, PGTVrpn, PTV1, and PTV2 by 5.99%, 6.51%, 5.93%, 2.24%, and 1.86%, respectively(P ≤ 0.004). P_{_3D} reduced the same targets by 1.78%, 2.56%, 1.75%, 1.16%, and 1.48%(P < 0.001), with a 31.3% increase in skin dose (P < 0.001). P_{_3D+MF} reduced the V_100% by 9.15%, 10.18%, 9.16%, 3.36%, and 3.28% (P < 0.001), respectively, while increasing the skin dose by 31.6% (P < 0.001). EBT3 film measurements showed that the P_{_3D+MF} dose distribution was more aligned with actual measurements, achieving a mean gamma pass rate of 92.14% under the 3%/3 mm criteria.

Conclusions: This study highlights the potential of Mask2Former in 3D-printed headrest and MFIF segmentation automation, providing a novel approach to enhance personalized radiation therapy plan accuracy. The attenuation effects of 3D-printed headrests and MFIFs reduce V_100% and D_mean for PTVs in head and neck cancer patients, while the buildup effects of 3D-printed headrests increases the skin dose (31.3%). Challenges such as segmentation inaccuracies for small targets and artifacts from metal fasteners in MFIFs highlight the need for model optimization and validation on larger, more diverse datasets.

Background: Differences in overall survival (OS) between pediatric and adult patients diagnosed with osteosarcoma are poorly understood. The objective of this study is to compare the OS of pediatric and adult patients with osteosarcoma, identify prognostic factors associated with OS, and explore factors specifically associated with pediatric osteosarcoma using data gathered from the National Cancer Database (NCDB).

Methods: Patients > = 1 years old and diagnosed with osteosarcoma between 2004 and 2017 were included in the study. Multivariable Cox regression analysis adjusted for gender, race, income, education, place of living, health insurance status, year of diagnosis, stage of cancer, surgery, chemotherapy, and radiation therapy (RT) was used to assess the association of age with the OS of the patients.

Results: The analysis included 8,458 patients among whom 3,027 (35.8%) were between 1 and 17 years old. In the multivariable Cox regression analysis, adult patients had worse OS compared with pediatric patients (HR: 1.84; p < .01). When stratified by treatment type, pediatric patients had better OS in several groups. This includes those who received chemotherapy alone (HR: 0.58, p < .01), surgery alone (HR: 0.48, p < .01), surgery plus chemotherapy (HR: 0.56, p < .01), and those who received no treatment (HR: 0.31, p < .01). There was no significant difference in OS between pediatric and adult patients receiving a combination of chemotherapy, surgery, and RT (HR: 0.81, p = .42). In analysis stratified by cancer stage, pediatric patients had better OS compared to adult patients at each stage. Multivariable logistic regression analysis revealed that pediatric patients are more likely to be non-white, have insurance, present with unknown/occult stage disease, have poorly differentiated tumors, and receive chemotherapy, or surgery. Additionally, multivariable Cox regression analysis identified factors associated with improved OS: age, diagnosis between 2011 and 2015, private insurance, non-metastatic disease, well-differentiated tumors, and receiving chemotherapy or surgery, but not RT.

Conclusion: Pediatric patients diagnosed with osteosarcoma had better OS compared to their adult counterparts. Pediatric patients had better OS compared to adults when the analysis was stratified by treatment modality and stage of cancer. Further research is necessary to elucidate the underlying reason for this difference.