BMC Cancer最新文献

Background: Predicting radiation dermatitis (RD), a common radiotherapy toxicity, is essential for clinical decision-making regarding toxicity management. This prospective study aimed to develop and validate a machine-learning model to predict the occurrence of grade ≥ 2 RD using thermal imaging in the early stages of radiotherapy in head and neck cancer.

Methods: Thermal images of neck skin surfaces were acquired weekly during radiotherapy. A total of 202 thermal images were used to calculate the difference map of neck skin temperature and analyze to extract thermal imaging features. Changes in imaging features during treatment were assessed in the two RD groups, grade ≥ 2 and grade ≤ 1 RD, classified according to the Common Terminology Criteria for Adverse Events (CTCAE) guidelines. Feature importance analysis was performed to select thermal imaging features correlated with grade ≥ 2 RD. A predictive model for grade ≥ 2 RD occurrence was developed using a machine learning algorithm and cross-validated. Area under the receiver-operating characteristic curve (AUC), precision, and sensitivity were used as evaluation metrics.

Results: Of the 202 thermal images, 54 images taken before the occurrence of grade ≥ 2 RD were used to develop the predictive model. Thermal radiomics features related to the homogeneity of image texture were selected as input features of the machine learning model. The gradient boosting decision tree showed an AUC of 0.84, precision of 0.70, and sensitivity of 0.75 in models trained using thermal features acquired before skin dose < 10 Gy. The support vector machine achieved a mean AUC of 0.71, precision of 0.68, and sensitivity of 0.70 for predicting grade ≥ 2 RD using thermal images obtained in the skin dose range of 10-20 Gy.

Conclusions: Thermal images acquired from patients undergoing radiotherapy for head and neck cancer can be used as an early predictor of grade ≥ 2 RD and may aid in decision support for the management of acute skin toxicity from radiotherapy. However, our results should be interpreted with caution, given the limitations of this study.

Background: The discovery of cellular tumor networks in glioblastoma, with routes of malignant communication extending far beyond the detectable tumor margins, has highlighted the potential of supramarginal resection strategies. Retrospective data suggest that these approaches may improve long-term disease control. However, their application is limited by the proximity of critical brain regions and vasculature, posing challenges for validation in randomized trials. Anterior temporal lobectomy (ATL) is a standardized surgical procedure commonly performed in patients with pharmacoresistant temporal lobe epilepsy. Translating the ATL approach from epilepsy surgery to the neuro-oncological field may provide a model for investigating supramarginal resection in glioblastomas located in the anterior temporal lobe.

Methods: The ATLAS/NOA-29 trial is a prospective, multicenter, multinational, phase III randomized controlled trial designed to compare ATL with standard gross-total resection (GTR) in patients with newly-diagnosed anterior temporal lobe glioblastoma. The primary endpoint is overall survival (OS), with superiority defined by significant improvements in OS and non-inferiority in the co-primary endpoint, quality of life (QoL; "global health" domain of the European organization for research and treatment of cancer (EORTC) QLQ-C30 questionnaire). Secondary endpoints include progression-free survival (PFS), seizure outcomes, neurocognitive performance, and the longitudinal assessment of six selected domains from the EORTC QLQ-C30 and BN20 questionnaires. Randomization will be performed intraoperatively upon receipt of the fresh frozen section result. A total of 178 patients will be randomized in a 1:1 ratio over a 3-year recruitment period and followed-up for a minimum of 3 years. The trial will be supervised by a Data Safety Monitoring Board, with an interim safety analysis planned after the recruitment of the 57th patient to assess potential differences in modified Rankin Scale (mRS) scores between the treatment arms 6 months after resection. Assuming a median improvement in OS from 17 to 27.5 months, the trial is powered at > 80% to detect OS differences with a two-sided log-rank test at a 5% significance level.

Discussion: The ATLAS/NOA-29 trial aims to determine whether ATL provides superior outcomes at equal patients' Qol compared to GTR in anterior temporal lobe glioblastoma, potentially establishing ATL as the surgical approach of choice for isolated temporal glioblastoma and redefining the standard of care for this patient population.

Trial registration: German Clinical Trials Register (DRKS00035314), registered on October 18, 2024.

Background: Bladder cancer (BLCA) exists a profound molecular diversity, with basal and luminal subtypes having different prognostic and therapeutic outcomes. Traditional methods for molecular subtyping are often time-consuming and resource-intensive. This study aims to develop machine learning models using deep learning features from hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) to predict basal and luminal subtypes in BLCA.

Methods: RNA sequencing data and clinical outcomes were downloaded from seven public BLCA databases, including TCGA, GEO datasets, and the IMvigor210C cohort, to assess the prognostic value of BLCA molecular subtypes. WSIs from TCGA were used to construct and validate the machine learning models, while WSIs from Shanghai Tenth People's Hospital (STPH) and The Affiliated Guangdong Second Provincial General Hospital of Jinan University (GD2H) were used as external validations. Deep learning models were trained to obtained tumor patches within WSIs. WSI level deep learning features were extracted from tumor patches based on the RetCCL model. Support vector machine (SVM), random forest (RF), and logistic regression (LR) were developed using these features to classify basal and luminal subtypes.

Results: Kaplan-Meier survival and prognostic meta-analyses showed that basal BLCA patients had significantly worse overall survival compared to luminal BLCA patients (hazard ratio = 1.47, 95% confidence interval: 1.25-1.73, P < 0.001). The LR model based on tumor patch features selected by Resnet50 model demonstrated superior performance, achieving an area under the curve (AUC) of 0.88 in the internal validation set, and 0.81 and 0.64 in the external validation sets from STPH and GD2H, respectively. This model outperformed both junior and senior pathologists in the differentiation of basal and luminal subtypes (AUC: 0.85, accuracy: 74%, sensitivity: 66%, specificity: 82%).

Conclusions: This study showed the efficacy of machine learning models in predicting the basal and luminal subtypes of BLCA based on the extraction of deep learning features from tumor patches in H&E-stained WSIs. The performance of the LR model suggests that the integration of AI tools into the diagnostic process could significantly enhance the accuracy of molecular subtyping, thereby potentially informing personalized treatment strategies for BLCA patients.

Background: Previous genome-wide association studies have linked three missense single nucleotide polymorphisms (SNPs) in C1q/TNF-related protein 6 (CTRP6) to diseases such as type 1 diabetes and autoimmune diseases. However, the potential association of newly identified missense CTRP6 variants with diseases, especially cancer, remains unclear.

Methods: We used several pathogenicity prediction algorithms to identify deleterious mutations within the highly conserved C1q domain of human CTRP6, following the retrieval of all SNPs from the Ensembl database. We systematically analyzed the effects of these mutations on the protein's stability, flexibility, structural conformation, compactness, stiffness, and overall functionality using various bioinformatics tools. Additionally, we investigated the association of these mutations with different cancer types using the cBioPortal and canSAR databases.

Results: We identified 11 detrimental missense SNPs within the C1q domain, a region critical for this protein's functionality. Using various computational methods, we predicted the functional impact of these missense variants and assessed their effects on the stability and flexibility of the CTRP6 structure. Molecular dynamics simulations revealed significant structural differences between the native and mutated structures, including changes in structural conformation, compactness, solvent accessibility, and flexibility. Additionally, our study shows a strong association between two mutations, G181S and R247W, and certain types of cancer: colon adenocarcinoma and uterine corpus endometrial carcinoma, respectively. We also found that the mutational status of CTRP6 and other cancer-related genes, such as MAP2K3, p16, TP53, and JAK1, affected each other's expression, potentially contributing to cancer development.

Conclusions: Our screening and predictive analysis of pathogenic missense variants in CTRP6 advance the understanding of the functional implications of these mutations, potentially facilitating more focused and efficient research in the future.

Background: Functional polymorphisms in the MDM2 promoters have been linked to cancer risk and several non-malignant conditions. Their potential role in bone marrow function during chemotherapy is largely unknown.

Methods: We investigated the potential associations between genotypes of MDM2 SNP309 (rs2279744), SNP285 (rs117039649) and del1518 (rs3730485) and neutrophil counts in breast cancer patients receiving neoadjuvant sequential epirubicin and docetaxel, with additional G-CSF, in the DDP-trial (NCT00496795). We applied longitudinal ratios, post vs. pre-treatment, of neutrophil counts as our main measure. Differences by genotypes were tested by Jonckheere-Terpstra test for ranked alternatives, while dominant and recessive models were tested by Mann-Whitney U test, and additional sub-analyses were performed for genotype combinations.

Results: The SNP309 reference T-allele was associated with a better sustained neutrophil count (p = 0.035). A similar association was observed for the alternative del-allele of the del1518 (p = 0.049). Additionally, in combined genotype-analyses, patients with the SNP309 TT genotype and at least one copy of the del1518 del-allele had particularly favorable sustained neutrophil counts during chemotherapy treatment (p = 0.005).

Conclusions: Our study provides evidence that MDM2 promoter polymorphisms may be associated with neutrophil counts and bone marrow recovery during chemotherapy treatment in breast cancer patients.

Trial registration: The DDP-trial was registered at ClinicalTrials.gov (NCT00496795; registration date 2007-07-04).

Background: Immune checkpoint inhibitors (ICIs) therapy has revolutionized anti-cancer therapy, with lung cancer exhibiting sustained clinical responses to it. However, there remains a lack of research into the risk factors of serious infections in patients with lung cancer following ICIs therapy. Therefore, we aimed to investigate the incidence and risk factors of serious infections in these patients.

Methods: Medical records were retrospectively collected and reviewed from 710 patients with lung cancer receiving ICIs therapy at Zhongshan Hospital between January 2021 and February 2023. Serious infections were defined as infections requiring hospitalization or parenteral antimicrobials occurring at any time from the initiation of the ICIs therapy to 3 months after its discontinuation.

Results: Among the study population, 191 patients had suffered from serious infections, with an overall infection rate of 26.90% during an average follow-up period of (432.62 ± 377.09) days. The predominant site of infection was the lung (75.61%), and the most prevalent pathogens were bacteria (85.07%), followed by Mycobacterium tuberculosis (6.47%), viruses (4.98%), and fungi (3.48%). In addition to chronic obstructive pulmonary disease (COPD), asthma, and systemic glucocorticoids use, low lymphocyte count and CD4/CD8 ratio were identified as independent risk factors (all p < 0.05).

Conclusion: Laboratory parameters may serve as strong predictors for serious infections in patients with lung cancer following ICIs therapy. Chronic airway diseases including COPD and asthma should be managed effectively. Systemic glucocorticoids should be used prudently to prevent serious infections in these patients.

Objectives: To explore the potential of serum exosomal miRNAs as novel biomarkers for pancreatic ductal adenocarcinoma (PDAC).

Methods: Serum exosomal miRNAs were screened and verified by microarray analysis and quantitative real-time PCR (qRT-PCR) in patients with PDAC and healthy controls. The correlation between the clinical characteristics of PDAC and candidate exosomal miRNAs was analyzed, and the diagnostic performance of the candidate biomarkers was evaluated.

Results: Serum exosomal miR-7977 and miR-451a were significantly upregulated in PDAC patients compared with healthy controls, and the levels of miR-7977 and miR-451a in serum exosomes were closely associated with the clinical stage and metastasis of PDAC patients. The area under curve (AUC) values of serum exosomal miR-7977 and miR-451a for PDAC were 0.825 and 0.804 in the training set and 0.796 and 0.830 in the validation set, respectively. A biomarker panel consisting of these two miRNAs resulted in a diagnostic power with an AUC of 0.901 in the training set and 0.918 in the validation set.

Conclusions: Serum exosomal miR-7977 and miR-451a might be diagnostic biomarkers for PDAC. These two miRNAs, when combined, exhibit optimal diagnostic performance.

Background: Radiomic models combining intratumoral with peritumoral features are potentially beneficial to enhance the predictive performance. This study aimed to identify the optimal ¹⁸F-FDG PET/CT-derived radiomic models for prediction of prognosis in hepatocellular carcinoma (HCC).

Methods: A total of 135 HCC patients from two institutions were retrospectively included. Four peritumoral regions were defined by dilating tumor region with thicknesses of 2 mm, 4 mm, 6 mm, and 8 mm, respectively. Based on segmentation of intratumoral, peritumoral and integrated volume of interest (VOI), corresponding radiomic features were extracted respectively. After feature selection, a total of 15 intratumoral radiomic models were constructed based on five ensemble learning algorithms and radiomic features from three image modalities. Then, the optimal combination of ensemble learning algorithms and image modality in the intratumoral models was selected to develop subsequent peritumoral radiomic models and integrated radiomic models. Finally, a nomogram was developed incorporating the optimal radiomic model with clinical independent predictors to achieve an intuitive representation of the prediction model.

Results: Among the intratumoral radiomic models, the one which combined PET/CT-based radiomic features with SVM classifier outperformed other models. With the addition of peritumoral information, the integrated model based on an integration of intratumoral and 2 mm-peritumoral VOI, was finally approved as the optimal radiomic model with a mean AUC of 0.831 in the internal validation, and a highest AUC of 0.839 (95%CI:0.718-0.960) in the external test. Furthermore, a nomogram incorporating the optimal radiomic model with HBV infection and TNM status, was able to predict the prognosis for HCC with an AUC of 0.889 (95%CI: 0.799-0.979).

Conclusions: The integrated intratumoral and peritumoral radiomic model, especially for a 2 mm peritumoral region, was verified as the optimal radiomic model to predict the overall survival of HCC. Furthermore, combination of integrated radiomic model with significant clinical parameter contributed to further enhance the prediction efficacy.

Trial registration: This study was a retrospective study, so it was free from registration.

Background: The therapeutic value of thermal ablation (TA) versus repeat hepatic resection (RHR) for recurrent hepatocellular carcinoma (rHCC) after initial hepatic resection is uncertain. This study aimed to investigate the prognosis of TA and RHR.

Materials and methods: In this multicenter real-world retrospective study, 473 patients were enrolled between January 2015 and August 2023, with 340 in the TA group and 133 in the RHR group. Propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) were employed to reduce selection bias. Local tumor progression (LTP), recurrence-free survival (RFS), and post-recurrence survival (PRS) were compared before and after PSM and IPTW.

Results: A total of 473 patients (231 aged ≥ 60 years; 393 men) were evaluated. LTP, RFS, and PRS rates did not differ significantly between groups before (P = 0.940, P = 0.180, and P = 0.700) and after matching (P = 0.420, P = 0.680, and P = 0.810) and weighting (P = 0.940, P = 0.180, and P = 0.700). Multivariable Cox analysis identified tumor number (HR: 2.28; P < 0.001) and PLT (HR: 0.73; P = 0.038) as independent prognostic factors for RFS in the entire rHCC cohort. And tumor location, size, number, ascites, AST, and AFP (HR: 0.55-2.18; P = 0.004-0.046) were independent prognostic factors for PRS. Subgroup analysis showed both TA and RHR were effective treatments for rHCC, regardless of tumor size, number, subcapsular, or perivascular lesions.

Conclusions: The cumulative LTP, RFS, and PRS were not significantly different between TA and RHR for rHCC within the Milan criteria. TA may be a viable curative option for early-stage rHCC patients.

Background: Bladder cancer (BLCA) is notably associated with advanced age, characterized by its high incidence and mortality among the elderly. Despite promising advancements in models that amalgamate molecular subtypes with treatment and prognostic outcomes, the considerable heterogeneity in BLCA poses challenges to their universal applicability. Consequently, there is an urgent need to develop a new molecular subtyping system focusing on a critical clinical feature of BLCA: senescence.

Methods: Utilizing unsupervised clustering on the Cancer Genome Atlas Program (TCGA)-BLCA cohort, we crafted a senescence-associated molecular classification and precision quantification system (Senescore). This method underwent systematic validation against established molecular subtypes, treatment strategies, clinical outcomes, the immune tumor microenvironment (TME), relevance to immune checkpoints, and identification of potential therapeutic targets.

Results: External validations were conducted using the Xiangya cohort, IMvigor210 cohort, and meta-cohort, with multiplex immunofluorescence confirming the correlation between Senescore, immune infiltration, and cellular senescence. Notably, patients categorized within higher Senescore group were predisposed to the basal subtype, showcased augmented immune infiltration, harbored elevated driver gene mutations, and exhibited increased senescence-associated secretory phenotype (SASP) factors expression in the transcriptome. Despite poorer prognoses, these patients revealed greater responsiveness to immunotherapy and neoadjuvant chemotherapy.

Conclusions: Our molecular subtyping and Senescore, informed by age-related clinical features, accurately depict age-associated biological traits and its clinical application potential in BLCA. Moreover, this personalized assessment framework is poised to identify senolysis targets unique to BLCA, furthering the integration of aging research into therapeutic strategies.