BMC Cancer最新文献

Background: Financial toxicity from cancer treatments is rising as an important patient-reported outcome. Its relevance was first assessed in the context of privately financed healthcare system, where the financial hardship caused by out-of-pocket payments negatively affects survival, while fewer evidence exists on its role in countries where care is financed by the public health care system. Head and Neck Cancer (HNC) patients face an increased risk for financial toxicity due to multimodal treatment and relevant out of pocket costs. The aim of this study was to develop and validate an Italian version of the Canadian Financial Index of Toxicity (FIT) questionnaire, defined FITALY.

Methods: FIT questionnaire was translated through a forward-backward process by two investigators independently, and the process was reviewed by a certified medical scientific English native speaker. Once reached consensus upon Italian translation, two Health Economics experts were consulted to adapt the questionnaire to Italian socio-economic context. The FITALY questionnaire v1.0 hereby developed was anonymously administered to two consecutive groups of 30 patients who had received curative, multimodal treatment for HNC cancer at ASST Spedali Civili of Brescia, Italy. A cognitive debriefing form was simultaneously administered to ask patients to exclude recurring and redundant items and include new relevant items.

Results: The 14-item FITALY questionnaire provides a global evaluation of financial toxicity ranging from 0 to 100. The questionnaire is divided into 4 domains: financial burden (6 items), exploring the objective financial toxicity burden; financial distress (2 items), which refers to the psychological distress related to financial toxicity; out-of-pocket costs (4 items), which focus on medical expenses paid by the patient; and loss of productivity (2 items), that investigates the disease impact on both patient's and caregiver's job activity.

Conclusions: Starting from the Canadian 9-item FIT questionnaire, we developed and validated the Italian 14-item FITALY questionnaire. Prospective application to a cohort of Italian HNC patients is ongoing.

Background: Bone metastases (BM) represent a prevalent complication of tumors. Early and accurate diagnosis, however, is a significant hurdle for radiologists. Recently, artificial intelligence (AI) has emerged as a valuable tool to assist radiologists in the detection of BM. This meta-analysis was undertaken to evaluate the AI diagnostic accuracy for BM.

Methods: Two reviewers performed an exhaustive search of several databases, including Wei Pu (VIP) database, China National Knowledge Infrastructure (CNKI), Web of Science, Cochrane Library, Ovid-Embase, Ovid-Medline, Wan Fang database, and China Biology Medicine (CBM), from their inception to December 2024. This search focused on studies that developed and/or validated AI techniques for detecting BM in magnetic resonance imaging (MRI) or computed tomography (CT). A hierarchical model was used in the meta-analysis to calculate diagnostic odds ratio (DOR), negative likelihood ratio (NLR), positive likelihood ratio (PLR), area under the curve (AUC), specificity (SP), and pooled sensitivity (SE). The risk of bias and applicability were assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST), while the Transparent Reporting of a multivariable prediction model for individual prognosis or diagnosis-artificial intelligence (TRIPOD-AI) was employed for evaluating the quality of evidence.

Result: This review covered 20 articles, among them, 16 studies were included in the meta-analysis. The results revealed a pooled SE of 0.88 (0.82-0.92), a pooled SP of 0.89 (0.84-0.93), a pooled AUC of 0.95 (0.92-0.96), PLR of 8.1 (5.57-11.80), NLR of 0.14 (0.09-0.21) and DOR of 58 (31-109). When focusing on imaging algorithms. Based on ML, a pooled SE of 0.88 (0.77-0.92), SP 0.88 (0.82-0.92), and AUC 0.93 (0.91-0.95). Based on DL, a pooled SE of 0.89 (0.81-0.95), SP 0.89 (0.81-0.94), and AUC 0.95 (0.93-0.97).

Conclusion: This meta-analysis underscores the substantial diagnostic value of AI in identifying BM. Nevertheless, in-depth large-scale prospective research should be carried out for confirming AI's clinical utility in BM management.

Background: Mutations in human receptor tyrosine kinase epidermal growth factor receptor-2 (HER2) are rare. This study aimed to investigate the clinical characteristics and computed tomography (CT) texture features of lung adenocarcinoma (LUAD) patients with HER2 mutation.

Methods: This study included 933 LUAD patients from January 2018 to December 2023 and classified their CT textures accordingly.

Results: The data indicated that the incidence of HER2 mutation was higher in younger LUAD patients than in elder patients [7.5% (31/413) vs. 1.5% (8/520), p < 0.0001] and was associated with never-smokers [0% (0/78) vs. 4.6% (39/855), p = 0.03]. In this study, the tumors were categorized based on their diameter into T1a and ≥ T1b. The data revealed that HER2 mutation was more frequent in T1a than in ≥ T1b [11.0% (23/210) vs. 2.2% (16/723), p < 0.0001]. Furthermore, non-pSD was more common than pSD in LUAD with HER2 mutation than in LUAD with HER2 wild type [82.1% (31/39) vs.17.9% (7/39), p = 0.01]. Moreover, the size of pGGO (0.94 ± 0.29 cm vs. 1.24 ± 0.39 cm, p = 0.0009), mGGO (0.86 ± 0.39 cm vs. 1.5 ± 0.77 cm, p < 0.0001) and pSD (1.75 ± 0.81 cm vs. 2.5 ± 1.4 cm, p < 0.05) in LUAD patients with HER2 mutation was smaller than those with HER2 wild type patients. In addition, when LUADs with HER2 wild type transformed from pGGO to mGGO, their sizes increased significantly (1.50 ± 0.77 cm vs. 1.24 ± 0.39 cm). It was also observed that the incidence of LUAD with HER2 mutation of ≤ 1 cm was significantly more than that of > 1 cm comparing to that in LUAD with HER2 wild type [14.8% (12/81) vs. 1.7% (3/173), p < 0.0001].

Conclusion: This study indicated that the incidence of HER2 mutation was higher in younger and never-smoking LUAD patients. Furthermore, the growth of LUAD with HER2 mutation was slower than that of those with HER2 wild type. Moreover, most LUAD with HER2 mutation changed into pSD after > 1 cm.

Background: Effective management of patients with borderline ovarian tumor (BOT) requires the timely identification of those at a higher risk of recurrence. Artificial neural networks have been successfully used in many areas of clinical event prediction, significantly affecting clinical decisions and practice.

Objective: We developed and validated a novel clinical model based on neural multi-task logistic regression (N-MTLR) for predicting recurrence in patients with BOT who underwent initial surgeries, and compared its prediction performance with that of the Cox regression model.

Methods: This retrospective study included 736 patients diagnosed with BOT from May 2011 to August 2022, with 84 recurrences. The synthetic minority oversampling technique (SMOTE) was used to balance the minority group such that the two patient types were 1:1. Using random sampling, the SMOTE-balanced dataset was divided into 80% of the sample (1043 patients) as the training set and 20% (261 patients) as the validation set. Both N-MTLR and Cox regression models were trained on the training set using SMOTE and evaluated on the validation set using the time-dependent area under the receiver operating characteristic curve (tdAUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy.

Results: Among the 736 enrolled patients, only 84 (11.41%) were diagnosed with BOT recurrence. Using SMOTE, the balanced dataset (1304 patients) contained equal numbers of patients (652 patients) in both recurrence and non-recurrence groups. Multivariate Cox regression analysis of the training set revealed that independent risk factors for BOT recurrence were premenopause, laparoscopic surgery, tumor rupture, advanced clinical stage, undissected lymph nodes, bilateral tumors, and fertility-sparing surgery (FSS). The N-MTLR model was constructed by correlation screening of 34 features in the training set, and 10 variables were screened including FSS, completeness of surgery, comorbidities, International Federation of Gynecology and Obstetrics (FIGO) staging, age, omentectomy, lymphadenectomy, parity, menopausal status, and peritoneal implantation. The N-MTLR model outperformed the Cox regression model in terms of AUC, accuracy, specificity, PPV, and NPV at the quartiles of follow-ups (2, 4, and 7 years).

Conclusions: The N-MTLR model effectively predicts BOT recurrence. Identifying high-risk recurrence groups in patients with BOT can facilitate close monitoring, suitable treatment, and an opportune time for intervention.

Background: Tumour pseudocapsule shows plasticity, whose representative indicator- pseudocapsule thickness- was little studied in clear cell renal cell carcinoma (ccRCC).

Methods: We enrolled 1037 ccRCC patients who underwent surgery between 2006 and 2013 at our institution. The patients did not receive therapy before surgery and were confirmed to have a pathological pseudocapsule. The associations of pseudocapsule thickness with overall survival and disease-free survival were studied using multivariable Cox regression analyses. Patients were stratified using points of maximum separation. Influential factors were examined with logistic regression.

Results: The average thickness was 0.47 mm (median: 0.43, interquartile range: 0.28-0.6). The average follow-up was 92.1. In multivariable analyses, every 0.1-mm increase in thickness resulted in a decreased risk of death (hazard ratio (HR) 0.906, p = 0.011) but not recurrence (HR 0.948, p = 0.105). For patients with a pseudocapsule thickness of 0.2 mm, 0.4 mm, 0.6 mm, 0.8 mm and 1.0 mm, the estimated 10-year overall survival rates were 74.9%, 83.3%, 87.8%, 90.1% and 91.0%, and the 10-year disease-free survival rates were 69.6%, 76.6%, 80.8%, 83.1% and 84.1%, respectively, with the best cut-off value being approximately 0.37 mm. The results of logistic regression revealed that female sex (p < 0.001), age (p = 0.002), a higher neutrophil count (p = 0.011), large tumour size (p < 0.001) and necrosis (p = 0.011) were independently associated with a thin pseudocapsule (≤ 0.37 mm).

Conclusions: Pseudocapsule thickness is heterogeneous in clear cell renal cell carcinoma. Generally, increased thickness is associated with improvement in long-term survival. A pseudocapsule being 0.37 mm or thinner is mostly influenced by both systematic and tumor-related parameters.

Objective: Metabolic disorders are common in cancer patients. This study aimed to classify the metabolic disorder status of cancer patients using hematological indicators and to examine the association between disorder types and prognosis.

Methods: A cohort of 6307 patients from INSCOC was classified into three clusters via K-means clustering based on hematological indicators. Logistic regression and Cox models assessed each cluster's impact on adverse outcomes.

Results: A total of 6,307 participants were included in the study, K-means clustering divided the population into three groups, Cluster 1 (Normal Group, NG), Cluster 2 (Mild Disorder Group, MDG) and Cluster 3 (Severe Disorder Group, SDG). Compared to NG, MDG (OR = 2.268; 95% CI: 1.967-2.616) and SDG (OR = 4.317; 95% CI: 2.441-7.634) had significantly higher risks of sarcopenia. MDG (OR = 1.943; 95% CI: 1.717-2.198) was associated with a higher risk of moderate malnutrition, and both MDG (OR = 3.786; 95% CI: 3.282-4.368) and SDG (OR = 14.501; 95% CI: 6.847-30.709) were identified as risk factors for severe malnutrition (p < 0.05). Cox regression analysis indicated that MDG and SDG were independent risk factors for all-cause mortality (MDG: HR = 1.460, 95% CI: 1.341-1.590; SDG: HR = 2.257, 95% CI: 1.622-3.140) and cancer-specific mortality (MDG: HR = 1.192, 95% CI: 1.039-1.367; SDG: HR = 2.068, 95% CI: 1.825-2.343) (p < 0.05).

Conclusion: K-means clustering effectively categorized metabolic disorder subgroups, supporting targeted interventions and demonstrating a significant link between disorder severity and adverse outcomes.

Background: Hand-foot syndrome (HFS) is a common adverse event of capecitabine causing treatment modifications. Topical urea cream can reduce sorafenib-induced hand-foot skin reaction. However, its benefit in preventing capecitabine-associated HFS was not seen early in the course and had been unknown with long-term use. The aim of this study was to evaluate the efficacy of urea cream for HFS prophylaxis throughout capecitabine treatment.

Methods: Patients with cancer who received capecitabine were randomized (1:1) to receive usual care alone or in combination with urea-based cream. The incidence and degree of HFS were assessed at each capecitabine cycle. The primary endpoint was the proportion of patients with any grade HFS. The secondary endpoints included the proportion of patients with severe (≥ grade 3) HFS, modifications in capecitabine because of HFS, and HFS onset.

Results: After a median of six capecitabine cycles, any grade HFS was reported by 68 of 109 patients (62.4%) who received usual care and by 60 of 107 patients (56%) who used urea cream (p = 0.36). The patients who received usual care and urea cream had similar proportions of grade 3 HFS occurrence [52 (47.7%) vs. 44 (41.1%), respectively, p = 0.34] and needed capecitabine modification because of HFS [20 patients (18.3%) vs. 17 patients (15.9%), respectively, p = 0.89], as well as similar HFS onset.

Conclusions: Urea-based cream did not prevent capecitabine-associated HFS, reduce capecitabine modification, and delay HFS onset. However, it had a tendency to lessen HFS severity, especially in the later cycles of capecitabine.

Clinical trial registration number: ClinicalTrials.gov Identifier: NCT05348278, registered on April 21, 2022.

Background: Ewing's sarcoma is a childhood bone and soft tissue cancer with poor prognosis. Treatment outcomes for Ewing's sarcoma patients have improved only modestly over the past decades, making the development of new treatment strategies paramount. In this study, the combined targeting of ribonucleotide reductase (RNR) and WEE1 was explored for its effectiveness against Ewing's sarcoma cells.

Methods: The RNR inhibitor triapine and the WEE1 inhibitors adavosertib and ZN-c3 were tested in p53 wild-type and p53 mutant Ewing's sarcoma cells. The combination of adavosertib with the PARP inhibitors olaparib and veliparib was tested for comparison. Combinatorial effects were determined by flow cytometric analyses of cell death, loss of mitochondrial membrane potential and DNA fragmentation as well as by caspase 3/7 activity assay, immunoblotting and real-time RT-PCR. The drug interactions were assessed using combination index analysis.

Results: RNR and WEE1 inhibitors were weakly to moderately effective on their own, but highly effective in combination. The combination treatments were similarly effective in p53 wild-type and p53 mutant cells. They synergistically induced cell death and cooperated to elicit mitochondrial membrane potential decay, to activate caspase 3/7 and to trigger DNA fragmentation, evidencing the induction of the apoptotic cell death cascade. They also cooperated to boost CHK1 phosphorylation, indicating augmented replication stress after combination treatment. In comparison, the combination of adavosertib with PARP inhibitors produced weaker synergistic effects.

Conclusion: Our findings show that combined inhibition of RNR and WEE1 was effective against Ewing's sarcoma in vitro. They thus provide a rationale for the evaluation of the potential of combined targeting of RNR and WEE1 in Ewing's sarcoma in vivo.

Background: Osteosarcoma (OS) is the most common primary bone malignancy with variable molecular biology and prognosis. However, our understanding of the association between cell types and OS progression remains poor.

Methods: We generated a human OS cell atlas by integrating over 110,000 single cells from 17 samples. Multiple machine learning algorithms were applied to develop tumor purity prediction models based on transcriptomic profile of OS. The Scissor algorithm and gene enrichment analyses were conducted to delve into cell-intrinsic molecular characteristics linked to OS prognosis. Moreover, the study investigated the impact of ATF6α in OS aggressiveness through genetic and pharmacological loss of function analyses. Lastly, the CellChat algorithm was employed to investigate cell-cell communications.

Results: Utilizing the high-quality human OS cell atlas, we identified tumor purity as a prognostic indicator and developed a robust tumor purity prediction model. We respectively delineated cancer cell- and immune cell-intrinsic molecular characteristics associated with OS prognosis at single-cell resolution. Interestingly, tumor cells with activated unfolded protein response (UPR) pathway were significantly associated with disease aggressiveness. Notably, ATF6α emerged as the top-activated transcription factor for this tumor subcluster. Subsequently, we confirmed that ATF6α was markedly associated with OS progression, while both genetic and pharmacological inhibition of ATF6α impaired the survival of HOS cells. Lastly, we depicted the landscape of signal crosstalk between the UPR-related subcluster and other cell types within the tumor microenvironment.

Conclusion: In summary, our work provides novel insights into the molecular biology of OS, and offers valuable resource for OS biomarker discovery and treatment strategy development.