DN-ODE: Data-driven neural-ODE modeling for breast cancer tumor dynamics and progression-free survivals.

Pharmacokinetic/Pharmacodynamic (PK/PD) modeling is crucial in the development of new drugs. However, traditional population-based PK/PD models encounter challenges when modeling for individual patients. We aim to explore the potential of constructing a pharmacodynamic model for individual breast cancer pharmacodynamics leveraging only limited data from early clinical trial phases. While previous studies on Neural Ordinary Differential Equations (ODEs) suggest promising results in clinical trial practices, they primarily focused on theoretical applications or independent PK/PD modeling. PD modeling from complex and irregular clinical trial data, especially when interacting with PK parameters, is still unclear. To achieve that, we introduce a Data-driven Neural Ordinary Differential Equation (DN-ODE) modeling for breast cancer tumor dynamics and progression-free survival data. To validate this approach, experiments are conducted with early-phase clinical trial data from the Amcenestrant (an oral treatment for breast cancer) dataset (AMEERA 1-2), aiming to predict pharmacodynamics in the later phase (AMEERA 3). DN-ODE model achieves RMSE scores of 8.78 and 0.21 in tumor size and progression-free survival, respectively, with R2 scores over 0.9 for each task. Compared to PK/PD methodologies, DN-ODE is able to predict robust individual tumor dynamics with only limited cycle data. We also introduce Principal Component Analysis visualizations for encoder results, demonstrating the DN-ODE's capability to discern individual distributions and diverse tumor growth patterns. Therefore, DN-ODE facilitates comprehensive drug efficacy assessments, pinpoints potential responders, and aids in trial design.

The prognostic role of an optimal machine learning model based on clinical available indicators in HCC patients.

Although methods in diagnosis and therapy of hepatocellular carcinoma (HCC) have made significant progress in the past decades, the overall survival (OS) of liver cancer is still disappointing. Machine learning models have several advantages over traditional cox models in prognostic prediction. This study aimed at designing an optimal panel and constructing an optimal machine learning model in predicting prognosis for HCC. A total of 941 HCC patients with completed survival data and preoperative clinical chemistry and immunology indicators from two medical centers were included. The OCC panel was designed by univariate and multivariate cox regression analysis. Subsequently, cox model and machine-learning models were established and assessed for predicting OS and PFS in discovery cohort and internal validation cohort. The best OCC model was validated in the external validation cohort and analyzed in different subgroups. In discovery, internal and external validation cohort, C-indexes of our optimal OCC model were 0.871 (95% CI, 0.863-0.878), 0.692 (95% CI, 0.667-0.717) and 0.648 (95% CI, 0.630-0.667), respectively; the 2-year AUCs of OCC model were 0.939 (95% CI, 0.920-0.959), 0.738 (95% CI, 0.667-0.809) and 0.725 (95% CI, 0.643-0.808), respectively. For subgroup analysis of HCC patients with HBV, aged less than 65, cirrhosis or resection as first therapy, C-indexes of our optimal OCC model were 0.772 (95% CI, 0.752-0.792), 0.769 (95% CI, 0.750-0.789), 0.855 (95% CI, 0.846-0.864) and 0.760 (95% CI, 0.741-0.778), respectively. In general, the optimal OCC model based on RSF algorithm shows prognostic guidance value in HCC patients undergoing individualized treatment.

GATIS score for predicting the prognosis of rectal neuroendocrine neoplasms: A Chinese multicenter study of 12-year experience.

BACKGROUND: There is currently a shortage of accurate, efficient, and precise predictive instruments for rectal neuroendocrine neoplasms (NENs). AIM: To develop a predictive model for individuals with rectal NENs (R-NENs) using data from a large cohort. METHODS: Data from patients with primary R-NENs were retrospectively collected from 17 large-scale referral medical centers in China. Random forest and Cox proportional hazard models were used to identify the risk factors for overall survival and progression-free survival, and two nomograms were constructed. RESULTS: A total of 1408 patients with R-NENs were included. Tumor grade, T stage, tumor size, age, and a prognostic nutritional index were important risk factors for prognosis. The GATIS score was calculated based on these five indicators. For overall survival prediction, the respective C-indexes in the training set were 0.915 (95% confidence interval: 0.866-0.964) for overall survival prediction and 0.908 (95% confidence interval: 0.872-0.944) for progression-free survival prediction. According to decision curve analysis, net benefit of the GATIS score was higher than that of a single factor. The time-dependent area under the receiver operating characteristic curve showed that the predictive power of the GATIS score was higher than that of the TNM stage and pathological grade at all time periods. CONCLUSION: The GATIS score had a good predictive effect on the prognosis of patients with R-NENs, with efficacy superior to that of the World Health Organization grade and TNM stage.

Combination of pemetrexed with bevacizumab for non-small-cell lung cancer: a meta-analysis study.

BACKGROUND: Combining pemetrexed with bevacizumab may have some potential in improving the efficacy in patients with non-small-cell lung cancer (NSCLC), and this meta-analysis aims to explore the impact of pemetrexed addition to bevacizumab on treatment efficacy for NSCLC. METHODS: PubMed, EMbase, Web of science, EBSCO, and Cochrane library databases were systematically searched, and we included randomized controlled trials (RCTs) assessing the effect of pemetrexed addition to bevacizumab on treatment efficacy in patients with NSCLC. Overall survival and progression-free survival were included in this meta-analysis. RESULTS: Four RCTs were finally included in the meta-analysis. Overall, compared with bevacizumab for NSCLC, pemetrexed addition showed significantly improved overall survival (hazard ratio [HR] = 0.87; 95% confidence interval [CI] = 0.76 to 0.99; P = 0.03), survival rate (odd ratio [OR] = 1.41; 95% CI = 1.06 to 1.86; P = 0.02), progression-free survival (HR = 0.63; 95% CI = 0.55 to 0.72; P < 0.00001) and progression-free survival rate (OR = 1.92; 95% CI = 1.38 to 2.67; P < 0.00001), but led to the increase in grade ≥ 3 adverse events (OR = 2.15; 95% CI = 1.62 to 2.84; P < 0.00001). CONCLUSIONS: Pemetrexed addition may be effective to improve treatment efficacy for NSCLC compared to bevacizumab treatment.

Nomogram for predicting survival after first-line anti-PD-1-based immunotherapy in unresectable stage IV melanoma: a multicenter international study.

BACKGROUND: The introduction of anti-programmed cell death protein 1 (PD-1) immunotherapy has revolutionized the treatment landscape for melanoma, enhancing both response rates and survival outcomes in patients with advanced stages of the disease. Despite these remarkable advances, a noteworthy subset of patients (40%-60%) does not derive advantage from this therapeutic approach. This study aims to identify key predictive factors and create a user-friendly predictive nomogram for stage IV melanoma patients receiving first-line anti-PD-1-based immunotherapy, improving treatment decisions. MATERIALS AND METHODS: In this retrospective study, we included patients with unresectable stage IV melanoma who received first-line treatment with either anti-PD-1 monotherapy or anti-PD-1 plus anti-cytotoxic T-lymphocyte associated protein 4 between 2014 and 2018. We documented clinicopathological features and blood markers upon therapy initiation. By employing the random survival forest model and backward variable selection of the Cox model, we identified variables associated with progression-free survival (PFS) after the first-line anti-PD-1-based treatment. We developed and validated a predictive nomogram for PFS utilizing the identified variables. We assessed calibration and discrimination performance metrics as part of the evaluation process. RESULTS: The study involved 719 patients, divided into a training cohort of 405 (56%) patients and a validation cohort of 314 (44%) patients. We combined findings from the random survival forest and the Cox model to create a nomogram that incorporates the following factors: lactate dehydrogenase (LDH), S100, melanoma subtype, neutrophil-to-lymphocyte ratio (NLR), body mass index, type of immune checkpoint inhibitor, and presence of liver or brain metastasis. The resultant model had a C-index of 0.67 in the training cohort and 0.66 in the validation cohort. Performance remained in different patient subgroups. Calibration analysis revealed a favorable correlation between predicted and actual PFS rates. CONCLUSIONS: We developed and validated a predictive nomogram for long-term PFS in patients with unresectable stage IV melanoma undergoing first-line anti-PD-1-based immunotherapy.

Mitomycin C vs. Bacillus Calmette-Guerin for treatment of intermediate-risk nonmuscle invasive bladder cancer patients-A comparative analysis from a single center.

BACKGROUND: Induction followed by 1 year maintenance instillation of intravesical Bacillus Calmette-Guerin (BCG) is the standard treatment for intermediate-risk (IR) nonmuscle invasive bladder cancer (NMIBC) patients. Few data exist on the efficacy of Mitomycin C (MMC) instillation in this setting. METHODS: We retrospectively analyzed 226 IR-NMIBC patients classified by the International Bladder Cancer Group (IBCG) and 250 IR-NMIBC intravescical treatment-naïve patients classified by the European Association of Urology (EAU). All patients received either a full induction course of BCG or 40 mg/40 ml MMC from 2012 to 2022. Optimal treatment was defined as 1-year maintenance for BCG and 11 monthly maintenance instillations for MMC. Kaplan-Meier analysis estimated recurrence-free survival (RFS) before and after inverse probability of treatment-weighting (IPTW) and progression-free survival (PFS). Multivariable Cox regression was used to evaluate difference in recurrence after adjustment for clinically relevant variables before and after IPTW. RESULTS: Optimal BCG and MMC courses were administered to 21% of IR-IBCG and 23% of IR-EAU patients. At 4-years, patients treated with optimal MMC and BCG treatment had similar RFS and PFS in both EAU and IBCG groups. Patients receiving nonoptimal BCG compared to optimal MMC exhibited lower 4-year RFS after IPTW (82% vs. 68% in EAU and 82% vs. 65% in IBCG). At 4-year optimal MMC had greater PFS non optimal BCG. Optimal MMC treatment predicted recurrence in EAU (adjusted and weighted HR 0.33, 95% CI, 0.11-0.98) and IBCG (adjusted and weighted HR 0.29, 95% CI, 0.08-0.97) groups compared to nonoptimal BCG. CONCLUSIONS: Optimal 40 mg/40 ml MMC treatment was as effective as optimal BCG in IR-IBCG and IR-EAU NMIBC patients, reducing both recurrence and progression compared to nonoptimal BCG. MMC could be a valid first line alternative to BCG for both IR-EAU and IR-IBCG intravescical treatment-naïve patients, during BCG shortages.

The clinical significance of inflammatory mediators in predicting obesity and progression-free survival in patients with adult-onset Craniopharyngioma.

BACKGROUND: Craniopharyngioma (CP) is a rare malformational tumor characterized by high rates of recurrence and morbid obesity. However, the role of inflammatory mediators in obesity and the prognosis of patients with CP remains unknown. Therefore, the present study aimed to analyze associations of inflammatory mediators with weight-related outcomes and the prognosis of patients with CP. METHODS: A total of 130 consecutive patients with CP were included in this study. The expression levels of seven inflammatory mediators and the plasma leptin concentration were investigated. Clinical parameters, weight changes, new-onset obesity, and progression-free survival (PFS) were recorded. The relationships between inflammatory mediators, clinicopathologic parameters, weight-related outcomes, and PFS were explored. RESULTS: Compared with those in normal pituitary tissue, the expressions of inflammatory mediators in tumor tissue were higher. Higher expression levels of CXCL1 and CXCL8 were identified as independent risk factors for significant weight gain, and CXCL1 and TNF were identified as independent risk factors for new-onset postoperative obesity. Poor PFS was associated with higher expression levels of CXCL1, CXCL8, IL1A, IL6, and TNF. CONCLUSION: The present study revealed that inflammatory mediators are associated with morbid obesity in patients with CP. Inflammatory mediators may be the critical bridge between elevated leptin and weight-related outcomes. Additionally, PFS was associated with the expression of inflammatory mediators. Further research is needed to elucidate the underlying mechanisms of inflammatory mediators and their potential as targets for novel therapies for CP.

The role of biomarkers and dosimetry parameters in overall and progression free survival prediction for patients treated with personalized 90Y glass microspheres SIRT: a preliminary machine learning study.

BACKGROUND: Overall Survival (OS) and Progression-Free Survival (PFS) analyses are crucial metrics for evaluating the efficacy and impact of treatment. This study evaluated the role of clinical biomarkers and dosimetry parameters on survival outcomes of patients undergoing 90Y selective internal radiation therapy (SIRT). MATERIALS/METHODS: This preliminary and retrospective analysis included 17 patients with hepatocellular carcinoma (HCC) treated with 90Y SIRT. The patients underwent personalized treatment planning and voxel-wise dosimetry. After the procedure, the OS and PFS were evaluated. Three structures were delineated including tumoral liver (TL), normal perfused liver (NPL), and whole normal liver (WNL). 289 dose-volume constraints (DVCs) were extracted from dose-volume histograms of physical and biological effective dose (BED) maps calculated on 99mTc-MAA and 90Y SPECT/CT images. Subsequently, the DVCs and 16 clinical biomarkers were used as features for univariate and multivariate analysis. Cox proportional hazard ratio (HR) was employed for univariate analysis. HR and the concordance index (C-Index) were calculated for each feature. Using eight different strategies, a cross-combination of various models and feature selection (FS) methods was applied for multivariate analysis. The performance of each model was assessed using an averaged C-Index on a three-fold nested cross-validation framework. The Kaplan-Meier (KM) curve was employed for univariate and machine learning (ML) model performance assessment. RESULTS: The median OS was 11 months [95% CI: 8.5, 13.09], whereas the PFS was seven months [95% CI: 5.6, 10.98]. Univariate analysis demonstrated the presence of Ascites (HR: 9.2[1.8,47]) and the aim of SIRT (segmentectomy, lobectomy, palliative) (HR: 0.066 [0.0057, 0.78]), Aspartate aminotransferase (AST) level (HR:0.1 [0.012-0.86]), and MAA-Dose-V205(%)-TL (HR:8.5[1,72]) as predictors for OS. 90Y-derived parameters were associated with PFS but not with OS. MAA-Dose-V205(%)-WNL, MAA-BED-V400(%)-WNL with (HR:13 [1.5-120]) and 90Y-Dose-mean-TL, 90Y-D50-TL-Gy, 90Y-Dose-V205(%)-TL, 90Y-Dose- D50-TL-Gy, and 90Y-BED-V400(%)-TL (HR:15 [1.8-120]) were highly associated with PFS among dosimetry parameters. The highest C-index observed in multivariate analysis using ML was 0.94 ± 0.13 obtained from Variable Hunting-variable-importance (VH.VIMP) FS and Cox Proportional Hazard model predicting OS, using clinical features. However, the combination of VH. VIMP FS method with a Generalized Linear Model Network model predicting OS using Therapy strategy features outperformed the other models in terms of both C-index and stratification of KM curves (C-Index: 0.93 ± 0.14 and log-rank p-value of 0.023 for KM curve stratification). CONCLUSION: This preliminary study confirmed the role played by baseline clinical biomarkers and dosimetry parameters in predicting the treatment outcome, paving the way for the establishment of a dose-effect relationship. In addition, the feasibility of using ML along with these features was demonstrated as a helpful tool in the clinical management of patients, both prior to and following 90Y-SIRT.

Diagnosing Progression in Glioblastoma-Tackling a Neuro-Oncology Problem Using Artificial-Intelligence-Derived Volumetric Change over Time on Magnetic Resonance Imaging to Examine Progression-Free Survival in Glioblastoma.

Glioblastoma (GBM) is the most aggressive and the most common primary brain tumor, defined by nearly uniform rapid progression despite the current standard of care involving maximal surgical resection followed by radiation therapy (RT) and temozolomide (TMZ) or concurrent chemoirradiation (CRT), with an overall survival (OS) of less than 30% at 2 years. The diagnosis of tumor progression in the clinic is based on clinical assessment and the interpretation of MRI of the brain using Response Assessment in Neuro-Oncology (RANO) criteria, which suffers from several limitations including a paucity of precise measures of progression. Given that imaging is the primary modality that generates the most quantitative data capable of capturing change over time in the standard of care for GBM, this renders it pivotal in optimizing and advancing response criteria, particularly given the lack of biomarkers in this space. In this study, we employed artificial intelligence (AI)-derived MRI volumetric parameters using the segmentation mask output of the nnU-Net to arrive at four classes (background, edema, non-contrast enhancing tumor (NET), and contrast-enhancing tumor (CET)) to determine if dynamic changes in AI volumes detected throughout therapy can be linked to PFS and clinical features. We identified associations between MR imaging AI-generated volumes and PFS independently of tumor location, MGMT methylation status, and the extent of resection while validating that CET and edema are the most linked to PFS with patient subpopulations separated by district rates of change throughout the disease. The current study provides valuable insights for risk stratification, future RT treatment planning, and treatment monitoring in neuro-oncology.

Prognostic Role of CA-125 Elimination Rate Constant (KELIM) in Patients with Advanced Epithelial Ovarian Cancer Who Received PARP Inhibitors.

BACKGROUND: This multicenter retrospective study aimed to investigate the prognostic value of the CA-125 elimination rate constant K (KELIM) in EOC patients who received platinum-based chemotherapy followed by PARP inhibitors, in either upfront or interval treatment settings. METHODS: Between July 2019 and November 2022, we identified stage III-IV EOC patients who underwent primary or interval cytoreductive surgery and received olaparib or niraparib. Individual KELIM values were assessed based on validated kinetics and classified into favorable and unfavorable cohorts. RESULTS: In a study of 252 patients undergoing frontline maintenance therapy with olaparib or niraparib, favorable KELIM (≥1) scores were associated with a higher PFS benefit in the primary cytoreductive surgery (PCS) cohort (hazard ratio (HR) for disease progression or death 3.51, 95% confidence interval (CI); 1.37-8.97, p = 0.009). Additionally, within the interval cytoreductive surgery (ICS) cohort, a favorable KELIM score (≥1) significantly increased the likelihood of achieving complete resection following cytoreductive surgery, with 59.4% in the favorable KELIM group compared to 37.8% in those with unfavorable KELIM. CONCLUSIONS: A favorable KELIM score was associated with improved PFS in patients with advanced EOC undergoing PCS. Furthermore, in the ICS cohort, a favorable KELIM score increased the probability of complete cytoreduction.

Prognostic Value of Ki67 in Epithelial Ovarian Cancer: Post-Neoadjuvant Chemotherapy Ki67 Combined with CA125 Predicting Recurrence.

Purpose: To evaluate Ki67 expression and prognostic value during neoadjuvant chemotherapy (NACT) in advanced epithelial ovarian cancer (EOC). Patients and Methods: 95 patients with advanced EOC receiving NACT followed by interval debulking surgery (IDS) were available for tissue samples from matched pre- and post-therapy specimens. The expression of Ki-67 was evaluated by immunohistochemistry and classified by percentage of stained cells. The optimal cutoff values of the Ki67 were assessed by receiver operating characteristic analysis. Kaplan-Meier analysis, the Log rank test, and Cox regression analysis were carried out to analyze survival. Results: Post-NACT Ki67 was an independent prognostic factor for recurrence by univariate (HR: 1.8, 95% CI: 1.1-3.0, P-value: 0.023) and multivariate (HR: 1.88, 95% CI: 1.08-3.26, P-value: 0.025) analysis. Residual disease >1cm (HR: 2.69, 95% CI: 1.31-5.54, P-value: 0.0070) and pre-treatment CA125 ≥ 1432 U/mL (HR: 2.00, 95% CI: 1.13-3.55, P-value: 0.017) were also independent risk factors for progression-free survival (PFS) in multivariate analysis. Post-NACT Ki67 ≥ 20% was an independent risk factor for PFS, however, baseline Ki67 and Ki67 change did not suggest prognostic significance. In patients with high CA125, the median PFS for patients with high postKi67 (median PFS: 15.0 months, 95% CI: 13.4-16.6 months) was significantly (P-value: 0.013) poorer compared to patients with low postKi67 (median PFS: 30.0 months, 95% CI: 13.5-46.5 months). Conclusion: Post-NACT Ki67 ≥ 20% was an independent factor associated with poorer PFS in patients with advanced-stage EOC undergoing NACT followed by IDS. The combination of post-NACT Ki67 and pretreatment CA125 could better identify patients with poorer PFS in NACT-administered patients.

Evaluation of prognostic biomarkers in meningiomas and their clinical implications in settings with limited resources.

Background: The 5th edition of the World Health Organization (WHO) Central Nervous System (CNS) tumor classification for meningiomas acknowledges the clinical relevance of genomic profiling studies and emphasizes the importance of incorporating molecular information alongside histopathological features, leading to more accurate diagnoses and improved patient care. Methods: We analyzed 206 meningioma samples (108 histological grade 1, 89 grade 2, and 9 grade 3) to study pTERT mutations, CDKN2A/B homozygous deletion, loss of H3K27me3, and p16 expression. The association of these molecular markers with survival outcomes was also assessed. Results: pTERT mutation was found in 4.85% of cases, predominantly occurring in histological grade 2 (11.24%), while none of the histological grade 1 or 3 meningiomas exhibited this mutation. CDKN2A/B gene deletion was absent in grade 1 and detected in 2.24% of grade2, and 33.3% of histological grade 3 cases. There was a significant increase in loss of H3K27me3 with higher tumor grades, while p16 loss was observed in over 50% of cases across all histological grades. The presence of pTERT mutation and CDKN2A/B homozygous deletion resulted in the reclassification of 5.33% (11/206) of meningiomas as integrated grade 3. pTERT mutation and CDKN2A/B deletion, emerged as prognostically relevant markers, showing significant differences in progression-free survival (PFS) between integrated grade 3 and histological grade 2 meningiomas (P = .0002). Conclusions: pTERT mutations are the most clinically relevant genetic alterations in meningiomas. Routine testing for pTERT mutations can identify high-risk cases of histologically grade 2 meningiomas, providing crucial prognostic information for treatment planning. CDKN2A/B alteration is rare and not cost-effective in assessing meningiomas. Immunohistochemical assessment of p16 and H3K27me3 expression lacks significant prognostic value. Assessment of pTERT mutations offers a cost-effective and valuable diagnostic tool for meningiomas.

Multi-parameter MRI radiomic features may contribute to predict progression-free survival in patients with WHO grade II meningiomas.

Aim: This study aims to investigate the potential value of radiomic features from multi-parameter MRI in predicting progression-free survival (PFS) of patients with WHO grade II meningiomas. Methods: Kaplan-Meier survival curves were used for survival analysis of clinical features. A total of 851 radiomic features were extracted based on tumor region segmentation from each sequence, and Max-Relevance and Min-Redundancy (mRMR) algorithm was applied to filter and select radiomic features. Bagged AdaBoost, Stochastic Gradient Boosting, Random Forest, and Neural Network models were built based on selected features. Discriminative abilities of models were evaluated using receiver operating characteristics (ROC) and area under the curve (AUC). Results: Our study enrolled 164 patients with WHO grade II meningiomas. Female gender (p=0.023), gross total resection (GTR) (p<0.001), age <68 years old (p=0.023), and edema index <2.3 (p=0.006) are protective factors for PFS in these patients. Both the Bagged AdaBoost model and the Neural Network model achieved the best performance on test set with an AUC of 0.927 (95% CI, Bagged AdaBoost: 0.834-1.000; Neural Network: 0.836-1.000). Conclusion: The Bagged AdaBoost model and the Neural Network model based on radiomic features demonstrated decent predictive ability for PFS in patients with WHO grade II meningiomas who underwent operation using preoperative multi-parameter MR images, thus bringing benefit for patient prognosis prediction in clinical practice. Our study emphasizes the importance of utilizing advanced imaging techniques such as radiomics to improve personalized treatment strategies for meningiomas by providing more accurate prognostic information that can guide clinicians toward better decision-making processes when treating their patients' conditions effectively while minimizing risks associated with unnecessary interventions or treatments that may not be beneficial.

Local treatment benefits patients with oligometastatic prostate cancer: A systematic review and meta-analysis.

OBJECTIVES: This study aims to evaluate the efficacy of local treatment (LT), including radiotherapy (RT) and cytoreductive prostatectomy (CRP), in improving outcomes for patients with oligometastatic prostate cancer (OmPCa). METHODS: A systematic review and meta-analysis of articles from PubMed, Embase, and Web of Science published between 2010 and November 2023 were conducted. The study included 11 articles, comprising three randomized controlled trials (RCTs) and eight retrospective analyses. The study assessed overall survival (OS), radiographic progression-free survival (rPFS), prostate-specific antigen (PSA) PFS, cancer-specific survival (CSS), and complication rate (CR). RESULTS: OS was significantly improved in the LT group, with both RCTs and non-RCTs showing statistical significance [hazard ratios (HR) = 0.64; 95% confidence intervals (95% CIs), 0.51-0.80; p < 0.0001; HR = 0.55; 95% CIs, 0.40-0.77; p = 0.0004]. For rPFS, RCTs did not show statistically significant outcomes (HR = 0.60; 95% CIs, 0.34-1.07; p = 0.09), whereas non-RCTs demonstrated significant results (HR = 0.42; 95% CIs, 0.24-0.72; p = 0.002). Both RCTs and non-RCTs showed a significant improvement in PSA-PFS (HR = 0.44; 95%CI, 0.29-0.67; p = 0.0001; HR = 0.51; 95% CIs, 0.32-0.81; p = 0.004). For CSS, RCTs demonstrated statistical differences (HR = 0.65; 95% CIs, 0.47-0.90; p = 0.009), whereas non-RCTs did not (HR = 0.61; 95% CIs, 0.29-1.27; p = 0.19). Regarding CR, the risk difference was -0.22 (95% CIs, -0.32 to -0.12; p < 0.00001). CONCLUSION: LT significantly improved OS and PFS in patients with OmPCa. Further RCTs are necessary to confirm these results.

The efficacy of adjuvant chemotherapy in patients with different midpoint-radiotherapy Epstein-Barr virus DNA plasma loads.

OBJECTIVES: This study aimed to evaluate the efficacy of adjuvant chemotherapy (AC) in patients with different midpoint-radiotherapy (mid-RT) Epstein-Barr virus (EBV) DNA plasma loads for locoregionally advanced nasopharyngeal carcinoma (NPC), and to provide decision-making regarding the use of AC. MATERIALS AND METHODS: A total of 675 consecutive patients diagnosed with stage III-IVa NPC were enrolled in this study. All patients underwent concurrent chemoradiotherapy (CCRT), either with or without induction chemotherapy or AC, or a combination of both. The primary endpoint of this study was progression-free survival (PFS). RESULTS: Among the 675 enrolled patients, 248 (36.7 %) received AC and 427 (63.3 %) were only observed after CCRT. In total, 149 (22.1 %) patients had detectable mid-RT EBV DNA levels, whereas 526 (77.9 %) had undetectable mid-RT EBV DNA levels. Patients with detectable mid-RT EBV DNA had worse 5-year PFS than those with undetectable mid-RT EBV DNA (74.8 % vs. 81.9 %, P = 0.045). AC group showed significantly better 5-year PFS than observation in patients with detectable mid-RT EBV DNA (82.8 % vs. 66.8 %; HR, 0.480; 95 % CI 0.250-0.919, P = 0.027). Multivariate analyses demonstrated that the treatment methods (AC vs. observation) were independent prognostic factors for PFS (HR, 0.37; 95 % CI 0.19-0.74, P = 0.005). However, in patients with undetectable mid-RT EBV DNA (5-year PFS: HR 0.873, 95 % CI 0.565-1.349, P = 0.52), AC group showed no survival benefit for observation. CONCLUSION: AC could reduce the risk of disease progression compared to observation in patients with detectable mid-RT EBV DNA. Our findings suggest that AC is effective in patients at a high risk of treatment failure.

Stereotactic radiosurgery for intraventricular meningioma: a systematic review and meta-analysis.

BACKGROUND: Intraventricular meningioma (IVM) is a rare subtype of intracranial meningioma, accounting for 9.8 to 14% of all intraventricular tumors. Currently, there is no clear consensus on which patients with IVM should receive conservative treatment, surgery, or stereotactic radiosurgery (SRS). This research aims to analyze the outcomes, including survival and recurrence rates of patients who undergo SRS for IVM as a primary or adjuvant treatment. METHODS: A systematic search was conducted in Scopus, Web of Science, PubMed, and Embase till June 5th 2023. Screening and data extraction were performed by two independent authors. Random-effect meta-analysis was performed to determine the tumor control proportion of IVM cases treated with SRS. Individual patient data (IPD) meta-analysis was performed for the progression-free survival (PFS) of the patients in the follow-up time. All analyses were performed using the R programming language. RESULTS: Out of the overall 132 records, 14 were included in our study, of which only 7 had enough data for the meta-analysis. The tumor control proportion was 0.92 (95% CI, 0.69-0.98) in patients who underwent SRS for primary IVM. The overall tumor control in both primary and adjuvant cases was 0.87 (95% CI, 0.34-0.99). the heterogeneity was not significant in both meta-analyses (P = 0.73 and P = 0.92, respectively). Post-SRS perifocal edema occurred in 16 out of 71 cases (0.16; 95% CI, 0.03-0.56), with no significant heterogeneity (P = 0.32). IPD meta-analysis showed a PFS of 94.70% in a 2-year follow-up. Log-rank test showed better PFS in primary SRS compared to adjuvant SRS (P < 0.01). CONCLUSIONS: According to this study, patients with IVM can achieve high rates of tumor control with a low risk of complications when treated with SRS, regardless of whether they have received prior treatment. Although SRS could be a promising first-line treatment option for asymptomatic IVM, its efficacy in symptomatic patients and its comparison with resection require further investigation.

Omics-imaging signature-based nomogram to predict the progression-free survival of patients with hepatocellular carcinoma after transcatheter arterial chemoembolization.

BACKGROUND: Enhanced magnetic resonance imaging (MRI) is widely used in the diagnosis, treatment and prognosis of hepatocellular carcinoma (HCC), but it can not effectively reflect the heterogeneity within the tumor and evaluate the effect after treatment. Preoperative imaging analysis of voxel changes can effectively reflect the internal heterogeneity of the tumor and evaluate the progression-free survival (PFS). AIM: To predict the PFS of patients with HCC before operation by building a model with enhanced MRI images. METHODS: Delineate the regions of interest (ROI) in arterial phase, portal venous phase and delayed phase of enhanced MRI. After extracting the combinatorial features of ROI, the features are fused to obtain deep learning radiomics (DLR)_Sig. DeLong's test was used to evaluate the diagnostic performance of different typological features. K-M analysis was applied to assess PFS in different risk groups, and the discriminative ability of the model was evaluated using the C-index. RESULTS: Tumor diameter and diolame were independent factors influencing the prognosis of PFS. Delong's test revealed multi-phase combined radiomic features had significantly greater area under the curve values than did those of the individual phases (P < 0.05).In deep transfer learning (DTL) and DLR, significant differences were observed between the multi-phase and individual phases feature sets (P < 0.05). K-M survival analysis revealed a median survival time of high risk group and low risk group was 12.8 and 14.2 months, respectively, and the predicted probabilities of 6 months, 1 year and 2 years were 92%, 60%, 40% and 98%, 90%,73%, respectively. The C-index was 0.764, indicating relatively good consistency between the predicted and observed results. DTL and DLR have higher predictive value for 2-year PFS in nomogram. CONCLUSION: Based on the multi-temporal characteristics of enhanced MRI and the constructed Nomograph, it provides a new strategy for predicting the PFS of transarterial chemoembolization treatment of HCC.

Treatment adherence to oral chemotherapy in patients with locally advanced or metastatic non-small cell lung cancer: Protocol for a hospital pharmacy-based real-world study.

OBJECTIVE: This article describes a study protocol for evaluating adherence to oral chemotherapy (OCT) in patients with locally advanced or metastatic non-small cell lung cancer (NSCLC) in Spain. METHODS: This multicenter, observational, prospective study will be conducted by 6 hospital pharmacists from 6 Spanish hospitals. The study will include men and women aged 18 years or older with a diagnosis of locally advanced or metastatic NSCLC who are being treated or have been prescribed OCT. Once included, the patient will be active and prospectively followed up for 3 months, including 4 study visits to record information on sociodemographic variables, antineoplastic treatment and adherence, pharmaceutical care, clinical variables, and patient-reported outcomes (PRO) (the 3-level version of EQ-5D, the EORTC Core Quality of Life Questionnaire, the Brief Illness Perception Questionnaire, the Treatment Satisfaction with Medicines Questionnaire, and the PRO version of Common Terminology Criteria for Adverse Events). Twelve months after patient inclusion, we will record information on the disease progression status and dispensed prescriptions. The primary outcome is the percentage of treatment adherence that will be calculated based on the pill count as follows: the difference between the number of pills dispensed minus the number of unused pills will be divided by the number of days of treatment multiplied by the number of pills/day prescribed by the oncologist; this quotient will be multiplied by 100 to obtain the percentage of adherence. Based on the that pill count reconciliation, those with a percentage adherence >80% will be primarily categorized as adherent. Secondarily, treatment adherence will be also calculated based on the proportion of days covered and the 4-items Morisky Green Levine Medication Adherence Scale. To analyze the impact of patients' and treatment characteristics on adherence, bivariate analyses will be performed using different adherence cut-off points. To evaluate the impact of adherence on treatment efficacy as evaluated by progression-free survival, we will be using the Kaplan-Meier method and compare it with the log-rank test and univariate Cox regression analysis. CONCLUSIONS: We expect that our study will provide initial information on key aspects of adherence to OCT (i.e., measurement, facilitators, and barriers) and its relationship with patients' and clinically relevant outcomes in the setting of NSCLC, and that this information will help in designing pharmaceutical interventions to improve adherence.

LINC01432 binds to CELF2 in newly diagnosed multiple myeloma promoting short progression-free survival to standard therapy.

Multiple Myeloma (MM) is a highly prevalent and incurable form of cancer that arises from malignant plasma cells, with over 35,000 new cases diagnosed annually in the United States. While there are a growing number of approved therapies, MM remains incurable and nearly all patients will relapse and exhaust all available treatment options. Mechanisms for disease progression are unclear and in particular, little is known regarding the role of long non-coding RNAs (lncRNA) in mediating disease progression and response to treatment. In this study, we used transcriptome sequencing to compare newly diagnosed MM patients who had short progression-free survival (PFS) to standard first-line treatment (PFS < 24 months) to patients who had prolonged PFS (PFS > 24 months). We identified 157 differentially upregulated lncRNAs with short PFS and focused our efforts on characterizing the most upregulated lncRNA, LINC01432. We investigated LINC01432 overexpression and CRISPR/Cas9 knockdown in MM cell lines to show that LINC01432 overexpression significantly increases cell viability and reduces apoptosis, while knockdown significantly reduces viability and increases apoptosis, supporting the clinical relevance of this lncRNA. Next, we used individual-nucleotide resolution cross-linking immunoprecipitation with RT-qPCR to show that LINC01432 directly interacts with the RNA binding protein, CELF2. Lastly, we showed that LINC01432-targeted locked nucleic acid antisense oligonucleotides reduce viability and increases apoptosis. In summary, this fundamental study identified lncRNAs associated with short PFS to standard NDMM treatment and further characterized LINC01432, which inhibits apoptosis.