Pretreatment Clinical Data Research Articles

Characterization of Fibroblast Growth Factor Receptor Alterations and Activation in Patients with High-Risk Non-Muscle Invasive Bladder Cancer.

The GARNER study investigated fibroblast growth factor receptor (FGFR) alteration (ALT) frequency and the clinical outcome relationship with Bacillus Calmette-Guérin (BCG) treatment in high-risk non-muscle invasive bladder cancer (HR-NMIBC). An FGFR predictive response signature (FGFR-PRS) was discovered that identifies patients with an activated FGFR pathway who could potentially benefit from FGFR-targeted therapy beyond those who are FGFR ALT (+). Pre-treatment tumor samples and clinical data were analyzed from 582 BCG-treated HR-NMIBC patients. FGFR-PRS was discovered using a separate BC dataset and applied to the GARNER and other BC cohorts. FGFR-PRS was also applied to in vitro data from UC cell lines treated with FGFR-active agents. 31% of pre-treatment GARNER HR-NMIBC tumors were FGFR ALT (+) but this was not significantly associated with BCG response. For the subset of patients with paired pre- and post-BCG treatment samples, nearly one-third pre-treatment ALT (+) patients were ALT (-) post-treatment. FGFR-PRS identified patients with an activated FGFR pathway and identified approximately 2-fold additional patients compared to ALT status alone, and this increase was similar across tumor stage. A positive relationship between tumor growth inhibition and FGFR-PRS score was shown in BC in vitro models treated with FGFR-active agents. These data provide support for FGFR-targeted therapy use in FGFR ALT (+) HR-NMIBC and describe tumors with shared FGFR pathway-activated biology that are FGFR ALT (-) but FGFR-PRS (+). The latter suggests a broader potential patient population for FGFR-targeted therapy, which will require subsequent validation in patients treated with FGFR-targeted therapy.

Functional Liver Imaging Score Derived from Gadoxetic Acid-enhanced MRI Predicts Cachexia and Prognosis in Hepatocellular Carcinoma Patients.

Cachexia occurs in approximately half of hepatocellular carcinoma (HCC) patients as the disease progresses and is correlated with a poor prognosis. Therefore, early identification of HCC patients at risk of developing cachexia and their prognosis is crucial. This study investigated the functional liver imaging score (FLIS) derived from gadoxetic acid-enhanced magnetic resonance imaging (MRI) to identify cachexia in HCC patients and their prognosis. Pretreatment clinical and MRI data from 339 HCC patients who underwent gadoxetic acid-enhanced MRI scans were retrospectively collected. Patient weights were recorded for 6 months following the MRI scan to diagnose cachexia. The FLIS was calculated as the sum of the enhancement quality score, the excretion quality score, and the portal vein sign quality score. A Cox proportional hazards model was used to determine the significant factors affecting overall survival (OS). Multivariable logistic regression was then conducted to identify variables predicting cachexia in HCC patients, which were subsequently used to predict OS. Cox regression analysis revealed a significant association between cachexia and worse OS. Both FLIS (0-4 vs. 5-6 points) (OR, 9.20; 95% CI: 4.68-18.10; P<0.001) and α-fetoprotein >100 ng/mL (OR, 4.08; 95% CI: 2.13-7.83; P<0.001) emerged as significant predictors of cachexia in patients with HCC. Furthermore, FLIS (0-4 vs. 5-6 points) (HR, 1.73; 95% CI: 1.19-2.51; P=0.004) was significantly associated with OS. Patients in the FLIS 0-4 points group had shorter OS than those in the FLIS 5-6 points group [20 months (95% CI, 14.7-25.3) vs. 43 months (95% CI, 27.7-58.3); P=0.001]. Cachexia was associated with worse OS. The functional liver imaging score emerged as a significant predictor of cachexia in HCC patients and their prognosis.

Deep Learning-Based Prediction of Post-treatment Survival in Hepatocellular Carcinoma Patients Using Pre-treatment CT Images and Clinical Data.

The objective of this study was to develop and evaluate a model for predicting post-treatment survival in hepatocellular carcinoma (HCC) patients using their CT images and clinical information, including various treatment information. We collected pre-treatment contrast-enhanced CT images and clinical information including patient-related factors, initial treatment options, and survival status from 692 patients. The patient cohort was divided into a training cohort (n = 507), a testing cohort (n = 146), and an external CT cohort (n = 39), which included patients who underwent CT scans at other institutions. After model training using fivefold cross-validation, model validation was performed on both the testing cohort and the external CT cohort. Our cascaded model employed a 3D convolutional neural network (CNN) to extract features from CT images and derive final survival probabilities. These probabilities were obtained by concatenating previously predicted probabilities for each interval with the patient-related factors and treatment options. We utilized two consecutive fully connected layers for this process, resulting in a number of final outputs corresponding to the number of time intervals, with values representing conditional survival probabilities for each interval. Performance was assessed using the concordance index (C-index), the mean cumulative/dynamic area under the receiver operating characteristics curve (mC/D AUC), and the mean Brier score (mBS), calculated every 3months. Through an ablation study, we found that using DenseNet-121 as the backbone network and setting the prediction interval to 6months optimized the model's performance. The integration of multimodal data resulted in superior predictive capabilities compared to models using only CT images or clinical information (C index 0.824 [95% CI 0.822-0.826], mC/D AUC 0.893 [95% CI 0.891-0.895], and mBS 0.121 [95% CI 0.120-0.123] for internal test cohort; C index 0.750 [95% CI 0.747-0.753], mC/D AUC 0.819 [95% CI 0.816-0.823], and mBS 0.159 [95% CI 0.158-0.161] for external CT cohort, respectively). Our CNN-based discrete-time survival prediction model with CT images and clinical information demonstrated promising results in predicting post-treatment survival of patients with HCC.

Development of machine learning-based personalized predictive models for risk evaluation of hepatocellular carcinoma in hepatitis B virus-related cirrhosis patients with low levels of serum alpha-fetoprotein

Introduction and ObjectivesThe increasing incidence of hepatocellular carcinoma (HCC) in China is an urgent issue, necessitating early diagnosis and treatment. This study aimed to develop personalized predictive models by combining machine learning (ML) technology with a demographic, medical history, and noninvasive biomarker data. These models can enhance the decision-making capabilities of physicians for HCC in hepatitis B virus (HBV)-related cirrhosis patients with low serum alpha-fetoprotein (AFP) levels. Patients and MethodsA total of 6,980 patients treated between January 2012 and December 2018 were included. Pre-treatment laboratory tests and clinical data were obtained. The significant risk factors for HCC were identified, and the relative risk of each variable affecting its diagnosis was calculated using ML and univariate regression analysis. The data set was then randomly partitioned into validation (20 %) and training sets (80 %) to develop the ML models. ResultsTwelve independent risk factors for HCC were identified using Gaussian naïve Bayes, extreme gradient boosting (XGBoost), random forest, and least absolute shrinkage and selection operation regression models. Multivariate analysis revealed that male sex, age >60 years, alkaline phosphate >150 U/L, AFP >25 ng/mL, carcinoembryonic antigen >5 ng/mL, and fibrinogen >4 g/L were the risk factors, whereas hypertension, calcium <2.25 mmol/L, potassium ≤3.5 mmol/L, direct bilirubin >6.8 μmol/L, hemoglobin <110 g/L, and glutamic-pyruvic transaminase >40 U/L were the protective factors in HCC patients. Based on these factors, a nomogram was constructed, showing an area under the curve (AUC) of 0.746 (sensitivity = 0.710, specificity=0.646), which was significantly higher than AFP AUC of 0.658 (sensitivity = 0.462, specificity=0.766). Compared with several ML algorithms, the XGBoost model had an AUC of 0.832 (sensitivity = 0.745, specificity=0.766) and an independent validation AUC of 0.829 (sensitivity = 0.766, specificity = 0.737), making it the top-performing model in both sets. The external validation results have proven the accuracy of the XGBoost model. ConclusionsThe proposed XGBoost demonstrated a promising ability for individualized prediction of HCC in HBV-related cirrhosis patients with low-level AFP.

Predicting cachexia in hepatocellular carcinoma patients: a nomogram based on MRI features and body composition.

Approximately half of all patients with hepatocellular carcinoma (HCC) develop cachexia during the course of the disease. It is important to be able to predict which patients will develop cachexia at an early stage. To develop and validate a nomogram based on the magnetic resonance imaging (MRI) features of HCC and body composition for potentially predicting cachexia in patients with HCC. A retrospective two-center study recruited the pretreatment clinical and MRI data of 411 patients with HCC undergoing abdominal MRI. The data were divided into three cohorts for development, internal validation, and external validation. Patients were followed up for six months after the MRI scan to record each patient's weight to diagnose cachexia. Logistic regression analyses were performed to identify independent variables associated with cachexia in the development cohort used to build the nomogram. The multivariable analysis suggested that the MRI parameters of tumor size > 5 cm (P = 0.001), intratumoral artery (P = 0.004), skeletal muscle index (P < 0.001), and subcutaneous fat area (P = 0.004) were independent predictors of cachexia in patients with HCC. The nomogram derived from these parameters in predicting cachexia reached an area under receiver operating characteristic curve of 0.819, 0.783, and 0.814 in the development, and internal and external validation cohorts, respectively. The proposed multivariable nomogram suggested good performance in predicting the risk of cachexia in HCC patients.

Evaluating the psychometric structure of the Hamilton Rating Scale for Depression pre- and post-treatment in antidepressant randomised trials: Secondary analysis of 6843 individual participants from 20 trials

BackgroundThe 17-item Hamilton Rating Scale for Depression (HRSD-17) is the most popular depression measure in antidepressant clinical trials. Prior evidence indicates poor replicability and inconsistent factorial structure. This has not been studied in pooled randomised trial data, nor has a psychometrically optimal model been developed. AimsTo examine the psychometric properties of the HRSD-17 for pre-treatment and post-treatment clinical trial data in a large pooled database of antidepressant randomised controlled trial participants, and to determine an optimal abbreviated version. MethodData for 6843 participants were obtained from the data repository Vivli.org and randomly split into groups for exploratory (n = 3421) and confirmatory (n = 3422) factor analysis. Invariance methods were used to assess potential sex differences. ResultsThe HRSD-17 was psychometrically sub-optimal and non-invariant for all models. High item variances and low variance explained suggested redundancy in each model. EFA failed at baseline and produced four item models for outcome groups (five for placebo-outcome), which were metric but not scalar invariant. ConclusionsIn antidepressant trial data, the HRSD-17 was psychometrically inadequate and scores were not sex invariant. Neither full nor abbreviated HRSD models are suitable for use in clinical trial settings and the HRSD's status as the gold standard should be reconsidered.

A plasma-based proteomic platform for predicting clinical benefit from immune checkpoint inhibitors in multiple cancers.

2568 Background: Immune checkpoint inhibitor (ICI)-based therapies are preferred treatment options for multiple cancer types. However, there is an unmet need for predictive tests that can identify patients likely to benefit. PROphet-NSCLC is a commercially available pretreatment plasma proteomic test that predicts clinical benefit from first-line PD-(L)1 inhibitor-based therapy in patients with metastatic non-small cell lung cancer (NSCLC), guiding the choice between ICI monotherapy versus ICI-chemotherapy. Here, we evaluate the broader clinical utility of PROphet-NSCLC by testing its predictive capabilities across diverse cancer types. Methods: Pre-treatment plasma samples and clinical data were collected for an observational study from patients with metastatic melanoma (n=68) and HPV-related cancers (n=43; NCT03427411) treated with PD-(L)1 inhibitor-based therapies. HPV-related cancers included anogenital squamous cell carcinoma (n=16), cervical carcinoma (n=18), and head and neck squamous cell carcinoma (n=9). Patients were assigned a PROphet-positive or -negative result based on computational analysis of SomaScan-derived proteomic profiles in pre-treatment plasma samples. Overall survival (OS) and progression-free survival (PFS) in PROphet-positive- and -negative groups was analyzed with the Kaplan-Meier method. Hazard ratios (HR) were calculated from univariate and multivariate Cox proportional hazard models. Results: In the melanoma cohort, PROphet-positive patients (n=51) displayed a significant OS benefit in comparison to PROphet-negative patients (n=17; median OS 92.8 months vs. 9.5 months, HR=0.14, 95% CI: 0.06-0.34, p&lt;0.0001), consistent with the clinically validated predictive performance of the test in patients with NSCLC. OS results remained significant after correcting for sex, age, histology, ECOG performance status, and treatment type (HR=0.05, 95% CI: 0.01-0.25, p&lt;0.001). In PROphet-positive patients, PD-1+CTLA-4 inhibitor combination therapy (n=27) was superior to PD-1 inhibitor monotherapy (n=24; OS: median 118.4 vs. 48.3 months, HR=0.58, p=0.24; PFS: median not reached vs. 10.8 months, HR=0.48, p=0.04). PROphet-negative patients displayed similarly poor outcomes with either treatment, providing a rationale to consider alternative therapies for such patients. In the HPV-related cohort, the median OS in PROphet-positive (n=10) vs PROphet-negative (n=33) groups was 43.6 vs 4.4 months (HR=0.22, 95% CI: 0.08-0.59, p=0.001), with similar trends per sub-cohort. Conclusions: Our findings show that the PROphet-NSCLC test can be applied to PD-(L)1 inhibitor-treated melanoma and HPV-related cancers, suggesting applicability to cancers beyond NSCLC. Analysis of additional patient samples is needed to explore the potential utility of PROphet-NSCLC for informing treatment decisions for a broad range of cancer types.

Harmonization radiomics model to predict immune checkpoint inhibitor-related pneumonitis (CIP) in patients with non-small cell lung cancer (NSCLC).

12142 Background: The increasing use of immunotherapy in advanced non-small cell lung cancer (NSCLC) presents a significant challenge in managing adverse events, particularly checkpoint inhibitor-associated pneumonitis (CIP). This potentially life-threatening complication often necessitates discontinuation of immunotherapy, even in patients experiencing tumor response. Presently, there are no reliable models to predict the onset of CIP. This research utilizes CT radiomics to create an innovative approach for anticipating the risk of CIP in patients with NSCLC. Methods: This IRB-approved, retrospective study analyzed data from 159 stage III-IV NSCLC patients undergoing immunotherapy. We categorized patients into pneumonitis (further subdivided into immunotherapy-induced, radiation-induced, and others) and non-pneumonitis groups. Using LIFEx software, we extracted 3D-radiomic features from both tumors and surrounding 1cm thick peritumoral regions. To address scanner-associated variations, a linear mixed-effect radiomics harmonization model was applied. A Random Forest algorithm was then used to develop a classification model predicting CIP occurrence based on the pre-treatment CT radiomic and clinical data. The dataset was split into training (70%) and test (30%) sets. The accuracy of predictions was evaluated using confusion matrix statistics and bootstrapping with 1,000 iterations for median and 95% confidence interval (CI). The performance metrics included sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the ROC curve (AUC). Results: 159 patients were analyzed, of which only 19 had checkpoint inhibitor-related pneumonitis. Among which, 17 (54.8%) patients had grade 1 pneumonitis, 12 (38.7%) patients had grade 2 pneumonitis, 2 (6.5%) patients had grade 3 pneumonitis, and none had grade 4 or grade 5 pneumonitis. The model achieved a sensitivity of 0.98 (0.97, 0.99), a specificity of 0.08 (0.05, 0.14), a PPV of 0.91 (0.90, 0.91), and an NPV of 0.33 ± 0.23 for predicting CIP. The AUC of 0.59 (0.56, 0.66) indicates that the model may predict checkpoint inhibitor-associated pneumonitis with an accuracy of 59%. Conclusions: The study provides insights into the potential of radiomic analysis coupled with AI algorithms and the use of harmonization model in predicting CIP among NSCLC patients treated with immunotherapy. However, larger studies are needed to validate our findings and the utility of harmonization models in predicting CIP.

MR radiomics and neoadjuvant chemo-responsiveness with insights into selective treatment de-intensification in HPV-positive oropharyngeal carcinoma.

6085 Background: In HPV-positive oropharyngeal squamous cell carcinoma (OPSCC), patients with good response to neoadjuvant chemotherapy (NAC) exhibit superior prognosis. Accurate prediction of NAC response allows for NAC candidate selection and personalized treatment de-intensification in HPV-positive OPSCC. In this study, we aimed to apply baseline magnetic resonance (MR) radiomic features to predict NAC response and prognosis. Methods: Pre-treatment MR images and clinical data of 131 patients with HPV-positive OPSCC were retrieved from Fudan University Shanghai Cancer Center. Radiomic features of both oropharyngeal lesions and metastatic nodes were extracted on T2WI and contrast-enhanced T1WI sequence. Patients were divided into training cohort (n=47), prospective validation cohort (n=49) and real-world validation cohort (n=35). Following radiomic feature selection, a linear support vector machine (SVM) model was built and validated for NAC response prediction. Nomograms that combined radiomics and clinical characteristics were then developed to predict survival outcomes. The performance of response models was assessed by the area under the curve (AUC), accuracy, sensitivity, specificity and prognostic models were measured by C-index. RNA-seq and proteomic data were further leveraged and compared to interpret the molecular features underlying radiomic signatures with differential NAC response. Results: For NAC response prediction, the fusion model with both oropharyngeal and nodal radiomic signatures on multi-sequence MR images achieved encouraging performance to predict good responders in the training cohort (AUC 0.89, 95% CI, 0.79-0.95) and prospective validation cohort (AUC 0.71, 95% CI, 0.59-0.83). For prognosis prediction, radiomics-based nomograms exhibited satisfactory discriminative ability between low-risk and high-risk patients in training cohort and two validation cohorts (PFS, C-index: 0.85, 0.76 and 0.83; OS, C-index: 0.79, 0.76 and 0.87). An exploratory analysis in the prospective validation cohort showed that de-intensified radiotherapy after NAC in low-risk patients yielded 100% in both PFS and OS. Furthermore, expression analysis unveiled distinct molecular phenotypes in relation to NAC response, where poor responders had predominantly enhanced keratinization while good responders were featured by stronger innate and adaptive immune response. Conclusions: The MR-based radiomic models and subsequent prognostic models efficiently discriminate among patients with different NAC response and survival risk. This study provides a new strategy for patient selection in HPV-positive OPSCC that are suitable for personalized de-intensification. Integration of radiomics in future trials is warranted.

Gamma Knife radiosurgery for the management of glomus jugulare tumors: A systematic review and report of the experience of a radioneurosurgery unit in Latin America.

Glomus jugulare tumors (GJTs) are rare and mainly affect women between the 5th and 6th decades of life. Its localization and anatomic relationships make conventional surgical treatment difficult and with a considerable risk of complications. This manuscript aims to describe the results of Gamma Knife radiosurgery (GKR) in patients with GJT treated in a single center in Latin America, as well as to systematically review the literature to determine the clinical and radiological effectiveness of this technique. A search of information from January 1995 to June 2023 was performed. Twenty-two articles reporting 721 GJT patients treated with GKR were included in the study. Variables such as symptomatic control, control of tumor size, and complications were evaluated. These variables were described using measures of central tendency and proportions. For the institutional experience, 77 patients with GJT tumors were included in the study. Pre-treatment clinical variables and follow-up data were collected from medical charts and phone interviews. The Short Form-36 scale was applied to assess the quality of life. The data were analyzed using the statistical program STATA17.0. A total of 721 patients were considered. The median of patients included in these studies was 18.5. The mean age was 58.4 years. The median of symptom control was 89%, and the median of imaging control was 95.7%. In our institution, 77 patients were included in the study. The mean age was 53.2 years. The median hospital stay was 4.92 hours. For the clinical follow-up, information on 47 patients was obtained. An improvement in pre-treatment symptoms was described in 58%, with general symptomatic control of 97%. The tumor-control rate was 95%, and there were statistically significant differences in six of the nine Short Form-36 scale domains. GKR is an effective, safe, and cost-effective technique that offers a high degree of symptomatic and tumor size control in patients with GJT.

Plasma Proteome-Based Test for First-Line Treatment Selection in Metastatic Non-Small Cell Lung Cancer.

Current guidelines for the management of metastatic non-small cell lung cancer (NSCLC) without driver mutations recommend checkpoint immunotherapy with PD-1/PD-L1 inhibitors, either alone or in combination with chemotherapy. This approach fails to account for individual patient variability and host immune factors and often results in less-than-ideal outcomes. To address the limitations of the current guidelines, we developed and subsequently blindly validated a machine learning algorithm using pretreatment plasma proteomic profiles for personalized treatment decisions. We conducted a multicenter observational trial (ClinicalTrials.gov identifier: NCT04056247) of patients undergoing PD-1/PD-L1 inhibitor-based therapy (n = 540) and an additional patient cohort receiving chemotherapy (n = 85) who consented to pretreatment plasma and clinical data collection. Plasma proteome profiling was performed using SomaScan Assay v4.1. Our test demonstrates a strong association between model output and clinical benefit (CB) from PD-1/PD-L1 inhibitor-based treatments, evidenced by high concordance between predicted and observed CB (R2 = 0.98, P < .001). The test categorizes patients as either PROphet-positive or PROphet-negative and further stratifies patient outcomes beyond PD-L1 expression levels. The test successfully differentiates between PROphet-negative patients exhibiting high tumor PD-L1 levels (≥50%) who have enhanced overall survival when treated with a combination of immunotherapy and chemotherapy compared with immunotherapy alone (hazard ratio [HR], 0.23 [95% CI, 0.1 to 0.51], P = .0003). By contrast, PROphet-positive patients show comparable outcomes when treated with immunotherapy alone or in combination with chemotherapy (HR, 0.78 [95% CI, 0.42 to 1.44], P = .424). Plasma proteome-based testing of individual patients, in combination with standard PD-L1 testing, distinguishes patient subsets with distinct differences in outcomes from PD-1/PD-L1 inhibitor-based therapies. These data suggest that this approach can improve the precision of first-line treatment for metastatic NSCLC.

Abstract TP144: Deep Learning on Pre-Procedural CT and Clinical Data Predicts Outcome Following Stroke Thrombectomy

Background: Deep learning using clinical and imaging data may improve pre-treatment prognostication in ischemic stroke patients undergoing endovascular thrombectomy (EVT). Methods: Deep learning models were trained and tested on pre-treatment clinical and imaging (CT head and CT angiography) data to predict 3-month functional outcomes in ischemic stroke patients who underwent EVT. Logistic regression models were constructed to compare their performance with the deep learning models. An external validation dataset was used to validate the models. The MR PREDICTS prognostic tool was tested on the external validation set, and its performance was compared with the deep learning and logistic regression models. Results: There were a total of 975 patients (550 men; mean±SD age 67.5±15.1 years), including 778 patients from the model development cohort, and 197 patients in the external validation cohort. The deep learning model trained on CT head and clinical data, and the logistic regression model (clinical data alone) demonstrated the strongest discriminative abilities for 3-month functional outcome and were comparable (AUC 0.811 versus 0.817, Q=0.82). Both models exhibited superior prognostic performance than the other deep learning (CT head alone, CT angiography alone) and MR PREDICTS models (all Q&lt;0.05). Conclusions: The discriminative performance of deep learning for predicting functional outcome following EVT was comparable to logistic regression. Future studies should focus on whether incorporating procedural and post-procedural data significantly improves model performance.

Benefit of Conversion Therapy in Patients with Unresectable Hepatocellular Carcinoma: A Propensity Score-Matched Study.

This study aimed to investigate the benefit of conversion therapy for patients with unresectable hepatocellular carcinoma (HCC). A retrospective cohort study was conducted involving 40 patients initially deemed unresectable HCC (uHCC). They received surgery following successful conversion therapy involving lenvatinib. The patients were matched in a 1:1 ratio to with a control group who underwent direct surgery, based on pre-treatment clinical data. The median recurrence-free survival (RFS) duration for the conversion therapy cohort was notably longer than that of the direct surgery cohort (25 months vs 11 months). Furthermore, the 1- and 2-year RFS rates were significantly higher in the conversion therapy group compared to the direct surgery group (1 year: 70.5% vs 40.1%; 2 years: 49.0% vs 19.1%). The survival curves indicated a statistically significantly longer RFS in the conversion therapy cohort compared to the direct surgery cohort (P = 0.007). While patients achieving good remission based on both RECIST 1.1 and mRECIST criteria showed superior median RFS, no significant disparity was observed in the survival curves. The subgroup analysis revealed significantly improved prognosis among patients in the conversion therapy group who were male, older, had a history of alcohol consumption, were non-smokers, had liver cirrhosis, possessed Child-Pugh A liver function, had a tumor diameter exceeding 5 cm, and had an AFP ≥ 400 ng/mL. Among the cohort of 40 patients, only 8 individuals encountered severe adverse reactions, which were managed through dose reduction. None of the patients experienced multiple severe adverse reactions concurrently. For patients with unresectable hepatocellular carcinoma, conversion therapy offers a significantly better prognosis than direct surgery for uHCC patients.

MRI features for prediction of the intravenous chemotherapy effect in patients with retinoblastoma

To investigate the value of orbital magnetic resonance imaging (MRI) features in predicting the efficacy of intravenous chemotherapy (IVC) for patients with retinoblastoma (RB). The pretreatment clinical and MRI data of 100 eyes from 80 RB patients who underwent IVC were collected retrospectively. There were 59 eyes in the effective group and 41 eyes in the ineffective group, and the baseline data of the two groups were compared statistically. Three radiologists reviewed and evaluated each lesion independently based on 25 MRI features. The predictive values of the MRI features for IVC efficacy were assessed by multi-factor logistic regression analysis, and their odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated. Receiver operating characteristic curves (ROCs) with the area under the curve (AUC) were used to determine the predictive abilities. A predictive model was constructed by integrating all independent predictors visualised by the nomogram. There were no statistically significant differences in sex or age between the effective and ineffective groups. The results of multivariate regression analysis showed that laterality, margin, and anterior eye segment enhancement were identified as independent factors that could predict IVC efficacy. The predictive model combining these three features was constructed, and it had an AUC of 0.732 (95% CI: 0.633, 0.831, p<0.01), a sensitivity of 71.2%, and a specificity of 70.7%. The data demonstrate that the orbital MRI features can be used to predict IVC efficiency before RB patients are treated.

Artificial Intelligence Predictive Model for Hormone Therapy Use in Prostate Cancer

BackgroundAndrogen deprivation therapy (ADT) with radiotherapy can benefit patients with localized prostate cancer. However, ADT can negatively impact quality of life, and there remain no validated predictive models to guide its use.MethodsWe used digital pathology images from pretreatment prostate tissue and clinical data from 5727 patients enrolled in five phase 3 randomized trials, in which treatment was radiotherapy with or without ADT, as our data source to develop and validate an artificial intelligence (AI)–derived predictive patient-specific model that would determine which patients would develop the primary end point of distant metastasis. The model used baseline data to provide a binary output that a given patient will likely benefit from ADT or not. After the model was locked, validation was performed using data from NRG Oncology/Radiation Therapy Oncology Group (RTOG) 9408 (n=1594), a trial that randomly assigned men to radiotherapy plus or minus 4 months of ADT. Fine–Gray regression and restricted mean survival times were used to assess the interaction between treatment and the predictive model and within predictive model–positive, i.e., benefited from ADT, and –negative subgroup treatment effects.ResultsOverall, in the NRG/RTOG 9408 validation cohort (14.9 years of median follow-up), ADT significantly improved time to distant metastasis. Of these enrolled patients, 543 (34%) were model positive, and ADT significantly reduced the risk of distant metastasis compared with radiotherapy alone. Of 1051 patients who were model negative, ADT did not provide benefit.ConclusionsOur AI-based predictive model was able to identify patients with a predominantly intermediate risk for prostate cancer likely to benefit from short-term ADT. (Supported by a grant [U10CA180822] from NRG Oncology Statistical and Data Management Center, a grant [UG1CA189867] from NCI Community Oncology Research Program, a grant [U10CA180868] from NRG Oncology Operations, and a grant [U24CA196067] from NRG Specimen Bank from the National Cancer Institute and by Artera, Inc. ClinicalTrials.gov numbers NCT00767286, NCT00002597, NCT00769548, NCT00005044, and NCT00033631.)

Association of Antifolate Response Signature Status and Clinical Activity of Pemetrexed-Platinum Chemotherapy in Non-Small Cell Lung Cancer: The Piedmont Study.

The Piedmont study is a prospectively designed retrospective evaluation of a new 48-gene antifolate response signature (AF-PRS) in patients with locally advanced/metastatic nonsquamous (NS) non-small cell lung cancer (NSCLC) treated with pemetrexed-containing platinum doublet chemotherapy (PMX-PDC). The study tested the hypothesis that AF-PRS identifies patients with NS-NSCLC who have a higher likelihood of responding positively to PMX-PDC. The goal was to gather clinical evidence supporting AF-PRS as a potential diagnostic test. Residual pretreatment FFPE tumor samples and clinical data were analyzed from 105 patients treated with first-line (1L) PMX-PDC. Ninety-five patients had sufficient RNA sequencing (RNA-seq) data quality and clinical annotation for inclusion in the analysis. Associations between AF-PRS status and associate genes and outcome measures including progression-free survival (PFS) and clinical response were evaluated. Overall, 53% of patients were AF-PRS(+), which was associated with extended PFS, but not overall survival, versus AF-PRS(-) (16.6 months vs. 6.6 months; P = 0.025). In patients who were stage I to III patients at the time of treatment, PFS was further extended in AF-PRS(+) versus AF-PRS(-) (36.2 months vs. 9.3 months; P = 0.03). Complete response (CR) to therapy was noted in 14 of 95 patients. AF-PRS(+) preferentially selected a majority (79%) of CRs, which were evenly split between patients stage I to III (six of seven) and stage IV (five of seven) at the time of treatment. AF-PRS identified a significant population of patients with extended PFS and/or clinical response following PMX-PDC treatment. AF-PRS may be a useful diagnostic test for patients indicated for systemic chemotherapy, especially when determining the optimal PDC regimen for locally advanced disease.

Based on CT radiomics model for predicting the response to first-line chemotherapy of diffuse large B-cell lymphoma

Objective: To investigate the potential value of CT Radiomics model in predicting the response to first-line chemotherapy in diffuse large B-cell lymphoma (DLBCL). Methods: Pre-treatment CT images and clinical data of DLBCL patients treated at Shanxi Cancer Hospital from January 2013 to May 2018 were retrospectively analyzed and divided into refractory patients (73 cases) and non-refractory patients (57 cases) according to the Lugano 2014 efficacy evaluation criteria. The least absolute shrinkage and selection operator (LASSO) regression algorithm, univariate and multivariate logistic regression analyses were used to screen out clinical factors and CT radiomics features associated with efficacy response, followed by radiomics model and nomogram model. Receiver operating characteristic (ROC) curve, calibration curve and clinical decision curve were used to evaluate the models in terms of the diagnostic efficacy, calibration and clinical value in predicting chemotherapy response. Results: Based on pre-chemotherapy CT images, 850 CT texture features were extracted from each patient, and 6 features highly correlated with the first-line chemotherapy effect of DLBCL were selected, including 1 first order feature, 1 gray level co-occurence matrix, 3 grey level dependence matrix, 1 neighboring grey tone difference matrix. Then, the corresponding radiomics model was established, whose ROC curves showed AUC values of 0.82 (95% CI: 0.76-0.89) and 0.73 (95% CI: 0.60-0.86) in the training and validation groups, respectively. The nomogram model, built by combining validated clinical factors (Ann Arbor stage, serum LDH level) and CT radiomics features, showed an AUC of 0.95 (95% CI: 0.90-0.99) and 0.91 (95% CI: 0.82-1.00) in the training group and the validation group, respectively, with significantly better diagnostic efficacy than that of the radiomics model. In addition, the calibration curve and clinical decision curve showed that the nomogram model had good consistency and high clinical value in the assessment of DLBCL efficacy. Conclusion: The nomogram model based on clinical factors and radiomics features shows potential clinical value in predicting the response to first-line chemotherapy of DLBCL patients.

Effects of dexmedetomidine on oxidative stress, programmed cell death, liver function, and expression of peripheral immune cells in patients with primary liver cancer undergoing hepatectomy.

Study background: Primary liver cancer is a severe health issue that imposes a significant health burden on families. Oxidation and subsequent cell death impair liver function and provoke an immune response. The present article investigates the effect of Dexmedetomidine on oxidation, cell death, the expression of peripheral immune cells, and liver function. The clinical data will represent the facts and evidence of the effects of this intervention. Methods: We analyzed clinical data reporting various accounts of the effects of Dexmedetomidine on oxidation, cell death, the expression of peripheral immune cells, and liver function among patients who underwent hepatectomy. The surgical procedure reported the differences in cell death as procedural outcomes among pre- and post-treatment records were compared and contrasted. Results: We found decreased cell apoptosis in the treatment group: the number of incisions to remove dead cells was lower in the treatment group than in the pre-treatment group. Likewise, lower oxidation was reported in pre-treatment than in post-treatment records. The expression of peripheral immune cells was higher in the pre-treatment clinical data than in post-treatment, suggesting a reduction in oxidation following dexmedetomidine treatment. Liver function was a function of oxidation and cell death outcomes. In the pre-treatment clinical data, liver function was poor, whereas improved functions were reported in the post-treatment clinical data. Discussion: We found compelling evidence of Dexmedetomidine's effects on oxidative stress and programmed cell death. The intervention suppresses the production of reactive oxygen species and the consequential apoptosis. Additionally, liver functions improve due to the decrease in hepatocyte apoptosis. Since the peripheral immune cells are expressed against tumors, a decrease in the progression of primary liver cancer decreased the expression of the peripheral immune cells. Conclusion: Dexmedetomidine's positive effects stood out in the present research article. The intervention reduced oxidation by balancing the production of reactive oxygen species and the detoxification processes. Reduced oxidation induced reduced cell death through apoptosis, resulting in a low expression of peripheral immune cells and improved liver functions.

Abstract P4-07-34: CO2 laser, radiofrequency, and promestriene in treatment of genitourinary syndrome of menopause in breast cancer survivors. Clinical and histological aspects of a randomized control trial

Abstract Background: 74% of women have urogenital symptoms classified as Genito-urinary Syndrome of menopause (GSM) during breast cancer treatment. Promestriene is effective and safe in the treatment of GSM. However, some women and oncologists are not comfortable with long-term use. Therefore, new options for and alleviating GSM have been considered, such as microablative fractional CO2 laser (CO2L) and microablative fractional radiofrequency (RF). Objective: To compare the effect of promestriene, CO2L, and RF treatments of GSM in women with breast cancer in the use of antiestrogens therapy, concerning clinical and histological findings of vulvar vestibule. Methods: This is a secondary analysis of a Multi-arm randomized controlled trial (NCT04081805). Were eligible for the study 100 women with breast cancer using adjuvant endocrine therapy referring moderate to severe symptoms of GSM (itching, dyspareunia, fissures, thinning of vaginal rugae, and tropism reduction). After providing written informed consent, they were evaluated according to pre and post-treatmentpost-treatment protocol by filling the VAS of GSM and by clinical evaluation including a standardized gynecological exam with vestibular biopsy. Women were then randomized to either CO2L, RF or promestriene groups. The CO2L and RF groups received 3 consecutive monthly outpatient vulvovaginal energy applications. The CT was oriented for domiciliary use of promestriene, 1g/d for 21 days and, subsequently, twi4 month seek for 4 months. The follow-up visit was performed 120 days after interventions and also included an evaluation of global patient impression of improvement (5 points Likert scale). Results: 94 women were randomized as follows: 32 in the CO2L group, 32 in the RF and 30 in the promestriene. 70 patients concluded the treatment and had adequate pre and post-treatment material to analyze, 23 CO2L, 21 RF and 26 promestriene. Pre-treatment demographic and clinical data are presented on table 1. The evolution of GSM according to each treatment is demonstrated on table 2. Was also reported a high satisfaction after the treatment protocol in all groups evaluated by Likert Scale:CO2L 4,783 (0,518), RF 4,150 (0,813), CT 4,280 (1.13), p=0,055. The histological parameters were presented on table 3. Histological atrophy was surprisingly identified in only 4 women (5.7%), pre-treatment. No injuries to the histological structure of the vulvar vestibule or relevant clinical adverse events were identified post-treatments. Conclusion: CO2L, RF, and promestriene provided significant and similar improvement of GSM in women with breast cancer using anti-estrogens. The use of energies did not cause structural tissue damage or relevant clinical complications. Table 1. Clinical and demographic parameters before treatment Mean (standard deviation); BMI body mass index, Tukey test: Similar letters (a, b) mean similar results. Chi-Square Table 2. Evolution of GSM (VAS) after treatment with CO2L, RF, and promestriene Mixed ANOVA. VAS - Visual Analog Scale of GSM Symptoms, values expressed in mean (SD) Table 3. Histological parameters according groups and pre and post-treatment Citation Format: Angela Flavia L. Waitzberg, Andreia Fabiana V. Franco, Ana Maria Bianchi-Ferraro, Roberto Marcela, Gabriela Cantarelli, Chayane Dedonatto, Marisa Patriarca, Rita Dardes, Neila Speck, Zsuzsanna Jarmy-di Bella, Marair Sartotti, Joaquim Almeida. CO2 laser, radiofrequency, and promestriene in treatment of genitourinary syndrome of menopause in breast cancer survivors. Clinical and histological aspects of a randomized control trial [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P4-07-34.

Discontinuing Hepatitis Activity Reduced Hepatocellular Carcinoma Recurrence after Primary Curative Therapy

Hepatocellular carcinoma (HCC) tends to recur after curative treatment. This study aimed to identify the clinical factors associated with HCC recurrence after initial curative therapy. We retrospectively included patients with early stage HCC Barcelona Clinic Liver Cancer (BCLC) stages 0 and A who received curative surgical resection or local ablation at three different Chang Gung Memorial Hospitals in Taiwan (527 patients from Linkou, 150 patients from Keelung, and 127 patients from Chiayi) from 2000 to 2009. Pretreatment clinical data were subjected to univariate and multivariate logistic analyses to identify the risk factors for HCC recurrence within five years after the primary curative treatment. Recurrence and survival rates were assessed using Kaplan-Meier curves and log-rank tests. Patients with a history of nucleoside analog or peg-interferon treatment for hepatitis B or hepatitis C infection had lower HCC recurrence rates than did those without such treatment. By contrast, alcohol drinking habits (p = 0.0049, hazard ratio (HR): 1.508, 95%CI: 1.133-2.009), a platelet count of < 14 × 104/μL (p = 0.003, HR: 1.533, 95%CI: 1.155-2.035), and a serum alanine aminotransferase level > 40 U/L (p = 0.0450, HR: 1.305, 95%CI: 1.006-1.694) were independent risk factors for HCC recurrence. The five-year HCC recurrence rates did not differ between patients who received either local radiofrequency ablation or surgical resection at BCLC stages 0 and A. Factors contributing to persistent hepatitis activity and advanced fibrosis precipitate tumor recurrence. Active intervention to discontinue liver injury or hepatitis could reduce HCC recurrence.