Predictor Of Progression-free Survival Research Articles

Extracellular volume-based scoring system for tracking tumor progression in pancreatic cancer patients receiving intraoperative radiotherapy

ObjectivesTo investigate the value of extracellular volume (ECV) derived from portal-venous phase (PVP) in predicting prognosis in locally advanced pancreatic cancer (LAPC) patients receiving intraoperative radiotherapy (IORT) with initial stable disease (SD) and to construct a risk-scoring system based on ECV and clinical-radiological features.Materials and methodsOne hundred and three patients with LAPC who received IORT demonstrating SD were enrolled and underwent multiphasic contrast-enhanced CT (CECT) before and after IORT. ECV maps were generated from unenhanced and PVP CT images. Clinical and CT imaging features were analyzed. The independent predictors of progression-free survival (PFS) determined by multivariate Cox regression model were used to construct the risk-scoring system. Time-dependent receiver operating characteristic (ROC) curve analysis and the Kaplan–Meier method were used to evaluate the predictive performance of the scoring system.ResultsMultivariable analysis revealed that ECV, rim-enhancement, peripancreatic fat infiltration, and carbohydrate antigen 19-9 (CA19-9) response were significant predictors of PFS (all p < 0.05). Time-dependent ROC of the risk-scoring system showed a satisfactory predictive performance for disease progression with area under the curve (AUC) all above 0.70. High-risk patients (risk score ≥ 2) progress significantly faster than low-risk patients (risk score < 2) (p < 0.001).ConclusionECV derived from PVP of conventional CECT was an independent predictor for progression in LAPC patients assessed as SD after IORT. The scoring system integrating ECV, radiological features, and CA19-9 response can be used as a practical tool for stratifying prognosis in these patients, assisting clinicians in developing an appropriate treatment approach.Critical relevance statementThe scoring system integrating ECV fraction, radiological features, and CA19-9 response can track tumor progression in patients with LAPC receiving IORT, aiding clinicians in choosing individual treatment strategies and improving their prognosis.Key PointsPredicting the progression of LAPC in patients receiving IORT is important.Our ECV-based scoring system can risk stratifying patients with initial SD.Appropriate prognostication can assist clinicians in developing appropriate treatment approaches.Graphical

A retrospective study of clinical outcomes for patients with esophageal cancer who were treated with radiotherapy alone

Introduction: Patients with esophageal cancer who are in a poor general condition receive radiotherapy alone, but outcomes are often unsatisfactory. The aim of this study was to clarify recent outcomes of radiotherapy alone for esophageal cancer. Methods: Patients who underwent 50 Gy or more of radiotherapy without chemotherapy were retrospectively reviewed. Endpoints were overall survival (OS), disease-specific survival (DSS), local control (LC), and progression-free survival (PFS). Survival curves were drawn using the Kaplan-Meier method and predictors were analyzed using the Cox proportional hazards model. Results: Sixty-nine patients were included. The median follow-up period was 17.9 months. The 5-year OS, DSS, LC, and PFS rates were 33.2%, 49.8%, 46.2%, and 16.8%, respectively. In the multivariate Cox proportional hazard model, clinical stage was a significant predictor for OS (hazard ratio [HR]: 4.42, 95% confidence interval [CI]: 1.80 – 11.17, p = 0.001), DSS (HR: 2.08, 95% CI: 1.43 – 3.12, p = 0.0001), LC (HR: 1.86, 95% CI: 1.28 – 2.74, p = 0.001), and PFS (HR: 1.65, 95% CI: 1.25 – 2.18, p = 0.0004). Radiation dose was a significant predictor for LC (HR: 0.87, 95% CI: 0.78 – 0.97, p = 0.018) and tumor location was a significant predictor for PFS (HR: 1.55, 95% CI: 1.10 – 2.19, p = 0.018). In subgroup analysis, the 5-year OS, DSS, LC, and PFS rates for stage I were 60.0%, 80.0%, 71.9%, and 46.1%, respectively. Conclusions: Stage, radiation dose and tumor location are significant predictors for outcomes. Patients with stage I esophageal cancer can be cured by radiotherapy alone.

Prognostic value of combining clinical factors, 18F-FDG PET-based intensity, volumetric features, and deep learning predictor in patients with EGFR-mutated lung adenocarcinoma undergoing targeted therapies: a cross-scanner and temporal validation study.

To investigate the prognostic value of 18F-FDG PET-based intensity, volumetric features, and deep learning (DL) across different generations of PET scanners in patients with epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma receiving tyrosine kinase inhibitor (TKI) treatment. We retrospectively analyzed the pre-treatment 18F-FDG PET of 217 patients with advanced-stage lung adenocarcinoma and actionable EGFR mutations who received TKI as first-line treatment. Patients were separated into analog (n = 166) and digital (n = 51) PET cohorts. 18F-FDG PET-derived intensity, volumetric features, ResNet-50 DL of the primary tumor, and clinical variables were used to predict progression-free survival (PFS). Independent prognosticators were used to develop prediction model. Model was developed and validated in the analog and digital PET cohorts, respectively. In the analog PET cohort, female sex, stage IVB status, exon 19 deletion, SUVmax, metabolic tumor volume, and positive DL prediction independently predicted PFS. The model devised from these six prognosticators significantly predicted PFS in the analog (HR = 1.319, p < 0.001) and digital PET cohorts (HR = 1.284, p = 0.001). Our model provided incremental prognostic value to staging status (c-indices = 0.738 vs. 0.558 and 0.662 vs. 0.598 in the analog and digital PET cohorts, respectively). Our model also demonstrated a significant prognostic value for overall survival (HR = 1.198, p < 0.001, c-index = 0.708 and HR = 1.256, p = 0.021, c-index = 0.664 in the analog and digital PET cohorts, respectively). Combining 18F-FDG PET-based intensity, volumetric features, and DL with clinical variables may improve the survival stratification in patients with advanced EGFR-mutated lung adenocarcinoma receiving TKI treatment. Implementing the prediction model across different generations of PET scanners may be feasible and facilitate tailored therapeutic strategies for these patients.

Predictive potential of preoperative Naples prognostic score-based nomogram model for the prognosis in surgical resected thoracic esophageal squamous cell carcinoma patients: A retrospective cohort study.

The present study aimed to establish an effective prognostic nomogram model based on the Naples prognostic score (NPS) for resectable thoracic esophageal squamous cell carcinoma (ESCC). A total of 277 patients with ESCC, who underwent standard curative esophagectomy and designated as study cohort, were retrospectively analyzed. The patients were divided into different groups, including NPS 0, NPS 1, NPS 2, and NPS 3 or 4 groups, for further analysis, and the results were validated in an external cohort of 122 ESCC patients, who underwent surgery at another cancer center. In our multivariate analysis of the study cohort showed that the tumor-node-metastasis (TNM) stage, systemic inflammation score, and NPS were the independent prognostic factors for the overall survival (OS) and progression-free survival (PFS) durations. In addition, the differential grade was also an independent prognostic factor for the OS in the patients with ESCC after surgery (all P < .05). The area under the curve of receiver operator characteristics for the PFS and OS prediction with systemic inflammation score and NPS were 0.735 (95% confidence interval [CI] 0.676-0.795, P < .001) and 0.835 (95% CI 0.786-0.884, P < .001), and 0.734 (95% CI 0.675-0.793, P < .001) and 0.851 (95% CI 0.805-0.896, P < .001), respectively. The above independent predictors for OS or PFS were all selected in the nomogram model. The concordance indices (C-indices) of the nomogram models for predicting OS and PFS were 0.718 (95% CI 0.681-0.755) and 0.669 (95% CI 0.633-0.705), respectively, which were higher than that of the 7th edition of American Joint Committee on Cancer TNM staging system [C-index 0.598 (95% CI 0.558-0.638) for OS and 0.586 (95% CI 0.546-0.626) for PFS]. The calibration curves for predicting the 5-year OS or PFS showed a good agreement between the prediction by nomogram and actual observation. In the external validation cohort, the nomogram discrimination for OS was better than that of the 7th edition of TNM staging systems [C-index: 0.697 (95% CI 0.639-0.755) vs 0.644 (95% CI 0.589-0.699)]. The calibration curves showed good consistency in predicting the 5-year survival between the actual observation and nomogram predictions. The decision curve also showed a higher potential of the clinical application of predicting the 5-years OS of the proposed nomogram model as compared to that of the 7th edition of TNM staging systems. The preoperative NPS-based nomogram model had a certain potential role for predicting the prognosis of ESCC patients.

Abstract PS06-04: The prognostic and predictive impact of circulating tumour DNA (ctDNA) dynamics in patients with metastatic Triple Negative Breast Cancer (TNBC) on olaparib based therapy: Results from Cohort E of the PlasmaMATCH trial

Abstract Background: Early changes in ctDNA levels, ctDNA dynamics, may help identify which patients are responding to therapy earlier than imaging. Few studies have assessed ctDNA dynamics during PARP inhibitor therapy. Here we report paired baseline and early on treatment ctDNA analysis from cohort E of plasmaMATCH, that recruited patients with TNBC to treatment with olaparib (PARP inhibitor) plus ceralasertib (ATR inhibitor). Methods: The plasmaMATCH trial assessed the ability of ctDNA testing to allocate patients to mutation matched treatment cohorts (A-D). Patients with TNBC, and without mutations matching cohorts B-D, were enrolled on cohort E. Samples were collected for ctDNA analysis pre-treatment at cycle 1-day1 (C1D1) and cycle 2-day 1 (C2D1). A minimum of 14 days of treatment in the first cycle was required for inclusion in this analysis. Samples were sequenced using error-corrected gene targeted panels (Guardant360, or GuardantOMNI, Guardant Health). Circulating DNA ratio (CDR) was calculated as the ratio of C2D1 ctDNA level to C1D1, pre-specified using the weighted mean of variant allele fractions (AF) of clonal mutations at C1D1, excluding variants with AF &amp;lt; 0.3%, and variants in genes frequently mutated in clonal haematopoesis (GNAS, JAK2, IDH1, IDH2 and ATM). The optimal cut-point for predicting progression free survival (PFS) was assessed as the cut-point with the highest Harrell’s C-index. Results: Of the 75 patients that were enrolled into cohort E, 53 patients had samples sent for paired C1D1-C2D1 ctDNA sequencing, 2 failed sequencing, and all 51 (68%) patients had detectable ctDNA at C1D1. The ctDNA analysis set was representative of the overall enrolled population. The optimal ctDNA dynamics C-index cut-point for predicting PFS was 0 (undetectable ctDNA at C2D1). Median PFS with ctDNA CDR &amp;gt;0 (detectable ctDNA at C2D1) was 4.3 months (95% CI 2.4-5.8), and with undetectable ctDNA was 12 months (95% CI 8-NA) (HR 4.02, 95% CI 1.22-13.23, p=0.01). Splitting patients by median CDR was not predictive (HR=0.98; 95%CI: 0.52-1.82, p=0.94). Confirmed objective response rate was 85.7% (42.1-99.6) in patients with undetectable ctDNA at C2D1, and with detectable ctDNA was 11.4% (3.8-24.6) (OR 4.02, 95% CI 1.22-13.23, p=0.01). Of the 7 patients with undetectable ctDNA at C2D1, one had a BRCA2 germline mutation, and all other patients were wildtype for BRCA1/2 mutations in tumour and germline. All patients with undetectable ctDNA and BRCA1/2 wildtype had a confirmed response.In cohorts A-D (mutation targeted therapies, in predominantly ER positive cancer), the optimal ctDNA dynamics C-index CDR cut-point was 0.165 (HR 3.44, 95%CI: 2.06-5.75, p&amp;lt; 0.001), with median CDR cut-point also highly predictive (HR=2.14, 95%CI:1.36-3.36, p=0.001). Undetectable ctDNA was also strongly predictive (HR=4.41; 95%CI: 1.97-9.87, p&amp;lt; 0.001) in cohort A-D.In cohort E, a significant association was found between baseline ctDNA and PFS, with an optimal C-index cutpoint of 6.81% (HR=3.02, 95% CI: 1.39-6.56, p=0.001). Median PFS for baseline ctDNA ≤ 6.81% was 10.2 months (95% CI 3.7-17.2), and for baseline ctDNA &amp;gt;6.81% was 4.4 months (95% CI 2.2-5.5). Conclusions: ‘Clearance’ of ctDNA to become undetectable at C2D1 identified sporadic TNBC patients who benefited from olaparib and ceralasertib. Although ‘clearance’ of ctDNA was associated with good outcome on olaparib plus ceralasertib, median CDR was not predictive of treatment benefit. This contrasts the results of ctDNA dynamic assessment of cohort A-D, where median CDR was highly predictive of treatment benefit. ctDNA dynamic assessment may differ between mutation targeted therapies (cohorts A-D) that induce cell-cycle arrest, and PARP inhibitors (cohort E) that inhibit DNA repair mechanisms. Implementing ctDNA dynamics into clinical trials and care may require distinct analysis for different therapies. Citation Format: Iseult Browne, Javier Pascual, Rosalind Cutts, Belinda Kingston, Sarah Hrebien, Lucy Kilburn, Alex Pearson, Laura Moretti, Andrew Wardley, Iain Macpherson, Richard Baird, Rebecca Roylance, Iris Faull, Kimberly C Banks, Isaac Garcia-Murillas, Judith Bliss, Alistair Ring, Nicholas Turner. The prognostic and predictive impact of circulating tumour DNA (ctDNA) dynamics in patients with metastatic Triple Negative Breast Cancer (TNBC) on olaparib based therapy: Results from Cohort E of the PlasmaMATCH trial [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS06-04.

Abstract PO2-27-05: Composite biomarkers for the prediction of progression-free survival with CDK4/6 inhibitors in metastatic HR+/HER2- breast cancer

Abstract CDK4/6 inhibitors are widely used in combination with endocrine therapy in the treatment of HR+/HER2- metastatic breast cancer. Generally the CDK4/6 inhibitors lead to a doubling of progression-free survival (PFS) over single agent endocrine therapy. In spite of a known mechanism of action, biomarkers to define the duration of response to CDK4/6 inhibitors with either aromatase inhibitors (AI) or fulvestrant (FUL) remain lacking. A cohort of over 300 patients receiving standard of care CDK4/6 inhibitor combination therapy for metastatic disease were consented for participation (NCT04526587). The duration of PFS in both AI and FUL treated sub-groups were comparable to that observed in randomized clinical trials. In this cohort progesterone receptor-status (HR 0.70, p=0.0123), visceral disease (HR 1.55, p=0.0013), prior endocrine therapy (HR 2.34, p&amp;lt; 0.001), and type of endocrine therapy (HR 2.16, p&amp;lt; 0.001) were associated with duration of PFS. To develop biomarkers, gene expression analyses was performed on 313 tumor samples representing over 200 individual patients. Focusing on the pre-treatment metastatic biopsy, established signatures associated with prognosis in HR+/HER2- breast cancer were employed to determine association with response to combination therapy in the metastatic setting. Proliferation-associated signatures were associated with shorter PFS. For example, an established RB loss signature was associated with PFS in both AI (HR 1.9, p=0.009) and FUL (HR 2.8, p=0.007) treated patients. Clinically employed signatures (e.g. OncotypeDx and Mammaprint) harbored variable associations with outcomes. Random Survival Forest feature selection was applied to individual and combined prognostic signatures. This approach resulted in a potent classifier for progression-free survival (HR 10, p&amp;lt; 1E-20). To enable applicability to different manifestations of disease, significant clinical pathological variables were incorporated into the model so that there was equivalent performance in AI and FUL treated patients for prediction of PFS. This predictive algorithm was also strongly associated with overall survival (HR 4.6, p=2.7E-8). Ongoing studies are evaluating the algorithms prospectively in NCT04526587. Citation Format: Agnieszka Witkiewicz, Jianxin Wang, Erik Knudsen, Ellis Levine, Tracey O'Connor. Composite biomarkers for the prediction of progression-free survival with CDK4/6 inhibitors in metastatic HR+/HER2- breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-27-05.

Maintenance treatment of immunotherapy after microwave ablation plus drug-eluting bead bronchial arterial chemoembolization for advanced non-small cell lung cancer: a retrospective single-center cohort study.

The combination therapy of immunotherapy and drug-eluting bead bronchial artery chemoembolization (DEB-BACE) or microwave ablation (MWA) has been attempted as an effective and safe approach for advanced non-small cell lung cancer (NSCLC). However, the outcomes of immunotherapy plus multiple interventional techniques for advanced NSCLC remain unclear. This retrospective study thus aimed to investigate the effectiveness and safety of the maintenance treatment of programmed cell death protein 1 (PD-1) blockade after MWA plus DEB-BACE for advanced NSCLC. This retrospective cohort study consists of 95 patients with advanced NSCLC who were treated with DEB-BACE between April 2017 and October 2022 and who were allocated to three groups: group A (MWA + DEB-BACE + PD-1 blockade; n=15), group B (MWA + DEB-BACE; n=25), and group C (DEB-BACE alone; n=55). The adverse events (AEs) were compared between the three groups. The outcomes were compared via Kaplan-Meier methods, including median progression-free survival (PFS) and overall survival (OS). Survival analyses were performed via the univariate and multivariate analyses to investigate the prognostic predictors. The overall incidence of AEs in the groups A-C was 53.3% (8/15), 36.0% (9/25), and 32.7% (18/55), respectively, which did not represent a significant difference (P=0.42). No severe AEs (SAEs) occurred. Group A, compared with group B and group C, had a significantly longer estimated median PFS (33.0 vs. 7.0 vs. 3.0 months; P<0.001) and OS (33.0 vs. 13.0 vs. 6.0 months; P=0.002). PD-1 blockade (P=0.006), tumor number (P=0.01), and DEB-BACE/bronchial artery infusion (BAI) chemotherapy cycles (P=0.04) were identified as the predictors of PFS, while the predictors of OS were PD-1 blockade (P<0.001), number of metastases (P<0.001), tumor diameter (P<0.001), and DEB-BACE/BAI cycles (P=0.02). Compared with that of advanced NSCLC treated with MWA plus DEB-BACE or DEB-BACE alone, the maintenance treatment of immunotherapy after MWA plus DEB-BACE might provide a superior prognosis without increasing the risk of AEs.

Real-World Data Analysis of CDK4/6 Inhibitor Therapy-A Patient-Centric Single Center Study.

The quest to comprehend the real-world efficacy of CDK4/6 inhibitors (CDKis) in breast cancer continues, as patient responses vary significantly. This single-center retrospective study evaluated CDKi use outside the trial condition from November 2016 to May 2020. Progression-free survival (PFS), time-to-treatment failure (TTF), short-term and prolonged treatment benefit (≥4 and ≥10 months), as well as prognostic and predictive markers were assessed with Kaplan-Meier and multivariate regression analyses. Out of 86 identified patients, 58 (67.4%) had treatment failure of which 40 (46.5%) were due to progression. Median PFS and TTF were 12 and 8.5 months, respectively. A total of 57 (66.3%) and 42 (48.8%) patients experienced short-term and prolonged treatment benefit. Independent, significant predictors for PFS were progesterone receptor expression (HR: 0.88), multiple metastatic sites (HR: 2.56), and hepatic metastasis (HR: 2.01). Significant predictors for TTF were PR expression (HR: 0.86), multiple sites (HR: 3.29), adverse events (HR: 2.35), and diabetes (HR: 2.88). Aside from tumor biology and adverse events, treatment modifications like pausing and switching of CDKi were predictive for short-term (OR: 6.73) and prolonged (OR: 14.27) therapeutic benefit, respectively. These findings emphasize the importance of tailored treatment strategies, highlighting the role of PR expression, metastatic burden, and therapeutic adjustments in optimizing patient outcomes in real-world breast cancer management.

The FJQR Has Synergistic Effect with Fluoropyrimidine in the Maintenance Treatment for HER-2 Negative Gastric Cancer.

Maintenance therapy aimed to strengthen the first-line chemotherapy and improve quality of life is needed for gastric cancer (GC). Currently, many clinical studies have confirmed the important role of fluoropyrimidine in the maintenance stage. Our team has created patented prescriptions "Fuzheng jiedu Quyu Method" recipe (FJQR), which was considered as an adjuvant therapeutic scheme (reduce toxicity and increase the efficacy of chemotherapy). This study aimed to evaluate the efficacy and safety of FJQR combined with fluoropyrimidine as a maintenance treatment in HER-2 negative GC patients. We performed the analysis of 129 patients with HER-2 negative GC who entered the maintenance stage in our hospital and Tianjin Cancer Hospital between January 2018 and December 2020. Out of the 129 eligible patients, 64 were categorized into the maintenance treatment group with FJQR+fluoropyrimidine, and 65 patients were assigned to the control group if they received fluoropyrimidine alone. Capecitabine was orally 1000mg/m2, Qd, half an hour after meals, and FGQR was 15g Bid after capecitabine. The primary endpoint was progression-free survival (PFS). The secondary endpoints were overall survival (OS), overall remission rate (ORR), quality of Life (QOL), TCM syndrome and safety. The mPFS in the treatment group was significantly prolonged compared with the control group (6.3 vs. 5.0 months, p = 0.03), while the mOS was not substantially improved (11.4 vs. 10.5 months, p = 0.38). Gastrointestinal symptoms and pain became better in the treatment group. The number of distant metastatic organs, first-line chemotherapy cycles, and lymph node metastasis were independent risk predictors for PFS. Blood stasis syndrome may be the protective factor. In terms of safety, treatment-related adverse events (AEs) in the treatment group were relatively lighter, and the incidence of grade III-IV AEs could be significantly reduced. FJQR and fluoropyrimidine have synergistic effects as maintenance treatment in HER-2 negative GC, with good efficacy and safety.

Prognostic Significance of the Bone Marrow-to-Aorta Uptake Ratio on 2-Deoxy-2-[18F]fluoro-d-glucose Positron Emission Tomography/Computed Tomography in Patients with Cholangiocarcinoma

2-Deoxy-2-[18F]fluoro-d-glucose (FDG) uptake of the reticuloendothelial system on positron emission tomography/computed tomography (PET/CT) is known to be related to systemic inflammatory response to cancer cells in patients with diverse malignancies. This retrospective study aimed to investigate whether FDG uptake by the reticuloendothelial system had a prognostic value in predicting progression-free survival (PFS) and overall survival (OS) in 138 cholangiocarcinoma patients. Quantifying FDG uptake of the aorta, bone marrow (BM), liver, and spleen from staging FDG PET/CT images, we found significant correlations between the BM-to-aorta uptake ratio (BAR), spleen-to-aorta uptake ratio, and BM-to-liver uptake ratio with tumor stage and serum inflammatory markers. In the multivariate survival analysis, BAR was an independent predictor of PFS (p = 0.016; hazard ratio, 2.308) and OS (p = 0.030; hazard ratio, 2.645). Patients with stages III–IV of the disease and a high BAR exhibited low 1-year PFS (35.8%) and OS (60.2%) rates, while those with stages I–II of the disease and low BAR showed robust rates of 90.0% and 96.7%, respectively. BAR measured on staging FDG PET/CT might be a potential imaging biomarker offering insights into the systemic inflammatory response and predicting prognosis in cholangiocarcinoma. This study highlights BAR as a promising, independent predictor with potential for personalized prognostication and treatment strategies.

Exploratory study of using Magnetic resonance Prognostic Imaging markers for Radiotherapy In Cervix cancer (EMPIRIC): a prospective cohort study protocol

IntroductionRadical chemoradiotherapy represents the gold standard for locally advanced cervical cancer. However, despite significant progress in improving local tumour control, distant relapse continues to impact overall survival. The development of...

Prognosis Prediction of Diffuse Large B-cell Lymphoma in 18F-FDG PET images Based on Multi-Deep-Learning Models.

Diffuse large B-cell lymphoma (DLBCL), a cancer of B cells, has been one of the most challenging and complicated diseases because of its considerable variation in clinical behavior, response to therapy, and prognosis. Radiomic features from medical images, such as PET images, have become one of the most valuable features for disease classification or prognosis prediction using learning-based methods. In this paper, a new flexible ensemble deep learning model is proposed for the prognosis prediction of the DLBCL in 18F-FDG PET images. This study proposes the multi-R-signature construction through selected pre-trained deep learning models for predicting progression-free survival (PFS) and overall survival (OS). The proposed method is trained and validated on two datasets from different imaging centers. Through analyzing and comparing the results, the prediction models, including Age, Ann abor stage, Bulky disease, SUVmax, TMTV, and multi-R-signature, achieve the almost best PFS prediction performance (C-index: 0.770, 95% CI: 0.705-0.834, with feature adding fusion method and C-index: 0.764, 95% CI: 0.695-0.832, with feature concatenate fusion method) and OS prediction (C-index: 0.770 (0.692-0.848) and 0.771 (0.694-0.849)) on the validation dataset. The developed multiparametric model could achieve accurate survival risk stratification of DLBCL patients. The outcomes of this study will be helpful for the early identification of high-risk DLBCL patients with refractory relapses and for guiding individualized treatment strategies.

Progressive sarcopenia and myosteatosis predict prognosis of advanced HCC patients treated with immune checkpoint inhibitors.

Immunotherapy stands as a pivotal modality in the therapeutic landscape for the treatment of advanced hepatocellular carcinoma, yet responses vary among patients. This study delves into the potential impact of sarcopenia, myosteatosis and adiposity indicators, as well as their changes during immunotherapy, on treatment response and prognosis in patients with advanced hepatocellular carcinoma treated with immune checkpoint inhibitors. In this retrospective analysis, 116 patients with advanced hepatocellular carcinoma receiving immune checkpoint inhibitors were recruited. Skeletal muscle, intramuscular, subcutaneous, and visceral adipose tissue were assessed by computed tomography at the level of the third lumbar vertebrae before and after 3 months of treatment. Sarcopenia and myosteatosis were evaluated by skeletal muscle index and mean muscle density using predefined threshold values. Patients were stratified based on specific baseline values or median values, along with alterations observed during the treatment course. Overall survival (OS) and progression-free survival (PFS) were compared using the log-rank test and a multifactorial Cox proportional risk model. A total of 116 patients were recruited and divided into two cohorts, 81 patients for the training set and 35 patients for the validating set. In the overall cohort, progressive sarcopenia (P=0.021) and progressive myosteatosis (P=0.001) were associated with objective response rates, whereas progressive myosteatosis (P<0.001) was associated with disease control rates. In the training set, baseline sarcopenia, myosteatosis, and subcutaneous and visceral adipose tissue were not significantly associated with PFS and OS. In multivariate analysis adjusting for sex, age, and other factors, progressive sarcopenia(P=0.002) and myosteatosis (P=0.018) remained independent predictors of PFS. Progressive sarcopenia (P=0.005), performance status (P=0.006) and visceral adipose tissue index (P=0.001) were all independent predictors of OS. The predictive models developed in the training set also had good feasibility in the validating set. Progressive sarcopenia and myosteatosis are predictors of poor clinical outcomes in patients with advanced hepatocellular carcinoma receiving immune checkpoint inhibitors, and high baseline visceral adiposity is associated with a poorer survival.

[The Value of Baseline PET/CT Imaging of Bone Marrow 18F-FDG Uptake Pattern in Predicting Prognosis of DLBCL].

To investigate the prognostic value of bone marrow uptake pattern in 18F-deoxyglucose (18F-FDG) PET/CT imaging before diffuse large B-cell lymphoma (DLBCL) treatment. The clinical data of 156 patients with DLBCL were retrospectively analyzed. All patients underwent bone marrow biopsy, bone marrow smear, flow cytometry and 18F-FDG PET/CT scan before treatment. Taking normal liver 18F-FDG uptake as the standard, the bone marrow uptake patterns of patients were divided into three types: focal increased bone marrow uptake (fPET+), diffusely increased bone marrow uptake (dPET+), and normal bone marrow uptake (nPET). Survival analysis was performed using the Kaplan-Meier method, log-rank test was used for comparison of differences between groups, and multivariate Cox regression analysis was used to identify risk factors associated with prognosis. Among the 156 patients, 17 cases were fPET+, 28 cases were dPET+, and 111 cases were nPET. Clinical diagnosis of bone marrow infiltration (BMI) was positive in 21 cases and negative in 135 cases. There were 62 cases of recurrence and progression, and 18 cases of death. Univariate analysis showed that Ann Arbor stage III/IV, B symptoms, NCCN-IPI score, lactate dehydrogenase (LDH), BMI+ and fPET+ were associated with progression-free survival (PFS) (all P < 0.05), while Ann Arbor stage III/IV, NCCN-IPI score, LDH, BMI+ and fPET+ were associated with overall survival (OS) (all P < 0.05). Multivariate analysis showed that Ann Arbor stage III/IV, LDH and fPET+ were independent predictors of PFS (all P < 0.05). There were no independent predictors of OS in multivariate analysis. The bone marrow uptake pattern of 18F-FDG imaging in DLBCL patients before treatment has a predictive value for DLBCL, while fPET+ is an independent risk factor for PFS.

Effectiveness and Safety of Pyrotinib-Based Therapy in the Treatment of HER2-Positive Breast Cancer Patients with Brain Metastases: A Multicenter Real-World Study

BackgroundApproximately 30% to 50% of patients with human epidermal growth factor receptor 2-positive metastatic breast cancer develop brain metastasis (BMs). Pyrotinib has shown promising efficacy in these patients. However, real-world evidence supporting its use is scarce. Therefore, we evaluate the efficacy and safety of pyrotinib-based regimens in the real world. Materials and MethodsWe enrolled patients with BMs from various healthcare facilities in China's Shandong region and used an updated breast-graded prognostic assessment (breast-GPA) to predict survival outcomes. ResultsEfficacy and toxicity were assessed in 101 patients. Overall, the median progression-free survival (PFS) was 11.0 months (95% CI, 7.6-14.4 months). PFS was shorter in patients with a breast-GPA of 0 to 2.0 (P< .001). Previous treatment with pertuzumab plus trastuzumab (P = .039) and varying numbers of BMs (P = .028) had a significant positive correlation with PFS. Additionally, radiotherapy (P = .033) for BMs, especially pyrotinib concurrent with radiotherapy (P = .013), significantly prolonged the PFS. In patients with a breast-GPA of 0 to 2.0, a significant difference in PFS was observed depending on whether the brain was the first metastatic site (P< .001). Furthermore, a breast-GPA (0-2.0 vs. 2.5-4.0), and radiotherapy for BMs were found to be independent predictors of PFS. Overall, the objective response rate was 42.6%, while the disease control rate was 88.1%. Diarrhea emerged as the most common adverse event. ConclusionPyrotinib-based therapy is effective and tolerable in human epidermal growth factor receptor 2-positive metastatic breast cancer with BMs. Patients who underwent radiotherapy for BMs, particularly those who received pyrotinib concurrently with radiotherapy, exhibited a more favorable prognosis.

Joint modeling of tumor dynamics and progression-free survival in advanced breast cancer: Leveraging data from amcenestrant early phase I-II trials.

A joint modeling framework was developed using data from 75 patients of early amcenestrant phase I-II AMEERA-1-2 dose escalation and expansion cohorts. A semi-mechanistic tumor growth inhibition (TGI) model was developed. It accounts for the dynamics of sensitive and resistant tumor cells, an exposure-driven effect on tumor proliferation of sensitive cells, and a delay in the initiation of treatment effect to describe the time course of target lesion tumor size (TS) data. Individual treatment exposure overtime was introduced in the model using concentrations predicted by a population pharmacokinetic model of amcenestrant. This joint modeling framework integrated complex RECISTv1.1 criteria information, linked TS metrics to progression-free survival (PFS), and was externally evaluated using the randomized phase II trial AMEERA-3. We demonstrated that the instantaneous rate of change in TS (TS slope) was an important predictor of PFS and the developed joint model was able to predict well the PFS of amcenestrant phase II monotherapy trial using only early phase I-II data. This provides a good modeling and simulation tool to inform early development decisions.

Development and validation of a deep learning-based model to predict response and survival of T790M mutant non-small cell lung cancer patients in early clinical phase trials using electronic medical record and pharmacokinetic data.

Epidermal growth factor receptor (EGFR) T790M mutation is the standard predictive biomarker for third-generation epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) treatment. While not all T790M-positive patients respond to third-generation EGFR-TKIs and have a good prognosis, it necessitates novel tools to supplement EGFR genotype detection for predicting efficacy and stratifying EGFR-mutant patients with various prognoses. Mixture-of-experts (MoE) is designed to disassemble a large model into many small models. Meanwhile, it is also a model ensembling method that can better capture multiple patterns of intrinsic subgroups of enrolled patients. Therefore, the combination of MoE and Cox algorithm has the potential to predict efficacy and stratify survival in non-small cell lung cancer (NSCLC) patients with EGFR mutations. We utilized the electronic medical record (EMR) and pharmacokinetic parameters of 326 T790M-mutated NSCLC patients, including 283 patients treated with Abivertinib in phase I (n=177, for training) and II (n=106, for validation) clinical trials and an additional validation cohort 2 comprising 43 patients treated with BPI-7711. Furthermore, 18 patients underwent whole-exome sequencing for biological interpretation of CoxMoE. We evaluated the predictive performance for therapeutic response using the area under the curve (AUC) and the Concordance index (C-index) for progression-free survival (PFS). CoxMoE exhibited AUCs of 0.73-0.83 for predicting efficacy defined by best overall response (BoR) and achieved C-index values of 0.64-0.65 for PFS prediction in training and validating cohorts. The PFS of 198 patients with a low risk [median, 6.0 (range, 1.0-23.3) months in the abivertinib treated cohort; median 16.5 (range, 1.4-27.4) months in BPI-7711 treated cohort] of being non-responder increased by 43% [hazard ratio (HR), 0.56; 95% confidence interval (CI), 0.40-0.78; P=0.0013] and 50% (HR, 0; 95% CI, 0-0; P=0.01) compared to those at high-risk [median, 4.2 (range, 1.0-35) months in the abivertinib treated cohort; median, 11.0 (range, 1.4-25.1) months in BPI-7711 treated cohort]. Additionally, activated partial thromboplastin time (APTT), creatinine clearance (Ccr), monocyte, and steady-state plasma trough concentration utilited to construct model were found significantly associated with drug resistance and aggressive tumor pathways. A robust correlation was observed between APTT and Ccr with PFS (log-rank test; P<0.01) and treatment response (Wilcoxon test; P<0.05), respectively. CoxMoE offers a valuable approach for patient selection by forecasting therapeutic response and PFS utilizing laboratory tests and pharmacokinetic parameters in the setting of early-phase clinical trials. Simultaneously, CoxMoE could predict the efficacy of third-generation EGFR-TKI non-invasively for T790M-positive NSCLC patients, thereby complementing existing EGFR genotype detection.

Development of Clinical Radiomics-Based Models to Predict Survival Outcome in Pancreatic Ductal Adenocarcinoma: A Multicenter Retrospective Study.

This multicenter retrospective study aims to identify reliable clinical and radiomic features to build machine learning models that predict progression-free survival (PFS) and overall survival (OS) in pancreatic ductal adenocarcinoma (PDAC) patients. Between 2010 and 2020 pre-treatment contrast-enhanced CT scans of 287 pathology-confirmed PDAC patients from two sites of the Hopital Universitaire de Bruxelles (HUB) and from 47 hospitals within the HUB network were retrospectively analysed. Demographic, clinical, and survival data were also collected. Gross tumour volume (GTV) and non-tumoral pancreas (RPV) were semi-manually segmented and radiomics features were extracted. Patients from two HUB sites comprised the training dataset, while those from the remaining 47 hospitals of the HUB network constituted the testing dataset. A three-step method was used for feature selection. Based on the GradientBoostingSurvivalAnalysis classifier, different machine learning models were trained and tested to predict OS and PFS. Model performances were assessed using the C-index and Kaplan-Meier curves. SHAP analysis was applied to allow for post hoc interpretability. A total of 107 radiomics features were extracted from each of the GTV and RPV. Fourteen subgroups of features were selected: clinical, GTV, RPV, clinical & GTV, clinical & GTV & RPV, GTV-volume and RPV-volume both for OS and PFS. Subsequently, 14 Gradient Boosting Survival Analysis models were trained and tested. In the testing dataset, the clinical & GTV model demonstrated the highest performance for OS (C-index: 0.72) among all other models, while for PFS, the clinical model exhibited a superior performance (C-index: 0.70). An integrated approach, combining clinical and radiomics features, excels in predicting OS, whereas clinical features demonstrate strong performance in PFS prediction.

Abstract 4967: Predicting progression-free survival (PFS) in first-line (1L) immune checkpoint inhibitor (ICI)-treated patients (pts) with advanced non-small cell lung cancer (aNSCLC): Machine learning (ML) application in real-world data

Abstract Introduction: Anti-programmed cell death (ligand)-1 ICIs are the standard of care 1L treatment for pts with aNSCLC without actionable oncogenic driver mutations. Heterogeneity in ICI utilization and outcomes is observed in real-world pts. By leveraging ML in electronic health records (EHRs), we identified predictors influencing key clinical outcomes, and developed a nomogram to predict likelihood of median PFS in 1L ICI-treated aNSCLC pts for potential risk stratification for interventions. Methods: 1L ICI-treated aNSCLC pts without ALK/ROS1/KRAS/BRAF/EGFR alterations were identified in a US oncology EHR database (ConcertAI; Jan 2015-Feb 2023). Survival ML models were trained on 112 clinical and demographic features with 5-fold nested cross-validation. The top predictors, determined by the best-performing ML model through SHapley Additive exPlanations and clinical judgement, were used to create a Cox proportional hazard (CPH) nomogram of median PFS. All models were evaluated using the concordance index (c-index). Patients were categorized as having high and low risk of progression/death at median PFS according to median risk predicted by the nomogram. Results: The study cohort had 4668 pts (median PFS: 6.1 months; 3811 events). The CPH nomogram predicting 6-month PFS had a c-index of 0.60 with the top 10 predictors identified using the XGBoost model (c-index: 0.62). Nomogram predictors included increased number of metastases, ECOG PS, cough suppressant use, WBC counts, and NLR (Table). Median probability of 6-month PFS for low- and high-risk groups was 35.9% and 19.4%, respectively. Conclusions: Future research should validate these findings and evaluate the opportunity to guide clinical practice to optimize outcomes in aNSCLC pts treated with 1L ICIs. Summary of top 10 clinical predictors at baseline Mean HR (95% CI) Max nomogram points Total metastases, n 1.15 (1.11-1.19) 100 ECOG PS, ordinal 1.14 (1.10-1.19) 55 Basophils, % 0.81 (0.73-0.90) 52 Albumin, g/dL 0.81 (0.75-0.87) 46 Chloride, mmol/L 0.98 (0.97-0.99) 46 Cough suppressants/expectorants, n 1.08 (1.03-1.14) 42 NLR 1.03 (1.02-1.05) 33 WBC counts, 103 cells/mL 1.02 (1.01-1.03) 29 PD-L1 expression, % 1.00 (1.00-1.00) 28 Presence of liver metastases 1.26 (1.15-1.39) 24 ECOG PS, Eastern Cooperative Oncology Group performance status; NLR, neutrophils-to-lymphocytes ratio; PD-L1, programmed cell death-ligand 1; WBC, white blood cell. Citation Format: Deep K. Hathi, James Harnett, Ning Wu, Zhenxing Xu, Weishen Pan, T G. Hager, Ruben G. Quek, Shivani Aggarwal, Young Kim, Petra Rietschel, Frank Seebach, Fei Wang, Ying Li. Predicting progression-free survival (PFS) in first-line (1L) immune checkpoint inhibitor (ICI)-treated patients (pts) with advanced non-small cell lung cancer (aNSCLC): Machine learning (ML) application in real-world data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4967.

End-of-treatment 18[F]-FDG PET can predict early progression in patients receiving bendamustine-rituximab for follicular lymphoma in first relapse: a prospective West Japan hematology Study Group (W-JHS) NHL01 trial.

Response determined by 18[F]-fluoro-2-deoxy-D-glucose (18F-FDG) positron emission tomography (PET)-CT after induction therapy can predict progression-free survival (PFS) in follicular lymphoma (FL). However, little prospective research has examined the significance of PET after second-line therapy. We conducted a prospective multicenter phase II trial (W-JHS NHL01) of bendamustine plus rituximab (BR) without rituximab maintenance for FL in first relapse. This study aimed to evaluate the usefulness of end-of-treatment (EOT)-PET for predicting PFS in FL patients in first relapse. EOT-PET examinations were performed between 6 and 8weeks from the start of the last BR cycle. The primary endpoint was 1-year PFS. Key secondary endpoints were overall response rate (ORR), complete response rate (CRR), and 1-year overall survival (OS). Seventy-five patients were enrolled, and 8 were excluded from analysis. ORR was 86.6% and CRR was 59.7%. One-year PFS was 88.9% (95% confidence interval [CI] 80.7-94.3%) and 1-year OS in 75 patients was 97.3% (95% CI 89.6-99.3%). One-year PFS was significantly inferior in EOT-PET-positive patients (n = 9) compared with PET-negative patients (n = 58) (77.8% vs. 93.1%; p = 0.02). We confirmed that EOT-PET after second-line BR therapy could predict early progression in FL patients in first relapse.