Predictor Of Recurrence-free Survival Research Articles

Early stage NSCLS patients' prognostic prediction with multi-information using transformer and graph neural network model.

We proposed a population graph with Transformer-generated and clinical features for the purpose of predicting overall survival (OS) and recurrence-free survival (RFS) for patients with early stage non-small cell lung carcinomas and to compare this model with traditional models. The study included 1705 patients with lung cancer (stages I and II), and a public data set for external validation (n=127). We proposed a graph with edges representing non-imaging patient characteristics and nodes representing imaging tumour region characteristics generated by a pretrained Vision Transformer. The model was compared with a TNM model and a ResNet-Graph model. To evaluate the models' performance, the area under the receiver operator characteristic curve (ROC-AUC) was calculated for both OS and RFS prediction. The Kaplan-Meier method was used to generate prognostic and survival estimates for low- and high-risk groups, along with net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis. An additional subanalysis was conducted to examine the relationship between clinical data and imaging features associated with risk prediction. Our model achieved AUC values of 0.785 (95% confidence interval [CI]: 0.716-0.855) and 0.695 (95% CI: 0.603-0.787) on the testing and external data sets for OS prediction, and 0.726 (95% CI: 0.653-0.800) and 0.700 (95% CI: 0.615-0.785) for RFS prediction. Additional survival analyses indicated that our model outperformed the present TNM and ResNet-Graph models in terms of net benefit for survival prediction. Our Transformer-Graph model was effective at predicting survival in patients with early stage lung cancer, which was constructed using both imaging and non-imaging clinical features. Some high-risk patients were distinguishable by using a similarity score function defined by non-imaging characteristics such as age, gender, histology type, and tumour location, while Transformer-generated features demonstrated additional benefits for patients whose non-imaging characteristics were non-discriminatory for survival outcomes. The study was supported by the National Natural Science Foundation of China (91959126, 8210071009), and Science and Technology Commission of Shanghai Municipality (20XD1403000, 21YF1438200).

Prediction of recurrence free survival for esophageal cancer patients using a protein signature based risk model.

Background: Biomarkers to predict the risk of disease recurrence in Esophageal squamous cell carcinoma (ESCC) patients are urgently needed to improve treatment. We developed proteins expression-based risk model to predict recurrence free survival for ESCC patients.Methods: Alterations in Wnt pathway components expression and subcellular localization were analyzed by immunohistochemistry in 80 ESCCs, 61 esophageal dysplastic and 47 normal tissues; correlated with clinicopathological parameters and clinical outcome over 86 months by survival analysis. Significant prognostic factors were identified by multivariable Cox regression analysis.Results: Biomarker signature score based on cytoplasmic β-catenin, nuclear c-Myc, nuclear DVL and membrane α-catenin was associated with recurrence free survival [Hazard ratio = 1.11 (95% CI = 1.05, 1.17), p < 0.001, C-index = 0.68] and added significant prognostic value over clinical parameters (p < 0.001). The inclusion of Slug further improved prognostic utility (p < 0.001, C-index = 0.71). Biomarker Signature Scoreslug improved risk classification abilities for clinical outcomes at 3 years, accurately predicting recurrence in 79% patients in 1 year and 97% in 3 years in high risk group; 73% patients within low risk group did not have recurrence in 1 year, with AUC of 0.76.Conclusions: Our comprehensive risk model predictive for recurrence allowed us to determine the robustness of our biomarker panel in stratification of ESCC patients at high or low risk of disease recurrence; high risk patients are stratified for more rigorous personalized treatment while the low risk patients may be spared from harmful side effects of toxic therapy.

Prediction of recurrence-free survival and adjuvant therapy benefit in patients with gastrointestinal stromal tumors based on radiomics features.

Development and validation of a radiomics nomogram for predicting recurrence and adjuvant therapy benefit populations in high/intermediate-risk gastrointestinal stromal tumors (GISTs) based on computed tomography (CT) radiomic features. Retrospectively collected from 2009.07 to 2015.09, 220 patients with pathological diagnosis of intermediate- and high-risk stratified gastrointestinal stromal tumors and received imatinib treatment were randomly divided into (6:4) training cohort and validation cohort. The 2D-tumor region of interest (ROI) was delineated from the portal-phase images on contrast-enhanced (CE) CT, and radiological features were extracted. The most valuable radiological features were obtained using a Lasso-Cox regression model. Integrated construction was conducted of nomograms of radiomics characteristics to predict recurrence-free survival (RFS) in patients receiving adjuvant therapy. Eight radiomic signatures were finally selected. The area under the curve (AUC) of the radiomics signature model for predicting 3-, 5-, and 7-year RFS in the training and validation cohorts (training cohort AUC = 0.80, 0.84, 0.76; validation cohort AUC = 0.78, 0.80, 0.76). The constructed radiomics nomogram was more accurate than the clinicopathological nomogram for predicting RFS in GIST (C-index: 0.864 95%CI, 0.817-0.911 vs. 0.733 95%CI, 0.675-0.791). Kaplan-Meier survival curve analysis showed a greater benefit from adjuvant therapy in patients with high radiomics scores (training cohort: p < 0.0001; validation cohort: p = 0.017), while there was no significant difference in the low-score group (p > 0.05). In this study, a nomogram constructed based on preoperative CT radiomics features could be used for RFS prediction in high/intermediate-risk GISTs and assist the clinical decision-making for GIST patients.

788O Association of pre-treatment ctDNA with disease recurrence and clinical and translational factors in patients with stage IIIB-D/IV melanoma treated with adjuvant immunotherapy (CheckMate 915)

In patients (pts) with resectable melanoma, circulating tumor DNA (ctDNA) detection post-resection may be useful to inform disease state and recurrence risk. Post-resection pre-treatment plasma from 1127 pts with stage IIIB-D/IV resected melanoma treated with nivolumab + ipilimumab vs nivolumab in the phase 3 CheckMate 915 study (NCT03068455) was retrospectively evaluated for ctDNA status and level, using a tumor-guided, pt-specific panel of up to 200 variants (Invitae Personalized Cancer MonitoringÔ). Kaplan-Meier and Cox regression models were used to evaluate the association between ctDNA status and recurrence-free survival (RFS), alone and combined with baseline clinical factors and biomarkers, including interferon-γ, TMB, tumor PD-L1, CD8+ T cells, tumor thickness, ulceration, and lymph node involvement. Overall pre-treatment ctDNA prevalence was ∼16% (95% CI: 14%–18%) and similar across most baseline demographics. A trend of greater ctDNA+ prevalence in higher stage III substages of melanoma was observed (IIIB = 11% [35/333], IIIC = 18% [110/596], IIID = 41% [13/32]). Pre-treatment ctDNA was associated with an increased risk of recurrence (HR 1.87, 95% CI: 1.48–2.36; Table). No significant interaction between ctDNA status and treatment arm was seen (ratio of hazard ratios: 0.99, 95% CI: 0.63–1.57). Patients with ctDNA present exhibited a greater rate of recurrence, seen as early as week 13 of therapy (Table). Distant metastasis-free survival results were similar. In composite analyses, improved RFS prediction was observed after combining ctDNA with clinical factors and biomarkers.Table: 788ORFS probability by pre-treatment ctDNA status in CheckMate 915 treated patientsStatusNumber of pts (n)13-week RFS (95% CI)12-month RFS (95% CI)24-month RFS (95% CI)ctDNA+18372.3% (65–78.3)58.4% (50.7–65.3)45.5% (37.9–52.8)ctDNA-94491.7% (89.7–93.3)75.1% (72.1–77.8)65.4% (62.1–68.4) Open table in a new tab Pre-treatment ctDNA was associated with increased risk of early recurrence across treatment arms. ctDNA is a useful biomarker for combined analyses predicting outcome for adjuvant melanoma.

Intraoperative low-dose dopamine is associated with worse survival in patients with hepatocellular carcinoma: A propensity score matching analysis.

BackgroundDopamine is widely used in patients during surgery. We evaluated the association between intraoperative low-dose dopamine administration and recurrence-free survival (RFS) and overall survival (OS) in patients with hepatocellular carcinoma (HCC).MethodsConsecutive patients with nonmetastatic HCC who underwent radical hepatectomy were enrolled between 2008 and 2010. Univariate and multivariate logistic regression analyses were used to evaluate the prognostic factors for RFS and OS. Survival outcomes were evaluated using Kaplan–Meier analyses with the log-rank test. A one-to-one propensity score matching (PSM) analysis was performed to reduce confounding bias.ResultsA total of 805 HCC patients, including 699 patients who did not receive dopamine consumption and 106 patients who received low-dose dopamine during the operation, were retrospectively analyzed. The patients who were assigned low-dose dopamine had worse RFS (p = 0.009) and OS (p = 0.041) than those who did not receive dopamine. Multivariate regression analysis showed that the intraoperative administration of low-dose dopamine was an independent unfavorable predictor for RFS (p = 0.004) but not for OS (p = 0.059). After PSM, the low-dose dopamine-treated group still had significantly poorer RFS (p = 0.003) and OS (p = 0.002). When stratified by time of recurrence, patients with low-dose dopamine use had a significantly greater chance of recurrence within 2 years (p = 0.007) but not after 2 years (p = 0.186).ConclusionsIntraoperative low-dose dopamine use has a negative impact on RFS and OS in HCC patients who have undergone radical hepatectomy. Further prospective studies are required to assess the effects of low-dose dopamine on surgical outcomes in HCC patients.

Histopathological components correlated with MRI features and prognosis in combined hepatocellular carcinoma-cholangiocarcinoma.

To distinguish MR features according to different proportions of the histopathological hepatocellular carcinoma (HCC) component and to investigate whether the proportion of the HCC component can predict the prognosis of patients with cHCC-CCA. The study enrolled 106 cHCC-CCA patients confirmed by histopathology. The MR imaging features and clinicopathological findings were retrospectively evaluated and compared between two subgroups with different proportions of the HCC component. The recurrence-free survival (RFS) and overall survival (OS) were evaluated using Kaplan-Meier survival curves and compared using the log-rank test. Moreover, whether the proportion of the HCC component was a predictor of RFS and OS was investigated using Cox regression analyses. The Liver Imaging Reporting and Data System (LI-RADS) category 4/5 was more prevalent in cHCC-CCAs with an HCC component > 50% (odds ratio (OR) = 5.559, p = 0.018), 70% (OR = 4.031, p = 0.008), and 90% (OR = 6.282, p = 0.012) than in those with an HCC component ≤ 50%, 70%, and 90%, respectively. In addition, cHCC-CCAs with an HCC component > 70% (HR: 0.241, p = 0.023) had a better OS prognosis than those with an HCC component ≤ 70%. cHCC-CCAs categorized as LR-4/5 are mainly composed of HCC component, and cHCC-CCAs with an HCC component > 70% are associated with better OS than those with an HCC component ≤ 70%. These findings suggest that the proportion of HCC or CCA component can predict the prognosis of cHCC-CCA patients. • cHCC-CCAs categorized as LR-4/5 are mainly composed of HCC component. • cHCC-CCAs with an HCC component > 70% are associated with better OS than those with an HCC component ≤ 70%.

Quantitative Pathologic Analysis of Digitized Images of Colorectal Carcinoma Improves Prediction of Recurrence-Free Survival

To examine whether quantitative pathologic analysis of digitized hematoxylin and eosin slides of colorectal carcinoma (CRC) correlates with clinicopathologic features, molecular alterations, and prognosis. A quantitative segmentation algorithm (QuantCRC) was applied to 6468 digitized hematoxylin and eosin slides of CRCs. Fifteen parameters were recorded from each image and tested for associations with clinicopathologic features and molecular alterations. A prognostic model was developed to predict recurrence-free survival using data from the internal cohort (n= 1928) and validated on an internal test (n= 483) and external cohort (n= 938). There were significant differences in QuantCRC according to stage, histologic subtype, grade, venous/lymphatic/perineural invasion, tumor budding, CD8 immunohistochemistry, mismatch repair status, KRAS mutation, BRAF mutation, and CpG methylation. A prognostic model incorporating stage, mismatch repair, and QuantCRC resulted in a Harrell's concordance (c)-index of 0.714 (95% confidence interval [CI], 0.702-0.724) in the internal test and 0.744 (95% CI, 0.741-0.754) in the external cohort. Removing QuantCRC from the model reduced the c-index to 0.679 (95% CI, 0.673-0.694) in the external cohort. Prognostic risk groups were identified, which provided a hazard ratio of 2.24 (95% CI, 1.33-3.87, P=.004) for low vs high-risk stage III CRCs and 2.36 (95% CI, 1.07-5.20, P= .03) for low vs high-risk stage II CRCs, in the external cohort after adjusting for established risk factors. The predicted median 36-month recurrence rate for high-risk stageIII CRCs was 32.7% vs 13.4% for low-risk stage III and 15.8% forhigh-risk stage II vs 5.4% for low-risk stage II CRCs. QuantCRC provides a powerful adjunct to routine pathologic reporting of CRC. A prognostic model using QuantCRC improves prediction of recurrence-free survival.

Clinical Usefulness of Postoperative Serum Carcinoembryonic Antigen in Patients with Colorectal Cancer with Liver Metastases.

Colorectal cancer with liver metastasis (CLM) has high postoperative recurrence rates; therefore, optimizing perioperative treatment is imperative. Postoperative carcinoembryonic antigen (CEA) can aid in detecting minimal residual disease in colon cancer following curative resection. This study aimed to identify the potential role of serum CEA following liver resection in patients with CLM. This retrospective study was conducted at the Cancer Institute Hospital of the Japanese Foundation for Cancer Research from 2004 to 2018 and enrolled patients with CLM who underwent complete resection of primary tumors and CLM. Associations between perioperative CEA levels and characteristics of recurrence were investigated. Recurrence was detected during a median follow-up period of 90.1 months in 343 (54.2%) out of 633 analyzed patients. Patients in the postoperative CEA level > 5 ng/ml group had a significantly higher recurrence rate (75.7% versus 50.0%,p < 0.01) and shorter time until recurrence (4.4 versus 36.9 months,p < 0.01) than those in the postoperative CEA level ≤ 5 ng/ml group. Multivariate analysis revealed that postoperative CEA level > 5 ng/ml was an independent predictor, with hazard ratios of 2.77 (p < 0.01) for recurrence-free survival (RFS) and 3.18 (p < 0.01) for overall survival (OS). Additionally, RFS was significantly shorter among patients in the postoperative CEA level > 5 ng/ml group who did not have normalized CEA levels following adjuvant chemotherapy than among those in the normalized CEA group. The postoperative and post-adjuvant chemotherapy CEA levels in the CEA level > 5 ng/ml group may be predictors of RFS and OS.

LB1003 The 31-GEP stratifies risk of recurrence and metastasis in 894 medicare-eligible patients with cutaneous melanoma

Background: The incidence of cutaneous melanoma (CM) is rising, particularly among older patients who generally have worse outcomes than younger patients. The 31-gene expression profile (GEP) test stratifies patient risk of recurrence and metastasis into low (Class 1A), intermediate (Class 1B/2A), and high risk (Class 2B). The study goal was to demonstrate the 31-GEP provides critical risk information for Medicare-eligible patients, allowing providers to identify patients who may benefit from increased surveillance or therapeutic treatment. Methods: Retrospective analysis of patients over 65 years old enrolled in previous studies who received 31-GEP testing (n=894). Recurrence-free survival (RFS) and distant metastasis-free survival (DMFS) were assessed using Kaplan-Meier, log-rank test, and Cox regression multivariable analysis. Results: Patients with a Class 1A 31-GEP result had higher 5-year RFS (93.9% vs. 52.0%, p<0.001) and DMFS (96.3% vs. 64.8%, p<0.001) than patients with a Class 2B result. A Class 2B result was a significant predictor for RFS (HR=4.66, p<0.001) and DMFS (5.34, p<0.001) in multivariable analysis including AJCC 8th edition stage. Stage II (RFS: HR=2.37, p<0.001; DMFS: HR=1.83, p=0.033) and stage III disease (RFS: HR=6.22, p<0.001; DMFS: HR=6.20, p<0.001) were also significant. Older patients with a negative SLNB and a Class 1A 31-GEP result had higher 5-year RFS (88.2% vs. 53.0%, p<0.001) and DMFS (93.0% VS. 69.4%, p<0.001) than those with a Class 2B result. Class 1A patients who did not receive a SLNB had high RFS (98.2%) and DMFS (99.1%). Conclusions: The 31-GEP test stratified risk of recurrence and survival in Medicare-eligible patients and added independent prognostic information to current staging methods. In patients with a potentially large number of comorbidities, identifying those who can forego more extensive therapy is vital.

Low skeletal muscle mass in cholangiocarcinoma treated by surgical resection. A meta-analysis

To date, the role of low skeletal muscle mass (LSMM) in cholangiocarcinoma (CC) is unclear. Our purpose was to analyze the influence of LSMM on survival in patients with CC treated by surgical resection.MEDLINE, Cochrane, and SCOPUS databases were screened for associations between LSMM and survival in CC up to June 2021. Overall, 16 studies met the inclusion criteria. The methodological quality of the involved studies was analyzed using the QUADAS instrument. The meta-analysis was undertaken using RevMan 5.4 software.The prevalence of LSMM was 48.40%. LSMM was associated with lower overall survival (OS): HR = 2.44, 95%CI = (2.01-2.96) (simple regression); HR = 2.39, 95%CI = (1.83-3.13) (multiple regression). In extrahepatic CC, sarcopenic patients had lower OS, simple regression: HR = 2.11, 95%CI = (1.39-3.20); multiple regression: HR = 2.28, 95%CI = (1.41-3.70). In intrahepatic CC, LSMM predicted recurrence free survival: HR = 2.33, 95%CI = (1.93-2.81) (simple regression); HR = 2.23, 95%CI = (1.73-2.88) (multiple regression). LSMM predicted OS in intrahepatic CC, simple regression: HR = 2.69, 95%CI = (2.24-3.24); multiple regression: HR = 2.43, 95%CI = (1.73-3.41).LSMM is a risk factor for OS in patients with CC treated by surgical resection. LSMM is a predictor of RFS in patients with intrahepatic CC.

Efficacy of Postoperative Adjuvant Transcatheter Arterial Chemoembolization in Hepatocellular Carcinoma Patients With Microscopic Portal Vein Invasion.

BackgroundMicroscopic portal vein invasion (MPVI) strongly predicts poor prognosis in patients with hepatocellular carcinoma (HCC). This study aims to investigate the impact of MPVI on the efficacy of postoperative adjuvant transcatheter arterial chemoembolization (PA-TACE).MethodsFrom April 2014 to July 2019, a total of 512 HCC patients who underwent curative liver resection (LR) with microscopic vascular invasion (MVI) confirmed by histopathological examination were enrolled and divided into LR alone and PA-TACE groups. They were subsequently stratified into subgroups according to the presence of MPVI. Recurrence-free survival (RFS) and overall survival (OS) were compared using Kaplan–Meier curves and the log-rank test. The efficacy of PA-TACE was tested using univariate and multivariate Cox regression analyses. Sensitivity analysis was conducted after propensity score matching (PSM).ResultsAmong all patients, 165 (32.3%) patients underwent PA-TACE, and 196 (38.2%) patients presented MPVI. In the entire cohort, PA-TACE and the presence of MPVI were identified as independent predictors for RFS and OS (all p<0.05). In the subgroup analysis, patients without MPVI who received PA-TACE had significantly better outcomes than those who underwent LR alone before and after PSM (all p<0.05). For patients with MPVI, PA-TACE displayed no significant benefit in terms of improving either RFS or OS, which was consistent with the results from the PSM cohort.ConclusionAmong the HCC patients without MPVI who underwent curative liver resection, those who received PA-TACE had better RFS and OS outcomes than those who underwent LR alone. For patients with MPVI, PA-TACE had no significant effect on either RFS or OS outcomes.

Identification of specific role of SNX family in gastric cancer prognosis evaluation

We here perform a systematic bioinformatic analysis to uncover the role of sorting nexin (SNX) family in clinical outcome of gastric cancer (GC). Comprehensive bioinformatic analysis were realized with online tools such as TCGA, GEO, String, Timer, cBioportal and Kaplan–Meier Plotter. Statistical analysis was conducted with R language or Perl, and artificial neural network (ANN) model was established using Python. Our analysis demonstrated that SNX4/5/6/7/8/10/13/14/15/16/20/22/25/27/30 were higher expressed in GC, whereas SNX1/17/21/24/33 were in the opposite expression profiles. GSE66229 was employed as verification of the differential expression analysis based on TCGA. Clustering results gave the relative transcriptional levels of 30 SNXs in tumor, and it was totally consistent to the inner relevance of SNXs at mRNA level. Protein–Protein Interaction map showed closely and complex connection among 33 SNXs. Tumor immune infiltration analysis asserted that SNX1/3/9/18/19/21/29/33, SNX1/17/18/20/21/29/31/33, SNX1/2/3/6/10/18/29/33, and SNX1/2/6/10/17/18/20/29 were strongly correlated with four kinds of survival related tumor-infiltrating immune cells, including cancer associated fibroblast, endothelial cells, macrophages and Tregs. Kaplan–Meier survival analysis based on GEO presented more satisfactory results than that based on TCGA-STAD did, and all the 29 SNXs were statistically significant, SNX23/26/28 excluded. SNXs alteration contributed to microsatellite instability (MSI) or higher level of MSI-H (hyper-mutated MSI or high level of MSI), and other malignancy encompassing mutation of TP53 and ARID1A, as well as methylation of MLH1.The multivariate cox model, visualized as a nomogram, performed excellently in patients risk classification, for those with higher risk-score suffered from shorter overall survival (OS). Compared to previous researches, our ANN models showed a predictive power at a middle-upper level, with AUC of 0.87/0.72, 0.84/0.72, 0.90/0.71 (GSE84437), 0.98/0.66, 0.86/0.70, 0.98/0.71 (GSE66229), 0.94/0.66, 0.83/0.71, 0.88/0.72 (GSE26253) corresponding to one-, three- and five-year OS and recurrence free survival (RFS) estimation, especially ANN model built with GSE66229 including exclusively SNXs as input data. The SNX family shows great value in postoperative survival evaluation of GC, and ANN models constructed using SNXs transcriptional data manifesting excellent predictive power in both OS and RFS prediction works as convincing verification to that.

Abstract 5177: Lymph node ratio is superior to AJCC N stage for predicting recurrence in papillary thyroid carcinoma

Abstract Objective: Recently, lymph node ratio (LNR) has emerged as an alternative to American Joint Committee on Cancer (AJCC) N stage, with superior prognostic value. The utility of LNR in Middle Eastern Papillary thyroid carcinoma (PTC) remains unknown. Therefore, we retrospectively analyzed a large cohort of 1407 PTC patients for clinico-pathological associations of LNR. Methods: Receiver operating characteristics (ROC) curve was used to determine the cut-off for LNR. We also performed multivariate logistic regression analysis to determine whether LNR or AJCC N stage was superior in predicting recurrence in PTC. Results: Based on ROC curve analysis, a cut-off of 0.15 was chosen for LNR. High LNR was significantly associated with adverse clinico-pathological characteristics such as male sex, extrathyroidal extension, lymphovascular invasion, multifocality, bilateral tumors, T4 tumors, lateral lymph node (N1b) involvement, distant metastasis, advanced tumor stage, ATA high risk category and tumor recurrence. On multivariate analysis, we found that LNR was a better predictor of tumor recurrence than AJCC N stage (Odds ratio: 1.96 vs. 1.30; p value: 0.0184 vs. 0.3831). We also found that LNR combined with TNM stage and ATA risk category improved the prediction of recurrence-free survival, compared to TNM stage or ATA risk category alone. Conclusions: The present study suggests LNR is an independent predictor of recurrence in Middle Eastern PTC. Integration of LNR with 8th edition AJCC TNM staging system and ATA risk stratification will improve the accuracy to predict recurrence in Middle Eastern PTC and help in tailoring treatment and surveillance strategies in these patients. Citation Format: Sandeep K. Parvathareddy, Abdul K. Siraj, Zeeshan Qadri, Saeeda O. Ahmed, Nabil Siraj, Felisa DeVera, Padmanaban Annaiyappanaidu, Maria A. Sabido, Wael Haqawi, Hassan Aldossari, Khawla S. Al-Kuraya. Lymph node ratio is superior to AJCC N stage for predicting recurrence in papillary thyroid carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5177.

Novel imaging marker for low- and mid-recurrence score patients at risk for recurrence.

e12565 Background: The 21-gene (Oncotype DX) recurrence score (RS) has been utilized as a prognostic assay in patients with hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2-) breast cancer. The results stratify patients into low-, mid- and high-risk of disease recurrence. Patients with a low recurrence score tend to have lower recurrence rates and are less responsive to chemotherapy. On the other hand, patients with high-recurrence scores are more likely to respond to chemotherapy. While for patients at the high end of the RS spectrum it is apparent that chemotherapy carries benefit, distinguishing chemotherapy benefit for low- and mid-recurrence score (RS-low/-mid) patients is less clear. Methods: To address this, we used a novel 2-paramenter pharmacokinetic modeling framework that allows biosignatures to be extracted from dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) studies that contain 3-6 timepoints spaced 60-90 seconds apart. These parameters, referred to as P1 and P2, represent leakiness from vessels to the extravascular space and vice versa. Results: Using this model, we performed a study in a cohort of 111 breast cancer patients with 21-gene assay RS and DCE-MRIs available. P1 and P2 were extracted from patient standard clinical DCE-MRIs. We performed univariate (Kaplan-Meier) survival analyses to test the prognostic value of the parameters covariate in predicting recurrence free survival (RFS). A pre-specified value of the median P1 and P2 across the patient population was used for stratifying patients into “low” and “high” groups, representing patients with “low” or “high” vessel leakiness. We analyzed patients according to RS. Low P1 showed better outcomes in RS-low and RS-mid patients (n=88, p≤0.028; log-rank test). Patients with a high P1 had a 20.2% chance recurrence at six years. The same trend was observed when considering just RS-mid patients only (n=23, p≤0.058). No recurrences were observed in patients with low P1 in either the RS-low or RS-mid categories. Additionally, there were no recurrences in the high P1, RS-low/-mid category that received chemotherapy, suggesting that chemotherapy could be beneficial in this category of patients, although the trend was not statistically significant (n=10, p=0.46). Similar results were seen for P2. Conclusions: Overall, we demonstrate how our prognostic imaging marker can identify a subset of patients with low-/mid-recurrence scores that could benefit from chemotherapy. This new prognostic approach has the potential to optimize treatment and improve personalized care by providing clarity on the use of chemotherapy in low-/mid-recurrence score patients.[Table: see text]

Contrast-enhanced CT radiomics for prediction of recurrence-free survival in gallbladder carcinoma after surgical resection.

Gallbladder carcinoma (GBC) is the most common and aggressive biliary tract malignancy with high postoperative recurrence rates. This single-center study aimed to develop and validate a radiomics signature to estimate GBC recurrence-free survival (RFS). This study retrospectively included 204 consecutive patients with pathologically diagnosed GBC and were randomly divided into development (n = 142) and validation (n = 62) cohorts (7:3). The radiomics features of tumor were extracted from preoperative contrast-enhanced CT imaging for each patient. In the development cohort, the least absolute shrinkage and selection operator (LASSO) Cox regression was employed to develop a radiomics signature for RFS prediction. The patients were stratified into high-score or low-score groups according to their median value of radiomics score. A nomogram was established using multivariable Cox regression by incorporating significant pathological predictors and radiomics signatures. The radiomics signature based on 12 features could discriminate high-risk patients with poor RFS. Multivariate Cox analysis revealed that pT3/4 stage (hazard ratio, [HR] = 2.691), pN2 stage (HR = 3.60), poor differentiation grade (HR = 2.651), and high radiomics score (HR = 1.482) were independent risk variables associated with worse RFS and were incorporated to construct a nomogram. The nomogram displayed good prediction performance in estimating RFS with AUC values of 0.895, 0.935, and 0.907 at 1, 3, and 5 years, respectively. The radiomics signature and combined nomogram may assist in predicting RFS in GBC patients. • A radiomics signature extracted from preoperative contrast-enhanced CT can be a useful tool to preoperatively predict RFS of GBC. • T3/T4 stage, N2, poor tumor differentiation, and high radiomics score were positively associated with postoperative recurrence.

Preoperative CA19-9 is a prognostic factor in pT3N0 gastric cancer patients undergoing curative resection.

The standard treatment for pT3N0 gastric cancer (GC) in Japanese guidelines is radical surgery without adjuvant chemotherapy. However, certain percentages of these patients develop recurrences; therefore, detecting the high-risk subgroup of recurrence may contribute to improve patient's outcome by adjuvant chemotherapy. In this study, we aimed to identify a predictive indicator of poor prognosis in pT3N0 GC. Eighty-one patients who were diagnosed as pT3N0 GC after curative surgical resection and had not received adjuvant chemotherapy were included. The clinicopathological factors and laboratory parameters were evaluated by univariate and multivariate analyses to identify prognostic factors of tumor recurrence. Survival analysis was performed by Kaplan-Meier method. Male (P = 0.027), a high body mass index (BMI) (P = 0.031), a high CA19-9 value (P = 0.025), and a lower number of retrieved lymph nodes (P = 0.018) were found to be significantly associated with a shorter recurrence free survival (RFS). In a multivariate analysis, high CA19-9 value (> 37 U/ml) [(hazard ratio (HR): 3.326; 95% confidence interval (CI): 1.044 to 10.596; P = 0.042] was found to be an independent predictor of RFS. The preoperative high CA19-9 value is considered a useful prognostic marker for predicting cancer recurrence after curative surgery in pT3N0 GC patients. For those patients, adjuvant chemotherapy might be considered to improve the survival outcome.

Development and Validation of an 8-Gene Signature to Improve Survival Prediction of Colorectal Cancer.

BackgroundMost prognostic signatures for colorectal cancer (CRC) are developed to predict overall survival (OS). Gene signatures predicting recurrence-free survival (RFS) are rarely reported, and postoperative recurrence results in a poor outcome. Thus, we aim to construct a robust, individualized gene signature that can predict both OS and RFS of CRC patients.MethodsPrognostic genes that were significantly associated with both OS and RFS in GSE39582 and TCGA cohorts were screened via univariate Cox regression analysis and Venn diagram. These genes were then submitted to least absolute shrinkage and selection operator (LASSO) regression analysis and followed by multivariate Cox regression analysis to obtain an optimal gene signature. Kaplan–Meier (K–M), calibration curves and receiver operating characteristic (ROC) curves were used to evaluate the predictive performance of this signature. A nomogram integrating prognostic factors was constructed to predict 1-, 3-, and 5-year survival probabilities. Function annotation and pathway enrichment analyses were used to elucidate the biological implications of this model.ResultsA total of 186 genes significantly associated with both OS and RFS were identified. Based on these genes, LASSO and multivariate Cox regression analyses determined an 8-gene signature that contained ATOH1, CACNB1, CEBPA, EPPHB2, HIST1H2BJ, INHBB, LYPD6, and ZBED3. Signature high-risk cases had worse OS in the GSE39582 training cohort (hazard ratio [HR] = 1.54, 95% confidence interval [CI] = 1.42 to 1.67) and the TCGA validation cohort (HR = 1.39, 95% CI = 1.24 to 1.56) and worse RFS in both cohorts (GSE39582: HR = 1.49, 95% CI = 1.35 to 1.64; TCGA: HR = 1.39, 95% CI = 1.25 to 1.56). The area under the curves (AUCs) of this model in the training and validation cohorts were all around 0.7, which were higher or no less than several previous models, suggesting that this signature could improve OS and RFS prediction of CRC patients. The risk score was related to multiple oncological pathways. CACNB1, HIST1H2BJ, and INHBB were significantly upregulated in CRC tissues.ConclusionA credible OS and RFS prediction signature with multi-cohort and cross-platform compatibility was constructed in CRC. This signature might facilitate personalized treatment and improve the survival of CRC patients.

CT-Based Radiomics for the Recurrence Prediction of Hepatocellular Carcinoma After Surgical Resection.

PurposeTo explore the effectiveness of radiomics signature in predicting the recurrence of hepatocellular carcinoma (HCC) and the benefit of postoperative adjuvant transcatheter arterial chemoembolization (PA-TACE).Patients and MethodsIn this multicenter retrospective study, 364 consecutive patients with multi-phase computed tomography (CT) images were included. Recurrence-related radiomics features of intra- and peritumoral regions were extracted from the pre-contrast, arterial and portal venous phase, respectively. The radiomics model was established in the training cohort (n = 187) using random survival forests analysis to output prediction probability as “Rad-score” and validated by the internal (n = 92) and external validation cohorts (n = 85). Besides, the Clinical nomogram was developed by clinical-radiologic-pathologic characteristics, and the Combined nomogram was further constructed to evaluate the added value of the Rad-score for individualized recurrence-free survival (RFS) prediction, which is our primary and only endpoint. The performance of the three models was assessed by the concordance index (C-index). Furthermore, all the patients were stratified into high- and low-risk groups of recurrence by the median value of the Rad-score to analyze the benefit of PA-TACE.ResultsThe model built using radiomics signature demonstrated favorable prediction of HCC recurrence across all datasets, with C-index of 0.892, 0.812, 0.809, separately in the training, the internal and external validation cohorts. Univariate and multivariate analysis revealed that the Rad-score was an independent prognostic factor. Significant differences were found between the high- and low-risk group in RFS prediction in all three cohorts. Further analysis showed that compared with the low-risk group, patients with the high-risk received more benefits from PA-TACE.ConclusionThe newly developed Rad-score was not only a powerful biomarker in predicting the RFS of HCC but also a strong stratification basis to explore the high-risk patients who could benefit from PA-TACE.

MP50-02 HOW IMPORTANT ARE MORPHOLOGIC SUBTYPES ON THE PROGNOSIS OF SURGICALLY TREATED NON-METASTATIC PAPILLARY RENAL CELL CARCINOMA? AN ANALYSIS FROM A CONTEMPORARY MULTI-INSTITUTIONAL DATABASE

You have accessJournal of UrologyCME1 May 2022MP50-02 HOW IMPORTANT ARE MORPHOLOGIC SUBTYPES ON THE PROGNOSIS OF SURGICALLY TREATED NON-METASTATIC PAPILLARY RENAL CELL CARCINOMA? AN ANALYSIS FROM A CONTEMPORARY MULTI-INSTITUTIONAL DATABASE Selcuk Erdem, Umberto Capitanio, Riccardo Campi, Daniele Amparore, Maria Carme Mir, Eduard Roussel, Nicola Pavan, Onder Kara, Tobias Klatte, Enes Degirmenci, Resat Aydin, Andrea Minervini, Sergio Serni, Alessandro Berni, Giacomo Rebez, and Faruk Ozcan Selcuk ErdemSelcuk Erdem More articles by this author , Umberto CapitanioUmberto Capitanio More articles by this author , Riccardo CampiRiccardo Campi More articles by this author , Daniele AmparoreDaniele Amparore More articles by this author , Maria Carme MirMaria Carme Mir More articles by this author , Eduard RousselEduard Roussel More articles by this author , Nicola PavanNicola Pavan More articles by this author , Onder KaraOnder Kara More articles by this author , Tobias KlatteTobias Klatte More articles by this author , Enes DegirmenciEnes Degirmenci More articles by this author , Resat AydinResat Aydin More articles by this author , Andrea MinerviniAndrea Minervini More articles by this author , Sergio SerniSergio Serni More articles by this author , Alessandro BerniAlessandro Berni More articles by this author , Giacomo RebezGiacomo Rebez More articles by this author , and Faruk OzcanFaruk Ozcan More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002625.02AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Papillary renal cell carcinoma (papRCC) is currently divided in 2 morphologic subtypes: type 1 and type 2. The aim of this contemporary multi-institutional study is to investigate the role of morphologic subtypes on the prognosis of surgically treated non-metastatic papRCC. METHODS: This study was conducted using a multi-institutional database consisting of 1219 patients with histopathologically confirmed non-metastatic papRCC after partial or radical nephrectomy collected from 8 tertiary European insitutions. The patients with accurate data for morphologic subtypes (type 1 and type 2) and follow-up records were included into this study. Clinical and histopathological parameters, recurrence free survival (RFS), cancer specific survival (CSS) and overall survival (OS) were compared between subtypes. Chi-square and Mann-Whitney U tests, and Kaplan-Meier survival analysis were used for the comparisons. Multivariable Cox Regression analysis was used to assess independent predictors on survival outcomes. RESULTS: A total of 879 patients were identified for this study. The patients in the type 2 group were older (64 vs. 66 years, p=0.006), had greater tumor size (3.8 vs. 4.3 cm, p=0.01) and more frequently underwent radical nephrectomy (25.7 vs. 35.2%, p=0.002). Nuclear grade (p<0.001), pathologic T stage (p<0.001) and N stage (p<0.001) were significantly higher in the type 2 group. The median follow-up was 48 (IQR:24-88) months. Kaplan-Meier survival analyses showed that 5 year RFS (90.3% vs. 76.7%, p<0.001), CSS (93.9% vs. 86.6%, p<0.001) and OS (88.1% vs. 81.0%, p=0.011) were significantly lower in type 2 papRCC. Age, grade, pathologic T and N stage adjusted Cox-Regression analyses found that type 2 was independent predictor for RFS (HR:1.78 [95%CI:1.22-2.58], p=0.003), but not for CSS (HR:1.54 [95%CI:0.93-2.56], p=0.094) and OS (HR:1.09 [95%CI:0.77-1.54], p=0.645). CONCLUSIONS: This contemporary multi-institutional study with a large dataset suggested that type 2 was associated with adverse histopathological outcomes, and was an independent predictor of RFS after surgical treatment of non-metastatic papRCC. These findings may guide decision making on follow-up schedule after surgery, and risk stratification for the adjuvant trials, in the individualized medicine era. Source of Funding: None © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e863 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Selcuk Erdem More articles by this author Umberto Capitanio More articles by this author Riccardo Campi More articles by this author Daniele Amparore More articles by this author Maria Carme Mir More articles by this author Eduard Roussel More articles by this author Nicola Pavan More articles by this author Onder Kara More articles by this author Tobias Klatte More articles by this author Enes Degirmenci More articles by this author Resat Aydin More articles by this author Andrea Minervini More articles by this author Sergio Serni More articles by this author Alessandro Berni More articles by this author Giacomo Rebez More articles by this author Faruk Ozcan More articles by this author Expand All Advertisement PDF DownloadLoading ...

A Prognostic Model of Differentiated Thyroid Cancer Based on Up-Regulated Glycolysis-Related Genes

ObjectiveThis study aims to identify reliable prognostic biomarkers for differentiated thyroid cancer (DTC) based on glycolysis-related genes (GRGs), and to construct a glycolysis-related gene model for predicting the prognosis of DTC patients.MethodsWe retrospectively analyzed the transcriptomic profiles and clinical parameters of 838 thyroid cancer patients from 6 public datasets. Single factor Cox proportional risk regression analysis and Least Absolute Shrinkage and Selection Operator (LASSO) were applied to screen genes related to prognosis based on 2528 GRGs. Then, an optimal prognostic model was developed as well as evaluated by Kaplan-Meier and ROC curves. In addition, the underlying molecular mechanisms in different risk subgroups were also explored via The Cancer Genome Atlas (TCGA) Pan-Cancer study.ResultsThe glycolysis risk score (GRS) outperformed conventional clinicopathological features for recurrence-free survival prediction. The GRS model identified four candidate genes (ADM, MKI67, CD44 and TYMS), and an accurate predictive model of relapse in DTC patients was established that was highly correlated with prognosis (AUC of 0.767). In vitro assays revealed that high expression of those genes increased DTC cancer cell viability and invasion. Functional enrichment analysis indicated that these signature GRGs are involved in remodelling the tumour microenvironment, which has been demonstrated in pan-cancers. Finally, we generated an integrated decision tree and nomogram based on the GRS model and clinicopathological features to optimize risk stratification (AUC of the composite model was 0.815).ConclusionsThe GRG signature-based predictive model may help clinicians provide a prognosis for DTC patients with a high risk of recurrence after surgery and provide further personalized treatment to decrease the chance of relapse.