Similar Datasets
Project description:The rectal cancer patient data set consists of 10 patients from a clinical study at the Surgery department of the University Medical Center Göttingen collected over a longer time. Patients were chosen based on the follow-up time and development of a distant metastasis. First a balanced sample size of five versus five patients with and without a metastatic event was choosen. This needed to be changed to six versus four.
2018-05-23 | GSE100109 | GEO
Project description:The rectal cancer patient data set consists of 10 patients from a clinical study at the Surgery department of the University Medical Center Göttingen collected over a longer time. Patients were chosen based on the follow-up time and development of a distant metastasis. First a balanced sample size of five versus five patients with and without a metastatic event was chosen. This needed to be changed to six versus four. Two conditions of rectal cancer patient data separates by the event of distant metastasis: 1. good prognosis, no event (4 samples), 2. bad prognosis, event (6 samples) Single-end sequencing on a Illumina HiSeq 200 machine with the standard total_RNA protocol with 50 base pairs read length was used
2018-05-23 | GSE99897 | GEO
Project description:Comparison of rectal patient data separated by their event of developing distant metastasis
| PRJNA390729 | ENA
Project description:Distant metastasis (DM) is the main cause of treatment failure in locally advanced rectal cancer. Adjuvant chemotherapy is usually used for distant control. However, not all patients can benefit from adjuvant chemotherapy, and particularly, some patients may even get worse outcomes after the treatment. We develop and validate an MRI-based radiomic signature (RS) for prediction of DM within a multicenter dataset. The RS is proved to be an independent prognostic factor as it not only demonstrates good accuracy for discriminating patients into high and low risk of DM in all the four cohorts, but also outperforms clinical models. Within the stratified analysis, good chemotherapy efficacy is observed for patients with pN2 disease and low RS, whereas poor chemotherapy efficacy is detected in patients with pT1-2 or pN0 disease and high RS. The RS may help individualized treatment planning to select patients who may benefit from adjuvant chemotherapy for distant control.
| S-EPMC7452897 | biostudies-literature
Project description:Background Distant metastasis from rectal cancer usually results in poorer survival and quality of life, so early identification of patients at high risk of distant metastasis from rectal cancer is essential. Method The study used eight machine-learning algorithms to construct a machine-learning model for the risk of distant metastasis from rectal cancer. We developed the models using 23867 patients with rectal cancer from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2017. Meanwhile, 1178 rectal cancer patients from Chinese hospitals were selected to validate the model performance and extrapolation. We tuned the hyperparameters by random search and tenfold cross-validation to construct the machine-learning models. We evaluated the models using the area under the receiver operating characteristic curves (AUC), the area under the precision-recall curve (AUPRC), decision curve analysis, calibration curves, and the precision and accuracy of the internal test set and external validation cohorts. In addition, Shapley’s Additive explanations (SHAP) were used to interpret the machine-learning models. Finally, the best model was applied to develop a web calculator for predicting the risk of distant metastasis in rectal cancer. Result The study included 23,867 rectal cancer patients and 2,840 patients with distant metastasis. Multiple logistic regression analysis showed that age, differentiation grade, T-stage, N-stage, preoperative carcinoembryonic antigen (CEA), tumor deposits, perineural invasion, tumor size, radiation, and chemotherapy were-independent risk factors for distant metastasis in rectal cancer. The mean AUC value of the extreme gradient boosting (XGB) model in ten-fold cross-validation in the training set was 0.859. The XGB model performed best in the internal test set and external validation set. The XGB model in the internal test set had an AUC was 0.855, AUPRC was 0.510, accuracy was 0.900, and precision was 0.880. The metric AUC for the external validation set of the XGB model was 0.814, AUPRC was 0.609, accuracy was 0.800, and precision was 0.810. Finally, we constructed a web calculator using the XGB model for distant metastasis of rectal cancer. Conclusion The study developed and validated an XGB model based on clinicopathological information for predicting the risk of distant metastasis in patients with rectal cancer, which may help physicians make clinical decisions. rectal cancer, distant metastasis, web calculator, machine learning algorithm, external validation
| S-EPMC10465697 | biostudies-literature
Project description:The benefit of adjuvant chemotherapy in patients with locally advanced rectal cancer (LARC) treated with neoadjuvant chemoradiotherapy (nCRT) and surgery is controversial. We examined the association of perineural invasion (PNI) with outcomes to determine whether PNI could be used to risk-stratify patients.We performed a retrospective study of 110 patients treated with nCRT and surgery for LARC at our institution from 2004 to 2011. Eighty-seven patients were identified in our final analysis. We evaluated the association of PNI with locoregional control, distant metastasis-free survival (DMFS), disease-free survival (DFS), and overall survival, using log-rank and Cox proportional hazard modeling.Fourteen patients (16%) were PNI+ and 73 patients (84%) were PNI-. The median follow-up was 27 months (range, 0.9 to 84 mo). The median DMFS was 13.5 months for PNI+ and median not reached (>40 mo) for PNI- (P<0.0001). The median DFS was 13.5 months for PNI+ and 39.8 months for PNI- (P<0.0001). In a multivariate model including 7 pathologic variables, type of surgery, time to surgery from end of nCRT, and use of adjuvant chemotherapy, PNI remained a significant independent predictor of DMFS (hazard ratio 9.79; 95% confidence interval, 3.48-27.53; P<0.0001) and DFS (hazard ratio 5.72; 95% confidence interval, 2.2-14.9; P=0.0001).For patients with LARC treated with nCRT, PNI found at the time of surgery is significantly associated with worse DMFS and DFS. Our data support testing the role of adjuvant chemotherapy in patients with PNI and perhaps other high-risk features.
| S-EPMC5940000 | biostudies-literature
Project description: Not available
| S-EPMC11291400 | biostudies-literature
Project description:Tumor necrosis factor-? (TNF-?), an important factor in systematic inflammation, is reportedly involved in several cancer types. The TNF-? -308 G>A (rs1800629) polymorphism in the promoter region influences TNF-? production. The association between TNF-? -308 G>A polymorphism and colorectal cancer (CRC) is not fully understood, especially the connections between TNF-? -308 G>A polymorphism and clinical features of CRC. In this study, TNF-? -308 G>A polymorphism was genotyped in 1140 individuals with or without CRC from Southwestern China. In case-control studies, we found no association between TNF-? -308 G>A polymorphism and CRC risk. Analysis of the correlations between TNF-? -308 G>A polymorphism and clinical features of CRC revealed that TNF-? -308 A allele was associated with higher body mass index (BMI) larger tumor size, and distant tumor metastasis in all CRC patients. Notably, rectal cancer (a subtype of CRC) patients with TNF-? -308 A allele had a very high risk of distant tumor metastasis [odds ratio (OR) = 4.481; 95% confidence interval (CI): 2.072-9.693; P = 0.00025]. The association between TNF-? -308 A allele and distant tumor metastasis remained even significant after adjusting all clinical characteristics (OR = 7.099; 95% CI: 2.482-20.301; P = 0.000256) in rectal cancer patients. Our results suggested that TNF-? -308 A allele was significantly associated with distant tumor metastasis in rectal cancer patients.
| S-EPMC5456043 | biostudies-literature
Project description:BackgroundRadical resection is associated with good prognosis among patients with cT1/T2Nx rectal cancer. However, still some of the patients experienced cancer recurrence following radical resection. This study tried to identify the postoperative risk factors of local recurrence and distant metastasis separately.MethodsThis retrospective, single-center study comprised of 279 consecutive patients from Linkou branch of Chang Gung Memorial Hospital in 2005-2016 with rectal adenocarcinoma, pT1/T2N0M0 at distance from anal verge ≤ 8cm, who received curative radical resection.ResultsThe study included 279 patients with pT1/pT2N0 mid-low rectal cancer with median follow-up of 73.5 months. Nineteen (6.8%) patients had disease recurrence in total. Nine (3.2%) of them had local recurrence, and fourteen (5.0%) of them had distant metastasis. Distal resection margin < 0.9 (cm) (hazard ratio = 4.9, p = 0.050) was the risk factor of local recurrence. Preoperative carcinoembryonic antigen (CEA) ≥ 5 ng/mL (hazard ratio = 9.3, p = 0.0003), lymph node yield (LNY) < 14 (hazard ratio = 5.0, p = 0.006), and distal resection margin < 1.4cm (hazard ratio = 4.0, p = 0.035) were the risk factors of distant metastasis.ConclusionFor patients with pT1/pT2N0 mid-low rectal cancer, current multidisciplinary treatment brings acceptable survival outcome. Insufficient distal resection margin attracted the awareness of risk factors for local recurrence and distant metastasis as a foundation for future research.
| S-EPMC8045195 | biostudies-literature
Project description:Head motion degrades image quality and causes erroneous parameter estimates in tracer kinetic modeling in brain PET studies. Existing motion correction methods include frame-based image registration (FIR) and correction using real-time hardware-based motion tracking (HMT) information. However, FIR cannot correct for motion within 1 predefined scan period, and HMT is not readily available in the clinic since it typically requires attaching a tracking device to the patient. In this study, we propose a motion correction framework with a data-driven algorithm, that is, using the PET raw data itself, to address these limitations. Methods: We propose a data-driven algorithm, centroid of distribution (COD), to detect head motion. In COD, the central coordinates of the line of response of all events are averaged over 1-s intervals to generate a COD trace. A point-to-point change in the COD trace in 1 direction that exceeded a user-defined threshold was defined as a time point of head motion, which was followed by manually adding additional motion time points. All the frames defined by such time points were reconstructed without attenuation correction and rigidly registered to a reference frame. The resulting transformation matrices were then used to perform the final motion-compensated reconstruction. We applied the new COD framework to 23 human dynamic datasets, all containing large head motion, with 18F-FDG (n = 13) and 11C-UCB-J ((R)-1-((3-(11C-methyl-11C)pyridin-4-yl)methyl)-4-(3,4,5-trifluorophenyl)pyrrolidin-2-one) (n = 10) and compared its performance with FIR and with HMT using Vicra (an optical HMT device), which can be considered the gold standard. Results: The COD method yielded a 1.0% ± 3.2% (mean ± SD across all subjects and 12 gray matter regions) SUV difference for 18F-FDG (3.7% ± 5.4% for 11C-UCB-J) compared with HMT, whereas no motion correction (NMC) and FIR yielded -15.7% ± 12.2% (-20.5% ± 15.8%) and -4.7% ± 6.9% (-6.2% ± 11.0%), respectively. For 18F-FDG dynamic studies, COD yielded differences of 3.6% ± 10.9% in K i value as compared with HMT, whereas NMC and FIR yielded -18.0% ± 39.2% and -2.6% ± 19.8%, respectively. For 11C-UCB-J, COD yielded 3.7% ± 5.2% differences in V T compared with HMT, whereas NMC and FIR yielded -20.0% ± 12.5% and -5.3% ± 9.4%, respectively. Conclusion: The proposed COD-based data-driven motion correction method outperformed FIR and achieved comparable or even better performance than the Vicra HMT method in both static and dynamic studies.
| S-EPMC7456171 | biostudies-literature