Predictors Of Recurrence Research Articles

MRI radiomics based on deep learning automated segmentation to predict early recurrence of hepatocellular carcinoma.

To investigate the utility of deep learning (DL) automated segmentation-based MRI radiomic features and clinical-radiological characteristics in predicting early recurrence after curative resection of single hepatocellular carcinoma (HCC). This single-center, retrospective study included consecutive patients with surgically proven HCC who underwent contrast-enhanced MRI before curative hepatectomy from December 2009 to December 2021. Using 3D U-net-based DL algorithms, automated segmentation of the liver and HCC was performed on six MRI sequences. Radiomic features were extracted from the tumor, tumor border extensions (5 mm, 10 mm, and 20 mm), and the liver. A hybrid model incorporating the optimal radiomic signature and preoperative clinical-radiological characteristics was constructed via Cox regression analyses for early recurrence. Model discrimination was characterized with C-index and time-dependent area under the receiver operating curve (tdAUC) and compared with the widely-adopted BCLC and CNLC staging systems. Four hundred and thirty-four patients (median age, 52.0 years; 376 men) were included. Among all radiomic signatures, HCC with 5 mm tumor border extension and liver showed the optimal predictive performance (training set C-index, 0.696). By incorporating this radiomic signature, rim arterial phase hyperenhancement (APHE), and incomplete tumor "capsule," a hybrid model demonstrated a validation set C-index of 0.706 and superior 2-year tdAUC (0.743) than both the BCLC (0.550; p < 0.001) and CNLC (0.635; p = 0.032) systems. This model stratified patients into two prognostically distinct risk strata (both datasets p < 0.001). A preoperative imaging model incorporating the DL automated segmentation-based radiomic signature with rim APHE and incomplete tumor "capsule" accurately predicted early postsurgical recurrence of a single HCC. The DL automated segmentation-based MRI radiomic model with rim APHE and incomplete tumor "capsule" hold the potential to facilitate individualized risk estimation of postsurgical early recurrence in a single HCC. A hybrid model integrating MRI radiomic signature was constructed for early recurrence prediction of HCC. The hybrid model demonstrated superior 2-year AUC than the BCLC and CNLC systems. The model categorized the low-risk HCC group carried longer RFS.

Beyond histology: A tissue algorithm predictive of post-surgical recurrence in hepatocellular carcinomas, including TERT promoter mutation.

Surgical resection for hepatocellular carcinoma (HCC) is burdened with a high recurrence rate and a lack of reliable prognostic factors. The aim of this study was to integrate the HCC pathological features with gene mutations to improve the prognostic role of pathological analysis.This is a monocentric prospective study, including 67 patients resected for HCC. All clinical data and histological features were collected, including tumor grade, architecture, margins, microvascular invasion, and microscopic portal vascular invasion (MPVI). Next-generation sequencing (NGS) was performed using a laboratory-developed multi-gene panel, allowing to amplify 330 amplicons (21.77kb), covering the relevant targets for solid tumor analysis. The most represented mutations were TERT promoter (n = 41, 61.2%), TP53 (n = 18, 26.9%) and CTNNB1 (n = 17, 25.4%). At follow-up, 13 (19.4%) patients experienced HCC recurrence: at multivariate analysis, tumor dimensions (p = 0.040), MPVI (p = 0.010), and TERT mutation (p = 0.034) correlated with recurrence. Dimensions ≥ 4.5cm (very close to AJCC stage pT3; 9 recurrences, p = 0.041, odd-ratio = 3.7), MPVI (9 recurrences, p = 0.062, OR = 3.3), and TERT (11 recurrences, p = 0.049, OR = 4.4) correlated with disease-free survival also at univariate analysis. The concomitant occurrence of these three variables was present in 7 cases, among which 5 recurred (p = 0.002, OR = 15.94).In conclusion, NGS analysis in resected HCC could not only be used for future therapies but should be integrated with histopathology to predict the risk of tumor recurrence after surgical resection: TERT mutation is among the strongest predictors of tumor recurrence, together with tumor stage (dimensions) and the occurrence of MPVI, which should always be reported separately from the classic MVI.

MRI radiomics nomogram integrating postoperative adjuvant treatments in recurrence risk prediction for patients with early-stage cervical cancer.

Adjuvant treatments are valuable to decrease the recurrence rate and improve survival for early-stage cervical cancer patients (ESCC), Therefore, recurrence risk evaluation is critical for the choice of postoperative treatment. A magnetic resonance imaging (MRI) based radiomics nomogram integrating postoperative adjuvant treatments was constructed and validated externally to improve the recurrence risk prediction for ESCC. 212 ESCC patients underwent surgery and adjuvant treatments from three centers were enrolled and divided into the training, internal validation, and external validation cohorts. Their clinical data, pretreatment T2-weighted images (T2WI) were retrieved and analyzed. Radiomics models were constructed using machine learning methods with features extracted and screen from sagittal and axial T2WI. A nomogram for recurrence prediction was build and evaluated using multivariable logistic regression analysis integrating radiomic signature and adjuvant treatments. A total of 8 radiomic features were screened out of 1020 extracted features. The extreme gradient boosting (XGboost) model based on MRI radiomic features performed best in recurrence prediction with an area under curve (AUC) of 0.833, 0.822 in the internal and external validation cohorts, respectively. The nomogram integrating radiomic signature and clinical factors achieved an AUC of 0.806, 0.718 in the internal and external validation cohorts, respectively, for recurrence risk prediction for ESCC. In this study, the nomogram integrating T2WI radiomic signature and clinical factors is valuable to predict the recurrence risk, thereby allowing timely planning for effective treatments for ESCC with high risk of recurrence.

Predictive value of SUVmax from initial 18F-FDG PET/CT scans for treatment outcomes in endometrial cancer patients undergoing fertility sparing management

ObjectiveTo evaluate whether the maximum standardized uptake value (SUVmax) from initial 18F-FDG PET/CT (fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography) scans could be a predictor of complete response and recurrence in...

Histopathologic Predictors for Locoregional Recurrence in Patients With Oral Squamous Cell Carcinoma - A Single-Center Retrospective Study.

Oral squamous cell carcinoma (OSCC) is known for its aggressive behavior and the high potential for locoregional recurrence (LRR), contributing to poor prognostic outcomes. The aim of this study was to investigate the role of histologic parameters in predicting LRR in patients with OSCC. A retrospective analysis was performed on 58 OSCC patients treated between January 2018 and December 2022. Data were collected from medical records, focusing on demographics, clinicopathologic features, and treatment details. Different histopathologic factors such as depth of invasion, tumor stage (T), pathologic node stage (N), histologic grade of differentiation, perineural invasion, lymphovascular invasion, extranodal extension (ENE), and margin of resection were correlated with LRR. Out of 58 patients, 20 (34.4%) reported LRR within the first year of follow-up. In the recurrence group, 14 patients succumbed to death within 24 months. Among all the histopathologic parameters, our study found a statistically significant correlation between higher pathologic node stage, presence of ENE, and closest margin of resection (≤5mm) with LRR. Higher pathologic node stage, presence of ENE, and closest margin of resection (≤5mm) were the histopathologic factors associated with LRR, and can serve as deciding prognostic factors. Treatment intensification in early-stage disease with higher pathologic nodal stage, presence of ENE, and closest margin of resection (≤5mm) may improve survival outcomes.

Role of radiomics as a predictor of disease recurrence in ovarian cancer: a systematic review.

Ovarian cancer is associated with high cancer-related mortality rate attributed to late-stage diagnosis, limited treatment options, and frequent disease recurrence. As a result, careful patient selection is important especially in setting of radical surgery. Radiomics is an emerging field in medical imaging, which may help provide vital prognostic evaluation and help patient selection for radical treatment strategies. This systematic review aims to assess the role of radiomics as a predictor of disease recurrence in ovarian cancer. A systematic search was conducted in Medline, EMBASE, and Web of Science databases. Studies meeting inclusion criteria investigating the use of radiomics to predict post-operative recurrence in ovarian cancer were included in our qualitative analysis. Study quality was assessed using the QUADAS-2 and Radiomics Quality Score tools. Six retrospective studies met the inclusion criteria, involving a total of 952 participants. Radiomic-based signatures demonstrated consistent performance in predicting disease recurrence, as evidenced by satisfactory area under the receiver operating characteristic curve values (AUC range 0.77-0.89). Radiomic-based signatures appear to good prognosticators of disease recurrence in ovarian cancer as estimated by AUC. The reviewed studies consistently reported the potential of radiomic features to enhance risk stratification and personalise treatment decisions in this complex cohort of patients. Further research is warranted to address limitations related to feature reliability, workflow heterogeneity, and the need for prospective validation studies.

The relationship between serum coagulation parameters and the recurrence of chronic subdural hematoma.

The aim is to investigate the relationship between serum coagulation parameters (PT, APTT, D-D and FDP) before hospitalization and recurrence of chronic subdural hematoma (CSDH). 236 patients with CSDH who were diagnosed for the first time and had complete medical records were followed up for at least 90days. Fifty patients (21.2%) had relapsed. Univariate analysis was conducted including general data, imaging data and test results. Serum coagulation parameters (PT, APTT, D-D and FDP) were detected for all CSDH patients. The study identified several factors that exhibited a significant correlation with chronic subdural hematoma (CSDH) recurrence. These factors included advanced age (p = 0.01), hypertension (p = 0.04), liver disease (p = 0.01), anticoagulant drug use (p = 0.01), antiplatelet drug use (p = 0.02), bilateral hematoma (p = 0.02), and single-layer hematoma (p = 0.01). In addition, the presence of fibrin/fibrinogen degradation products (FDP) exceeding 5mg/L demonstrated a significant relationship with CSDH recurrence (P < 0.05). Notably, the combined assessment of D-dimer (D-D) and FDP exhibited a significant difference, particularly regarding recurrence within 30days after surgery (P < 0.05). The simultaneous elevation of serum FDP and D-D levels upon admission represents a potentially novel predictor for CSDH recurrence. This finding is particularly relevant for patients who experience recurrence within 30days following surgical intervention. Older individuals with CSDH who undergo trepanation and drainage should be closely monitored due to their relatively higher recurrence rate.

Application of improved DBN and GRU based on intelligent optimization algorithm in power load identification and prediction

Non intrusive load monitoring belongs to the key technologies of intelligent power management systems, playing a crucial role in smart grids. To achieve accurate identification and prediction of electricity load, intelligent optimization algorithms are introduced into deep learning optimization for improvement. A load recognition model combining sparrow search algorithm and deep confidence network is designed, as well as a gated recurrent network prediction model on the grounds of particle swarm optimization. The relevant results showed that the sparrow search algorithm used in the study performed well on the solution performance evaluation metrics with a minimum value of 0.209 for the inverse generation distance and a maximum value of 0.814 for the hyper-volume. The accuracy and recall values of the optimized load identification model designed in the study were relatively high. When the accuracy was 0.9, the recall rate could reach 0.94. The recognition accuracy of the model on the basis of the test set could reach up to 0.924. The lowest classification error was only 0.05. The maximum F1 value of the harmonic evaluation index of the bidirectional gated recurrent network optimized by particle swarm optimization converged to 90.06%. The loss function had been optimized by particle swarm optimization, and both the convergence value and convergence speed had been markedly enhanced. The average absolute error and root mean square error of the prediction model were both below 0.3. Compared to the bidirectional gated recurrent model before optimization, the particle swarm optimization strategy had a significant improvement effect on prediction details. In addition, the research method had superior recognition response speed and adaptability in real application environments. This study helps to understand the load demand of the power system, optimize the operation of the power grid, and strengthen the reliability, efficiency, and sustainability of the power system.

Remaining useful lifetime prediction for milling blades using a fused data prediction model (FDPM)

AbstractIn various industry sectors, predicting the real-life availability of milling applications poses a significant challenge. This challenge arises from the need to prevent inefficient blade resource utilization and the risk of machine breakdowns due to natural wear. To ensure timely and accurate adjustments to milling processes based on the machine's cutting blade condition without disrupting ongoing production, we introduce the Fused Data Prediction Model (FDPM), a novel temporal hybrid prediction model. The FDPM combines the static and dynamic features of the machines to generate simulated outputs, including average cutting force, material removal rate, and peripheral milling machine torque. These outputs are correlated with real blade wear measurements, creating a simulation model that provides insights into predicting the wear progression in the machine when associated with real machine operational parameters. The FDPM also considers data preprocessing, reducing the dimensional space to an advanced recurrent neural network prediction algorithm for forecasting blade wear levels in milling. The validation of the physics-based simulation model indicates the highest fidelity in replicating wear progression with the average cutting force variable, demonstrating an average relative error of 2.38% when compared to the measured mean of rake wear during the milling cycle. These findings illustrate the effectiveness of the FDPM approach, showcasing an impressive prediction accuracy exceeding 93% when the model is trained with only 50% of the available data. These results highlight the potential of the FDPM model as a robust and versatile method for assessing wear levels in milling operations precisely, without disrupting ongoing production.

DGPRI, a new liver fibrosis assessment index, predicts recurrence of AFP-negative hepatocellular carcinoma after hepatic resection: a single-center retrospective study

Although patients with alpha-fetoprotein-negative hepatocellular carcinoma (AFPNHCC) have a favorable prognosis, a high risk of postoperative recurrence remains. We developed and validated a novel liver fibrosis assessment index, the direct bilirubin-gamma-glutamyl transpeptidase-to-platelet ratio (DGPRI). DGPRI was calculated for each of the 378 patients with AFPNHCC who underwent hepatic resection. The patients were divided into high- and low-score groups using the optimal cutoff value. The Lasso-Cox method was used to identify the characteristics of postoperative recurrence, followed by multivariate Cox regression analysis to determine the independent risk factors associated with recurrence. A nomogram model incorporating the DGPRI was developed and validated. High DGPRI was identified as an independent risk factor (hazard ratio = 2.086) for postoperative recurrence in patients with AFPNHCC. DGPRI exhibited better predictive ability for recurrence 1–5 years after surgery than direct bilirubin and the gamma-glutamyl transpeptidase-to-platelet ratio. The DGPRI-nomogram model demonstrated good predictive ability, with a C-index of 0.674 (95% CI 0.621–0.727). The calibration curves and clinical decision analysis demonstrated its clinical utility. The DGPRI nomogram model performed better than the TNM and BCLC staging systems for predicting recurrence-free survival. DGPRI is a novel and effective predictor of postoperative recurrence in patients with AFPNHCC and provides a superior assessment of preoperative liver fibrosis.

Rates of Recurrent Intestinal Metaplasia and Dysplasia After Successful Endoscopic Therapy of Barrett's Neoplasia by Endoscopic Mucosal Resection vs Endoscopic Submucosal Dissection and Ablation: A Large North American Multicenter Cohort.

Endoscopic eradication therapy (EET) combining endoscopic resection (ER) with endoscopic mucosal resection (EMR) or endoscopic submucosal dissection (ESD) followed by ablation is the standard of care for the treatment of dysplastic Barrett's esophagus (BE). We have previously shown comparable rates of complete remission of intestinal metaplasia (CRIM) with both approaches. However, data comparing recurrence after CRIM are lacking. We compared rates of recurrence after CRIM with both techniques in a multicenter cohort. Patients undergoing EET achieving CRIM at 3 academic institutions were included. Demographic and clinical data were abstracted. Outcomes included rates and predictors of any BE and dysplastic BE recurrence in the 2 groups. Cox-proportional hazards models and inverse probability treatment weighting (IPTW) analysis were used for analysis. A total of 621 patients (514 EMR and 107 ESD) achieving CRIM were included in the recurrence analysis. The incidence of any BE (15.7, 5.7 per 100 patient-years) and dysplastic BE recurrence (7.3, 5.3 per 100 patient-years) were comparable in the EMR and ESD groups, respectively. On multivariable analyses, the chances of BE recurrence were not influenced by ER technique (hazard ratio 0.87; 95% confidence interval 0.51-1.49; P = 0.62), which was also confirmed by IPTW analysis (ESD vs EMR: hazard ratio 0.98; 95% confidence interval 0.56-1.73; P = 0.94). BE length, lesion size, and history of cigarette smoking were independent predictors of BE recurrence. Patients with BE dysplasia/neoplasia achieving CRIM, initially treated with EMR/ablation, had comparable recurrence rates to ESD/ablation. Randomized trials are needed to confirm these outcomes between the 2 ER techniques.

Nomogram for predicting post-therapy recurrence in BCLC A/B hepatocellular carcinoma with Child-Pugh B cirrhosis

IntroductionThis study conducts a retrospective analysis on patients with BCLC stage A/B hepatocellular carcinoma (HCC) accompanied by Child-Pugh B cirrhosis, who underwent transarterial chemoembolization (TACE) in combination with local ablation therapy. Our goal was to uncover risk factors contributing to post-treatment recurrence and to develop and validate an innovative 1-, 3-, and 5-year recurrence free survival (RFS) nomogram.MethodsData from 255 BCLC A/B HCC patients with Child-Pugh B cirrhosis treated at Beijing You’an Hospital (January 2014 - January 2020) were analyzed using random survival forest (RSF), LASSO regression, and multivariate Cox regression to identify independent risk factors for RFS. The prognostic nomogram was then constructed and validated, categorizing patients into low, intermediate, and high-risk groups, with RFS assessed using Kaplan-Meier curves.ResultsThe nomogram, integrating the albumin/globulin ratio, gender, tumor number, and size, showcased robust predictive performance. Harrell’s concordance index (C-index) values for the training and validation cohorts were 0.744 (95% CI: 0.703–0.785) and 0.724 (95% CI: 0.644–0.804), respectively. The area under the curve (AUC) values for 1-, 3-, and 5-year RFS in the two cohorts were also promising. Calibration curves highlighted the nomogram’s reliability and decision curve analysis (DCA) confirmed its practical clinical benefits. Through meticulous patient stratification, we also revealed the nomogram’s efficacy in distinguishing varying recurrence risks.ConclusionThis study advances recurrence prediction in BCLC A/B HCC patients with Child-Pugh B cirrhosis following TACE combined with ablation. The established nomogram accurately predicts 1-, 3-, and 5-year RFS, facilitating timely identification of high-risk populations.

Meningioma recurrence: Time for an online prediction tool?

Background: Meningioma, the most common brain tumor, traditionally considered benign, has a relatively high risk of recurrence over a patient’s lifespan. In addition, with the emergence of several clinical, radiological, and molecular variables, it is becoming evident that existing grading criteria, including Simpson’s and World Health Organization classification, may not be sufficient or accurate. As web-based tools for widespread accessibility and usage become commonplace, such as those for gene identification or other cancers, it is timely for meningioma care to take advantage of evolving new markers to help advance patient care. Methods: A scoping review of the meningioma literature was undertaken using the MEDLINE and Embase databases. We reviewed original studies and review articles from September 2022 to December 2023 that provided the most updated information on the demographic, clinical, radiographic, histopathological, molecular genetics, and management of meningiomas in the adult population. Results: Our scoping review reveals a large body of meningioma literature that has evaluated the determinants for recurrence and aggressive tumor biology, including older age, female sex, genetic abnormalities such as telomerase reverse transcriptase promoter mutation, CDKN2A deletion, subtotal resection, and higher grade. Despite a large body of evidence on meningiomas, however, we noted a lack of tools to aid the clinician in decision-making. We identified the need for an online, self-updating, and machine-learning-based dynamic model that can incorporate demographic, clinical, radiographic, histopathological, and genetic variables to predict the recurrence risk of meningiomas. Conclusion: Although a challenging endeavor, a recurrence prediction tool for meningioma would provide critical information for the meningioma patient and the clinician making decisions on long-term surveillance and management of meningiomas.

T-antigen as a biomarker of progression-free survival in patients with glioblastoma.

Glioblastoma (GBM) is one of the most aggressive brain tumors and often leads to poor outcomes. Studies have indicated that glycan levels are significantly correlated with the pathogenesis and development of cancers. However, whether glycan levels can serve as diagnostic or prognostic biomarkers in GBM remains unclear. We obtained glycomic profiles in tissue and serum samples from 55 individuals with GBM using a well-established lectin biochip platform probing with 11 specific lectins. Our univariate analysis showed that 5 out of the 11 lectin-probed glycans (LPGs) were significantly higher in GBM tissues than in peri-tumoral tissues. After logistic regression analyses, only the Jacalin-probed T-antigen difference between the two groups remained significant (p = 0.037). Moreover, survival-related analyses showed that the level of Jacalin-probed T-antigen was significantly associated with the progression-free survival (p = 0.038) of patients. However, none of the LPG levels were correlated with the overall survival or the chemosensitivity to temozolomide therapy. The correlation coefficient analysis showed a moderate-to-strong correlation in the Jacalin-probed T-antigen levels between GBM tissues and serum samples, indicating its potential usefulness as a non-invasive GBM progression biomarker. Glycomics analyses can be helpful in the prediction of GBM recurrences and may provide information useful for GBM glycan-based target therapies or vaccine development.

Clinical and Paraclinical Considerations Regarding ki67’s Role in the Management of Differentiated Thyroid Carcinoma—A Literature Review

Background and Objectives: The ki67 nuclear protein is a tool for diagnosis and prognosis in oncology that is used to evaluate cell proliferation. Differentiated thyroid carcinoma is usually a slow-growing neoplasm, the most common type being the papillary form. Some clinical and pathological aspects may predict aggressive behaviour. There are reported cases of recurrence without clinico-pathological findings of aggressiveness. To obtain better predictions of the disease outcome in thyroid carcinoma, many immunohistochemical markers have been studied. The aim of this narrative literature review is to identify the benefits that ki67 may add to the management of patients with differentiated thyroid carcinoma, according to the latest evidence. Materials and Methods: We performed a search on the PubMed and Google Scholar databases using controlled vocabulary and keywords to find the most suitable published articles. A total number of sixty-eight items were identified, and five other articles were selected from other sources. After refining the selection, the inclusion criteria and exclusion criteria were applied, and a total number of twenty-nine articles were included in this literature review. Results and Discussion: The studies consist of retrospective studies (89.66%), case reports (6.9%) and literature reviews (3.45%), evaluating the role, implications and other parameters of ki67 as a diagnostic and/or prognostic tool. The statistical correlations between ki67 and other features were systematized as qualitative results of this review in order to improve the treatment strategies presented in the included articles. Conclusions: The included studies present converging data regarding most of the aspects concerning ki67. The ki67 proliferation index is a diagnostic/prognostic tool of interest in differentiated thyroid carcinoma and a good predictor of disease-free survival, disease recurrence and metastatic development. Prospective studies on large cohorts may add value for ki67 as a specific tool in the management strategy of differentiated thyroid carcinoma.

P-wave alternans rebound following pulmonary vein isolation predicts atrial arrhythmia recurrence.

Numerous P-wave indices have been explored as biomarkers to assess atrial fibrillation (AF) risk and the impact of therapy with variable success. We investigated the utility of P-wave alternans (PWA) to track the effects of pulmonary vein isolation (PVI) and to predict atrial arrhythmia recurrence. This medical records study included patients who underwent PVI for AF ablation at our institution, along with 20 control subjects without AF or overt cardiovascular disease. PWA was assessed using novel artificial intelligence-enabled modified moving average (AI-MMA) algorithms. PWA was monitored from the 12-lead ECG at ~1 h before and ~16 h after PVI (n = 45) and at the 4- to 17-week clinically indicated follow-up visit (n = 30). The arrhythmia follow-up period was 955 ± 112 days. PVI acutely reduced PWA by 48%-63% (p < .05) to control ranges in leads II, III, aVF, the leads with the greatest sensitivity in monitoring PWA. Pre-ablation PWA was ~6 µV and decreased to ~3 µV following ablation. Patients who exhibited a rebound in PWA to pre-ablation levels at 4- to 17-week follow-up (p < .01) experienced recurrent atrial arrhythmias, whereas patients whose PWA remained reduced (p = .85) did not, resulting in a significant difference (p < .001) at follow-up. The AUC for PWA's prediction of first recurrence of atrial arrhythmia was 0.81 (p < .01) with 88% sensitivity and 82% specificity. Kaplan-Meier analysis estimated atrial arrhythmia-free survival (p < .01) with an adjusted hazard ratio of 3.4 (95% CI: 1.47-5.24, p < .02). A rebound in PWA to pre-ablation levels detected by AI-MMA in the 12-lead ECG at standard clinical follow-up predicts atrial arrhythmia recurrence.

Performance of the EORTC and CUETO Models to Predict Recurrence and Progression in High-risk Non-muscle-invasive Bladder Cancer Patients.

To evaluate the performance of the European Organization for Research and Treatment of Cancer (EORTC) and the Spanish Urological Club for Oncological Treatment (CUETO) risk scoring models in non-muscle-invasive bladder cancer (NMIBC) patients defined as high risk according to European Association of Urology guidelines and managed based on current recommendations. Data from 187 high-risk NMIBC patients treated at a tertiary center between July 2010 and November 2021 were analyzed retrospectively. One- and five-year recurrence- and progression-free survival were assessed for each patient using the EORTC and CUETO risk scores. The patients were divided into four risk groups according to their risk scores as low, medium-low, medium-high and high risk, as indicated in the models. Discriminative ability was evaluated with the Harrell's concordance index (c-index). Both risk scoring models overestimated the risk of recurrence and progression at one and five years. Only the prediction of recurrence at five years in the high risk group according to the CUETO model was compatible with our cohort. CUETO (c-indices for recurrence and progression were 0.802 and 0.834, respectively) exhibited better discrimination than EORTC (0.722 for recurrence and 0.752 for progression) in the prediction of disease recurrence and progression. The CUETO model was superior to the EORTC model in predicting recurrence and progression and stratifying patients with different prognoses in our high-risk NMIBC patient population treated according to current guideline recommendations. However, both models overestimated the probability of disease recurrence and progression. Only the probability of recurrence at five years in the high-risk group of the CUETO model was compatible with our cohort.

Predictors of corticotroph tumour recurrence after surgical resection

Predictors of corticotroph tumour recurrence after surgical resection

Risk Factors for Relapse of Prostate Cancerafter Radical Prostatectomy in Chinese Population.

To analyze the risk factors for the relapse of prostate cancer (PC) after radical prostatectomy (RP) and build a nomogram as a predictive model. Materials andMethods: The patients who underwent PR from March 2019 to February 2022 were retrospectively enrolled in our hospital's case system. During the follow-up process, two consecutive prostate-specific antigens (PSA) ≥0.2 μg/L were performed. And needle biopsy was performed to further determine whether the patient had prostate cancer recurrence. According to the follow-up results, the patients were divided into non-relapsed and relapsed groups.The related parameters of the two groups were collected. Independent risk factors for postoperative recurrence were determined using a Cox proportional hazards regression model. Statistical software, R, was used to build nomograms. R software was used to construct a nomogram, and the prediction effect of the nomogram was evaluated by the calibration curve and the area under the ROC curve (AUC). Among the 367 patients who underwent RP, 112 (30.52%) had, and 255 (69.48%) did not have relapses after surgery. Cox multivariableregression analysis revealed that preoperative Gleason score, preoperative PSA, pathological staging, positive margin, and seminal vesicle invasion, were the risk factors for postoperative recurrence after RP (all P < 0.05). Verification of the predictive model by ROC curve demonstrated that the AUC of the ROC curves for patients' relapses 3 and 5 years after RP was 0.986 (95%CI0.975-0.998) and 0.974 (95%CI0.961-0.987), respectively. This model validation showed that the results of the predictive model were basically consistent with the actual results, suggesting that the nomogram was able to accurately predict a patient's relapse. The nomogram of this study was a good predictor of postoperative recurrence of PC after RP, which will help doctors provide personalized treatment and follow-up strategies for patients.

Use of deep learning to evaluate tumor microenvironmental features for prediction of colon cancer recurrence.

Deep learning may detect biologically important signals embedded in tumor morphologic features that confer distinct prognoses. Tumor morphological features were quantified to enhance patient risk stratification within DNA mismatch repair (MMR) groups using deep learning. Using a quantitative segmentation algorithm (QuantCRC) that identifies 15 distinct morphological features, we analyzed 402 resected stage III colon carcinomas (191 d-MMR; 189 p-MMR) from participants in a phase III trial of FOLFOX-based adjuvant chemotherapy. Results were validated in an independent cohort (176 d-MMR; 1094 p-MMR). Association of morphological features with clinicopathologic variables, MMR, KRAS, BRAFV600E, and time-to-recurrence (TTR) was determined. Multivariable Cox proportional hazard models were developed to predict TTR. Tumor morphological features differed significantly by MMR status. Cancers with p-MMR had more immature desmoplastic stroma. Tumors with d-MMR had increased inflammatory stroma, epithelial tumor-infiltrating lymphocytes (TILs), high grade histology, mucin, and signet ring cells. Stromal subtype did not differ by BRAFV600E or KRAS status. In p-MMR tumors, multivariable analysis identified tumor-stroma ratio (TSR) as the strongest feature associated with TTR [HRadj 2.02; 95% CI,1.14-3.57; P=0.018; 3-year recurrence: 40.2% vs 20.4%; Q1 vs Q2-4]. Among d-MMR tumors, extent of inflammatory stroma [continuous HRadj 0.98; 95% CI,0.96-0.99; P=0.028; 3-year recurrence: 13.3% vs 33.4%, Q4 vs Q1] and N stage were the most robust prognostically. Association of TSR with TTR was independently validated. In conclusion, QuantCRC can quantify morphological differences within MMR groups in routine tumor sections to determine their relative contributions to patient prognosis, and may elucidate relevant pathophysiologic mechanisms driving prognosis.