Survival In Rectal Cancer Patients Research Articles

A nomogram for predicting the overall survival in rectal cancer patients after total neoadjuvant therapy.

Total neoadjuvant therapy (TNT) has been recommended by the National Comprehensive Cancer Network for treating locally advanced rectal cancer (LARC), but extremely rare studies have focused on establishing nomograms to predict the prognosis in these patients after TNT. We aimed to develop a nomogram to predict overall survival (OS) in rectal cancer patients who underwent TNT. In retrospective cohort study, we extract the data of the rectal cancer patients from the SEER database between 2010 and 2015, including demographic information and tumor characteristics. The cohort was divided into training set and validation set based on a ratio of 7:3. Univariate logistic regression analysis was utilized for the comparison of variables in training set. Candidate variables with P < 0.1 in training set was entered into the best subset selection, LASSO regression and Boruta feature selection. Finally, the selected variables significantly associated with the 3-year, 5-year, and 8-year OS were used to build a nomogram, followed by validation using receiver operating characteristic (ROC) curve, area under the curve (AUC), and calibration curve. A total of 3265 rectal cancer patients (training set: 2285; test set: 980) were included in the present study. A nomogram was developed to predict the 3-year, 5-year, and 8-year OS based on age, household income, total number of in situ/malignant tumors, CEA, T stage, N stage and perineural invasion. The nomogram showed good efficiency in predicting the 3-year, 5-year and 8-year OS with good AUC for the training set and test set, respectively. We established a nomogram for predicting the 3-year, 5-year, and 8-year OS of the rectal cancer patients, which showed good prediction efficiency for the OS after TNT.

Time interval between neoadjuvant radiation therapy and surgery and overall survival of rectal cancer patients

Time interval between neoadjuvant radiation therapy and surgery and overall survival of rectal cancer patients

Prediction models of locally advanced rectal cancer prognosis incorporating perioperative longitudinal clinical data: A retrospective longitudinal cohort study.

e15624 Background: For locally advanced rectal cancer, neoadjuvant chemoradiation(CCRT) followed by surgery is a standard treatment. Clinical T/N stage, whether pathological complete remission (pCR) after neoadjuvant CCRT or not are well recognized tumor features that predict disease prognosis. Although the parameters that determine pathologic stage are the strongest predictors of postoperative outcome, other clinical, molecular, and histologic features may influence prognosis independent of stage. The aim is to compare the predictive efficiency, between different models for overall survival in rectal cancer patients. Methods: This retrospective study is approved by TMU-JIRB(Protocol Number: N201809032).This is also a single institute study. 175 newly-diagnosed patients with locally advanced rectal cancer were retrospectively enrolled between Jan.2011 and Dec.2016. Basic information include sex, age, body weight, body height, previous medical history were recorded. CEA level, chemotherapy regimen, type of surgery, and distance between tumor and anal verge were also collected. Chemotherapy regimen of CCRT including 5FU+LV, capecitabine (Xeloda), FOLFOX, and FOLFIRI are recorded. Surgical resection was then followed 4-8 weeks after completion of CCRT. The 2010 AJCC tumor response grading (TRG) system was used to report treatment response of proctectomy samples. The progression free survival (PFS) and overall survival (OS) are then calculated. PFS is defined as survival without any recurrence or distant metastasis before Dec 31st, 2023. A comprehensive evaluation was conducted by incorporating all these machine learning models together. The aim was to compare their predictive efficiency, with models such as Random Forest, Naïve Bayes, KNeighbor, SVM, Decision Tree, Logistic Regression, XGBoost, and LightGBM. Results: 175 patients (104 men and 71 women) were enrolled in this study. The median follow up time was 7.1 yrs. The BMI was 24.05 ± 0.03 for men and 24.20 ± 0.07 for women. 32 (17.3%) patients achieved pCR, TRG 0. Clinical staging cT3 (78.8%) and nodal positive (78.8%) are the most common tumor feature in this study group. The median age of male and female patient is 58.6 and 56.1 respectively. Upon applying various machine learning models, different predictive accuracies emerged. The Random Forest, KNeighbor, and SVM models all achieved an accuracy of 0.9250. Naïve Bayes had an accuracy of 0.8620, while the SVC model registered at 0.9310, and XGBoost was slightly behind at 0.9286. SVC model is marking the best-performing model in this evaluation. Conclusions: This multicenter retrospective data-driven deep learning prediction model informed that SVC model yielded the best predictive results. Further prospective clinical trials and external validation of this prediction model are needed to further reinforce our findings.

Predictive role of preoperative sarcopenia for long-term survival in rectal cancer patients: A meta-analysis.

To identify the predictive role of sarcopenia in long-term survival among rectal cancer patients who underwent surgery based on available evidence. The Medline, EMBASE and Web of Science databases were searched up to October 20, 2023, for relevant studies. Overall survival (OS), disease-free survival (DFS) and cancer-specific survival (CSS) were the endpoints. Hazard ratios (HRs) and 95% confidence intervals (CIs) were combined to evaluate the association between sarcopenia and survival. Fifteen studies with 4283 patients were included. The pooled results demonstrated that preoperative sarcopenia significantly predicted poorer OS (HR = 2.07, 95% CI = 1.67-2.57, P<0.001), DFS (HR = 1.85, 95% CI = 1.39-2.48, P<0.001) and CSS (HR = 1.83, 95% CI = 1.31-2.56, P<0.001). Furthermore, subgroup analysis based on neoadjuvant therapy indicated that sarcopenia was a risk factor for worse OS and DFS in patients who received (OS: HR = 2.44, P<0.001; DFS: HR = 2.16, P<0.001) but not in those who did not receive (OS: HR = 2.44, P<0.001; DDFS: HR = 1.86, P = 0.002) neoadjuvant chemoradiotherapy. In addition, subgroup analysis based on sample size and ethnicity showed similar results. Preoperative sarcopenia is significantly related to poor survival in surgical rectal cancer patients and could serve as a novel and valuable predictor of long-term prognosis in these patients.

Predictive value of FCGBP expression for treatment response and survival in rectal cancer patients undergoing chemoradiotherapy.

Despite neoadjuvant chemoradiotherapy (CRT) being the established standard for treating advanced rectal cancer, clinical outcomes remain suboptimal, necessitating the identification of predictive biomarkers for improved treatment decisions. Previous studies have hinted at the oncogenic properties of the Fc fragment of IgG binding protein (FCGBP) in various cancers; however, its clinical significance in rectal cancer remains unclear. In this study, we first conducted an analysis of a public transcriptome comprising 46 rectal cancer patients. Focusing on cell adhesion during data mining, we identified FCGBP as the most upregulated gene associated with CRT resistance. Subsequently, we assessed FCGBP immunointensity using immunohistochemical staining on 343 rectal cancer tissue blocks. Elevated FCGBP immunointensity correlated with lymph node involvement before treatment (p = 0.001), tumor invasion, and lymph node involvement after treatment (both p < 0.001), vascular invasion (p = 0.001), perineural invasion (p = 0.041), and reduced tumor regression (p < 0.001). Univariate analysis revealed a significant association between high FCGBP immunoexpression and inferior disease-specific survival, local recurrence-free survival, and metastasis-free survival (all p ≤ 0.0002). Furthermore, high FCGBP immunoexpression independently emerged as an unfavorable prognostic factor for all three survival outcomes in the multivariate analysis (all p ≤ 0.025). Enriched pathway analysis substantiated the role of FCGBP in conferring resistance to radiation. In summary, our findings suggest that elevated FCGBP immunoexpression in rectal cancer significantly correlates with a poor response to CRT and diminished patient survival. FCGBP holds promise as a valuable prognostic biomarker for rectal cancer patients undergoing CRT.

Predicting survival in locally advanced rectal cancer with effective chemoradiotherapy response

BackgroundLocally advanced rectal cancer patients often display favorable responses and favorable oncologic outcomes. Due to the low recurrence rate, there is scarcity of studies investigating the prognostic factors influencing their survival. Therefore, our study sought to assess the prognostic factors associated with survival in rectal cancer patients who achieved either a pathologic complete response or a pathologic stage I after neoadjuvant chemoradiotherapy combined with radical resection. MethodsIn this retrospective study, we analyzed data from cohort of 1394 patients diagnosed with locally advanced rectal cancer who underwent neoadjuvant chemoradiotherapy combined with total mesorectal excision from January 2008 to April 2017. Finally, we selected 474 (34.2 %) who exhibited either a pathologic complete response or attained pathologic stage I following the treatment. Subsequently, we analyzed the prognostic factors influencing disease-free and overall survival. ResultsA total of 161 (34 %) achieved a pathologic complete response. Our analysis revealed that circumferential resection margin and the administration of adjuvant chemotherapy were prognostic factors for disease-free survival (p = 0.011, p = 0.022). Furthermore, factors influencing overall survival included the clinical N stage and administration of adjuvant chemotherapy (p = 0.035, p = 0.015). ConclusionIn conclusion, the circumferential resection margin, clinical N stage, and administration of adjuvant chemotherapy were prognostic factors for survival in patients showing good response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. For patients with a positive circumferential resection margin and clinical N (+) stage, intensive follow-up might be needed to achieve favorable oncologic outcomes. Also, we recommend considering adjuvant chemotherapy as a beneficial treatment approach for these patients.

Predicting rectal cancer prognosis from histopathological images and clinical information using multi-modal deep learning.

The objective of this study was to provide a multi-modal deep learning framework for forecasting the survival of rectal cancer patients by utilizing both digital pathological images data and non-imaging clinical data. The research included patients diagnosed with rectal cancer by pathological confirmation from January 2015 to December 2016. Patients were allocated to training and testing sets in a randomized manner, with a ratio of 4:1. The tissue microarrays (TMAs) and clinical indicators were obtained. Subsequently, we selected distinct deep learning models to individually forecast patient survival. We conducted a scanning procedure on the TMAs in order to transform them into digital pathology pictures. Additionally, we performed pre-processing on the clinical data of the patients. Subsequently, we selected distinct deep learning algorithms to conduct survival prediction analysis using patients' pathological images and clinical data, respectively. A total of 292 patients with rectal cancer were randomly allocated into two groups: a training set consisting of 234 cases, and a testing set consisting of 58 instances. Initially, we make direct predictions about the survival status by using pre-processed Hematoxylin and Eosin (H&E) pathological images of rectal cancer. We utilized the ResNest model to extract data from histopathological images of patients, resulting in a survival status prediction with an AUC (Area Under the Curve) of 0.797. Furthermore, we employ a multi-head attention fusion (MHAF) model to combine image features and clinical features in order to accurately forecast the survival rate of rectal cancer patients. The findings of our experiment show that the multi-modal structure works better than directly predicting from histopathological images. It achieves an AUC of 0.837 in predicting overall survival (OS). Our study highlights the potential of multi-modal deep learning models in predicting survival status from histopathological images and clinical information, thus offering valuable insights for clinical applications.

Influence of Certification Program on Treatment Quality and Survival for Rectal Cancer Patients in Germany: Results of 13 Certified Centers in Collaboration with AN Institute.

The certification of oncological units as colorectal cancer centers (CrCCs) has been proposed to standardize oncological treatment and improve the outcomes for patients with colorectal cancer (CRC). The proportion of patients with CRC in Germany that are treated by a certified center is around 53%. Lately, the effect of certification on the treatment outcomes has been critically discussed. Our aim was to investigate the treatment outcomes in patients with rectal carcinoma at certified CrCCs, in German hospitals of different medical care levels. We performed a retrospective analysis of a prospective, multicentric database (AN Institute) of adult patients who underwent surgery for rectal carcinoma between 2002 and 2016. We included 563 patients from 13 hospitals of different medical care levels (basic, priority, and maximal care) over periods of 5 years before and after certification. The certified CrCCs showed a significant increase in the use of laparoscopic approach for rectal cancer surgery (5% vs. 55%, p < 0.001). However, we observed a significantly prolonged mean duration of surgery in certified CrCCs (161 Min. vs. 192 Min., p < 0.001). The overall morbidity did not improve (32% vs. 38%, p = 0.174), but the appearance of postoperative stool fistulas decreased significantly in certified CrCCs (2% vs. 0%, p = 0.036). Concerning the overall in-hospital mortality, we registered a positive trend in certified centers during the five-year period after the certification (5% vs. 3%, p = 0.190). The length of preoperative hospitalization (preop. LOS) was shortened significantly (4.71 vs. 4.13 days, p < 0.001), while the overall length of in-hospital stays was also shorter in certified CrCCs (20.32 vs. 19.54 days, p = 0.065). We registered a clear advantage in detailed, high-quality histopathological examinations regarding the N, L, V, and M.E.R.C.U.R.Y. statuses. In the performed subgroup analysis, a significantly longer overall survival after certification was registered for maximal medical care units (p = 0.029) and in patients with UICC stage IV disease (p = 0.041). In patients with UICC stage III disease, we registered a slightly non-significant improvement in the disease-free survival (UICC III: p = 0.050). The results of the present study indicate an improvement in terms of the treatment quality and outcomes in certified CrCCs, which is enforced by certification-specific aspects such as a more differentiated surgical approach, a lower rate of certain postoperative complications, and a multidisciplinary approach. Further prospective clinical trials are necessary to investigate the influence of certification in the treatment of CRC patients.

Influence of skeletal muscle and intermuscular fat on postoperative complications and long-term survival in rectal cancer patients.

The body composition of patients with rectal cancer potentially affects postoperative outcomes. This study explored the correlations between skeletal muscle and adipose tissue quantified by computed tomography (CT) with postoperative complications and long-term prognosis in patients with rectal cancer after surgical resection. This retrospective cohort study included patients with rectal cancer who underwent surgical resection at the Wuhan Union Hospital between 2014 and 2018. CT images within 3months prior to the surgery were used to quantify the indices of skeletal muscle and adipose tissue at the levels of the third lumbar vertebra (L3) and umbilicus. Optimal cut-off values for each index were defined separately for males and females. Associations between body composition and postoperative complications, overall survival (OS), and disease-free survival (DFS) were evaluated using logistic and Cox proportional hazards models. We included 415 patients (240 males and 175 females; mean age: 57.8±10.5years). At the L3 level, a high skeletal muscle density (SMD; hazard ratio [HR]: 0.357, 95% confidence interval [CI]: 0.191-0.665, P=0.001; HR: 0.571, 95% CI: 0.329-0.993, P=0.047) and a high skeletal muscle index (SMI; HR: 0.435, 95% CI 0.254-0.747, P=0.003; HR: 0.568, 95% CI: 0.359-0.897, P=0.015) were independent prognostic factors for better OS and DFS. At the umbilical level, a large intermuscular fat area (IMFA; HR: 1.904, 95% CI: 1.068-3.395, P=0.029; HR: 2.064, 95% CI: 1.299-3.280, P=0.002) was an independent predictive factor for worse OS and DFS, and a high SMI (HR: 0.261, 95% CI: 0.132-0.517, P<0.001; HR: 0.595, 95% CI: 0.387-0.913, P=0.018) was an independent prognostic factor for better OS and DFS. The models combining body composition and clinical indicators had good predictive abilities for OS. The receiver operating characteristic areas under the curve were 0.848 and 0.860 at the L3 and umbilical levels, respectively (both P<0.05). No correlations existed between CT-quantified body composition parameters and postoperative complications. However, a high SMD and high SMI were significantly associated with longer OS and DFS at the L3 level, whereas a large IMFA and low SMI were associated with worse OS and DFS at the umbilical level. Combining CT-quantified body composition and clinical indicators could help physicians predict the prognosis of patients with rectal cancer after surgery.

Quantitative MRI-based radiomics analysis identifies blood flow feature associated to overall survival for rectal cancer patients.

Radiomics objectively quantifies image information through numerical metrics known as features. In this study, we investigated the stability of magnetic resonance imaging (MRI)-based radiomics features in rectal cancer using both anatomical MRI and quantitative MRI (qMRI), when different methods to define the tumor volume were used. Second, we evaluated the prognostic value of stable features associated to 5-year progression-free survival (PFS) and overall survival (OS). On a 1.5T MRI scanner, 81 patients underwent diagnostic MRI, an extended diffusion-weighted sequence with calculation of the apparent diffusion coefficient (ADC) and a multiecho dynamic contrast sequence generating both dynamic contrast-enhanced and dynamic susceptibility contrast (DSC) MR, allowing quantification of Ktrans, blood flow (BF) and area under the DSC curve (AUC). Radiomic features were extracted from T2w images and from ADC, Ktrans, BF and AUC maps. Tumor volumes were defined with three methods; machine learning, deep learning and manual delineations. The interclass correlation coefficient (ICC) assessed the stability of features. Internal validation was performed on 1000 bootstrap resamples in terms of discrimination, calibration and decisional benefit. For each combination of image and volume definition, 94 features were extracted. Features from qMRI contained higher prognostic potential than features from anatomical MRI. When stable features (> 90% ICC) were compared with clinical parameters, qMRI features demonstrated the best prognostic potential. A feature extracted from the DSC MRI parameter BF was associated with both PFS (p = 0.004) and OS (p = 0.004). In summary, stable qMRI-based radiomics features was identified, in particular, a feature based on BF from DSC MRI was associated with both PFS and OS.

Analysis of the impact of radiotherapy and surgical treatment regimens based on the SEER database on the survival outcomes of rectal cancer patients over 70 years.

This study evaluates the impact of different combinations of treatment regimens, such as additional radiation, chemotherapy, and surgical treatments, on the survival of elderly rectal cancer patients ≥ 70 years of age to support physicians' clinical decision-making. Data from a sample of elderly rectal cancer patients aged ≥ 70 years diagnosed from 2005-2015 from the US surveillance, epidemiology, and end results (SEER) database were retrospectively analyzed. The best cut-off point was selected using the x-tile software for the three continuity indices: age, tumor size, and number of regional lymph nodes. All patients were categorized into either the neoadjuvant radiotherapy and surgery group (R_S group), the surgical treatment group (S group), or the surgery and adjuvant radiotherapy group (S_R group). The propensity score allocation was used to match each included study subject in a 1:1 ratio, and the restricted mean survival time method (RMST) was used to predict the mean survival of rectal cancer patients within 5 and 10 years. The prognostic risk factors for rectal cancer patients were determined using univariate and multivariate Cox regression analyses, and nomograms were constructed. A subgroup stratification analysis of patients with different treatment combination regimens was performed using the Kaplan-Meier method, and log-rank tests were used for between-group comparisons. The model's predictive accuracy was assessed by receiver operating characteristic (ROC) curves, correction curves, and a clinical decision curve analysis (DCA). A total of 7556 cases of sample data from 2005 to 2015 were included, which were categorized into 6639 patients (87.86%) in the S group, 408 patients (5.4%) in the R_S group, and 509 patients (6.74%) in the S_R group, according to the relevant order of radiotherapy and surgery. After propensity score matching (PSM), the primary clinical characteristics of the groups were balanced and comparable. The difference in the mean survival time before and after PSM was not statistically significant in both R_S and S groups (P value > 0.05), and the difference in the mean survival time after PSM was statistically substantial in S_R and S groups (P value < 0.05). In the multifactorial Cox analysis, the M1 stage and Nodes ≥ 9 were independent risk factors. An age between 70-75 was an independent protective factor for patients with rectal cancer in the R_S and S groups. The Marital_status, T4 stage, N2 stage, M1 stage, and Nodes ≥ 9 were independent risk factors for patients with rectal cancer in the S_R and S groups, and an age between 70-81 was an independent protective factor. The ROC curve area, the model C index, and the survival calibration curve suggested good agreement between the actual and predicted values of the model. The DCA for 3-year, 5-year, and 10-year survival periods indicated that the model had some potential for application. The results of the study showed no significant difference in the overall survival (OS) between elderly patients who received neoadjuvant radiotherapy and surgery and those who received surgery alone; elderly patients who received surgery and adjuvant radiotherapy had some survival benefits compared with those who received surgery alone, though the benefit of adjuvant radiotherapy was not significant. Therefore, radiotherapy for rectal cancer patients older than 70 years old should be based on individual differences in condition, and a precise treatment plan should be developed.

A Novel CT-Based Radiomic Analysis for the Prediction of Overall Survival in Rectal Cancer Patients Treated with Short Course RT Total Neoadjuvant Therapy

Radiomics is a method based on the extraction of features from medical images, enriching the construction of predictive models for aiding patient management. The purpose of this study is to develop a model based on radiomic features for the overall survival (OS) in rectal cancer patients treated with total neoadjuvant therapy (TNT). Prediction of neoadjuvant rectal (NAR) score was also preliminarily evaluated. A retrospective cohort including patients treated with TNT at a single center between 2017-2021 was identified. TNT consisted of short course pelvic radiotherapy (25 Gy in 5 fractions) followed by FOLFOX or XELOX. A single CTV included mesorectum and nodal regions. Two endpoints were used: OS at 3 years (from the start of RT) and NAR score binary classification. A cut-off of 8.4 was used for NAR based on a previous local study. CTV radiomic features were extracted from simulation CTs using Pyradiomics v3. Features were extracted from the unfiltered images as well as from a set of eight filtered images. Radiomic features consist of 7 classes: shape, first order statistics, glcm, gldm, glrlm, glszm and ngtdm. 1967 initial features were extracted. Features with 0 standard deviation were removed as well as highly correlated ones (0.99 Pearson Coefficient). Dimension reduction using PCA was applied to each feature class, preserving 95% of the explained variance. Supervised algorithms were evaluated using a 10-fold cross-validation technique. Demographic and clinical features were also considered (age, sex, cT stage and N positivity) RESULTS: Eighty-two patients were included (37 females). Median age was 61.5 years (IQR 53-70). Clinical staging was comprised of 5 cT2 patients, 57 cT3 and 20 cT4, with 69 cN+. 3y-OS was 72%, with a median follow-up of 43.7 months (IQR 35.3-48.6). For the 3y-OS endpoint two prediction models were developed. First, a reference model (KNN classifying algorithm) based on demographic and clinical features only resulting in an AUC of 0.659 (SD 0.029). The second model (CART classifying algorithm) included demographic, clinical and radiomic features resulting in an AUC of 0.744 (SD 0.032). For the binary NAR classification model, a subset of 77 patients was used. Twenty-three presented a NAR<8.4 and 54 a NAR≥8.4. The CART algorithm presented the best performance with an AUC of 0.808 (SD 0.037) considering demographic, clinical and radiomic features. Radiomic models were developed for 3y-OS prediction and binary NAR classification. The addition of radiomic features to clinical and demographic information improved the predictive capability of the 3y-OS model. Radiomic prediction of NAR makes it a promising tool for the identification of potential short course RT TNT responders.

Preoperative Radiotherapy Decision-Tree for Rectal Cancer Patients: A Real-World Analysis Based on the Swedish Colorectal Cancer Registry

BackgroundThere are 3 widely used preoperative radiotherapy (RT) procedures in rectal cancer treatment including long-course RT (LRT), short-course RT with delayed surgery (SRTW), and short-course RT with immediate surgery (SRT). However, further evidence is required to determine which treatment option results in more optimal patient survival. MethodsThis Swedish Colorectal Cancer Registry-based retrospective study of real-world data included 7766 stage I–III rectal cancer patients, of which 2982, 1089, 763, and 2932 patients received no RT (NRT), LRT, SRTW, and SRT, respectively. The Kaplan-Meier survival curve and Cox proportional hazard multivariate model were used to identify potential risk factors and to examine the independent association of RT with patient survival after adjusting for baseline confounding factors. ResultsRT effects on survival differed by age and clinical T stage (cT) subgroups. Subsequent survival analysis by age and cT subgroups confirmed that patients ≥70 years old with cT4 benefited from any RT (P < .001, NRT as reference) and equally from any RT (P > .05 pairwise between RTs). In contrast, for cT3 patients ≥70 years, SRT and LRT were associated with better survival than SRTW (P < .001). In patients <70 years, LRT and SRTW had superior survival benefits in cT4 patients but inferior to SRT (P < .001); SRT was the only effective treatment in the cT3N+ subgroup (P = .032); patients with cT3N0 and <70 years did not benefit from any RT. ConclusionThis study suggests that preoperative RT strategies may have varying effects on the survival of rectal cancer patients, depending on their age and clinical stage.

ASO Author Reflections: Understanding the Association between Extramural Venous Invasion and Survival in Rectal Cancer Patients.

ASO Author Reflections: Understanding the Association between Extramural Venous Invasion and Survival in Rectal Cancer Patients.

Prognostic Consequences of Anastomotic Leakage in Patients after Laparoscopic Rectal Cancer Resection

Introduction: Anastomotic leakage is the horrendous complication after the laparoscopic rectal cancer resection. The main objective of the present study is to assess the relationship between AL, tumor recurrence and overall survival in rectal cancer patients treated with laparoscopic surgery. Methods: One hundred seventy patients who were diagnosed as rectal cancer with stages 1-3 followed by laparoscopic surgery between March 2012 and February 2017 from Beijing Friendship Hospital were evaluated retrospectively. Twelve factors after laparoscopic surgery were evaluated by the univariate analysis and multivariate analysis. The disease-free survival and overall survival curves were performed by the Kaplan- Meier method and were compared with the log-rank test. Results: The incidence rate of AL was 18.82%. The multivariate analysis showed AL [Hazard Ratio (HR); 3.114, 95% Confidence Interval (CI); 1.029-9.428, P; 0.044], advanced TNM stage [HR; 12.714, 95%CI; 2.364-68.377, P; 0.003] and intraoperative blood loss [HR; 0.269, 95%CI; 0.085-0.859, P; 0.027] were associated with poor OS. In addition, AL [Hazard Ratio (HR); 3.134, 95% Confidence Interval (CI); 1.274-7.711, P; 0.013] and advanced TNM stage [HR; 88.378, 95%CI; 2.231-31.465, P; 0.002] were associated with poor DFS. The logrank test p-value for OS and DFS curve by Kaplan-Meier method were 0.382 and 0.108 respectively. Conclusion: Anastomotic leakage was associated with poorer overall survival and increased risk of tumor recurrence. Moreover, there was no difference in three-year/five-year overall survival rate and disease-frees survival rate between the leakage and non-leakage groups in the present analysis.

Artificial intelligence fusion for predicting survival of rectal cancer patients using immunohistochemical expression of Ras homolog family member B in biopsy.

The process of biomarker discovery is being accelerated with the application of artificial intelligence (AI), including machine learning. Biomarkers of diseases are useful because they are indicators of pathogenesis or measures of responses to therapeutic treatments, and therefore, play a key role in new drug development. Proteins are among the candidates for biomarkers of rectal cancer, which need to be explored using state-of-the-art AI to be utilized for prediction, prognosis, and therapeutic treatment. This paper aims to investigate the predictive power of Ras homolog family member B (RhoB) protein in rectal cancer. This study introduces the integration of pretrained convolutional neural networks and support vector machines (SVMs) for classifying biopsy samples of immunohistochemical expression of protein RhoB in rectal-cancer patients to validate its biologic measure in biopsy. Features of the immunohistochemical expression images were extracted by the pretrained networks and used for binary classification by the SVMs into two groups of less and more than 5-year survival rates. The fusion of neural search architecture network (NASNet)-Large for deep-layer feature extraction and classifier using SVMs provided the best average classification performance with a total accuracy = 85%, prediction of survival rate of more than 5 years = 90%, and prediction of survival rate of less than 5 years = 75%. The finding obtained from the use of AI reported in this study suggest that RhoB expression on rectal-cancer biopsy can be potentially used as a biomarker for predicting survival outcomes in rectal-cancer patients, which can be informative for clinical decision making if the patient would be recommended for preoperative therapy.

Abdominal Fat Characteristics and Mortality in Rectal Cancer: A Retrospective Study.

The aim of this study was to evaluate the association of adipose tissue characteristics with survival in rectal cancer patients. All consecutive patients, diagnosed with stage II-IV rectal cancer between 2010-2016 using baseline unenhanced Computed Tomography (CT), were included. Baseline total, subcutaneous and visceral adipose tissue areas (TAT, SAT, VAT) and densities (TATd, SATd, VATd) at third lumbar vertebra (L3) were retrospectively measured. The association of these tissues with cancer-specific and progression-free survival (CCS, PFS) was assessed by using competitive risk models adjusted by age, sex and stage. Among the 274 included patients (median age 70 years, 41.2% females), the protective effect of increasing adipose tissue area on survival could be due to random fluctuations (e.g., sub-distribution hazard ratio-SHR for one cm2 increase in SAT = 0.997; 95%confidence interval-CI = 0.994-1.000; p = 0.057, for CSS), while increasing density was associated with poorer survival (e.g., SHR for one Hounsfield Unit-HU increase in SATd = 1.03, 95% CI = 1.01-1.05, p = 0.002, for CSS). In models considering each adipose tissue area and respective density, the association with CSS tended to disappear for areas, while it did not change for TATd and SATd. No association was found with PFS. In conclusion, adipose tissue density influenced survival in rectal cancer patients, raising awareness on a routinely measurable variable that requires more research efforts.

A transformation perspective on marginal and conditional models.

Clustered observations are ubiquitous in controlled and observational studies and arise naturally in multicenter trials or longitudinal surveys. We present a novel model for the analysis of clustered observations where the marginal distributions are described by a linear transformation model and the correlations by a joint multivariate normal distribution. The joint model provides an analytic formula for the marginal distribution. Owing to the richness of transformation models, the techniques are applicable to any type of response variable, including bounded, skewed, binary, ordinal, or survival responses. We demonstrate how the common normal assumption for reaction times can be relaxed in the sleep deprivation benchmark data set and report marginal odds ratios for the notoriously difficult toe nail data. We furthermore discuss the analysis of two clinical trials aiming at the estimation of marginal treatment effects. In the first trial, pain was repeatedly assessed on a bounded visual analog scale and marginal proportional-odds models are presented. The second trial reported disease-free survival in rectal cancer patients, where the marginal hazard ratio from Weibull and Cox models is of special interest. An empirical evaluation compares the performance of the novel approach to general estimation equations for binary responses and to conditional mixed-effects models for continuous responses. An implementation is available in the tram add-on package to the R system and was benchmarked against established models in the literature.

Computed tomography-measured body composition and survival in rectal cancer patients: a Swedish cohort study

BackgroundThe association between body composition and survival in rectal cancer patients is still unclear. Therefore, we aimed to evaluate the impact of computed tomography (CT)-measured body composition on survival in rectal cancer patients, stratifying our analyses by sex, tumour location, tumour stage and radiotherapy.MethodsThis retrospective cohort study included 173 patients with rectal adenocarcinoma. CT colonography scans at the time of diagnosis were used to assess the skeletal muscle index (SMI) and the visceral adipose tissue area (VAT). The patients were divided into a low or high SMI group and a low or high VAT group according to previously defined cutoff values. Endpoints included cancer-specific survival (CSS) and overall survival (OS).ResultsIn all patients, low SMI was associated with worse CSS (HR, 2.63; 95% CI, 1.35–5.12; P = 0.004) and OS (HR, 3.57; 95% CI, 2.01–6.34; P < 0.001) compared to high SMI. The differences remained significant after adjusting for potential confounders (CSS: adjusted HR, 2.28; 95% CI, 1.13–4.58; P = 0.021; OS: adjusted HR, 3.17; 95% CI, 1.73–5.82; P < 0.001). Low SMI was still related to a poor prognosis after stratifying by sex, tumour location, stage and radiotherapy (P < 0.05). High VAT was associated with better CSS (HR, 0.31; 95% CI, 0.11–0.84; P = 0.022) and OS (HR, 0.40; 95% CI, 0.17–0.97; P = 0.044) compared to low VAT among men with rectal cancer ≤ 10 cm from the anal verge. High VAT was associated with worse CSS (HR, 4.15; 95% CI, 1.10–15.66; P = 0.036) in women with rectal cancer ≤ 10 cm from the anal verge.ConclusionsLow SMI was associated with worse survival. High VAT predicted better survival in men but worse survival in women. The results suggest that CT-measured body composition is a useful tool for evaluating the prognosis of rectal cancer patients and demonstrate the need to include the sex and the tumour location in the analyses.

High SLC28A2 expression endows an inferior survival for rectal cancer patients managed by neoadjuvant CCRT.

For rectal cancer patients with stage T3-4 disease or positive lymph node, neoadjuvant concurrent chemoradiotherapy (CCRT) has become the standard treatment, but the clinical outcomes are still far from satisfactory. Accordingly, a more precise predictive tool such as genetic biomarkers is urgently required to optimize therapy decisions. Colorectal cancer (CRC) development has been considerably correlated with cellular metabolic process involving nucleotides, but the underlying molecular mechanisms remain unclear. In this study, we employed a transcriptome dataset comprising 46 rectal adenocarcinoma patients undergoing preoperative CCRT and focused on nucleobase-containing compound metabolic process (GO: 0055134) for data mining. We identified solute carrier family 28 member 2 (SLC28A2) as the most considerably upregulated gene among rectal cancer patients with CCRT resistance. Afterwards, there were a total of 172 rectal cancer tissue blocks procuring from our biobank, and the immunointensity of SLC28A2 was appraised utilizing immunohistochemical staining. Strong SLC28A2 immunointensity was significantly linked to female patients (p=0.032), vascular invasion (p=0.021), and post-CCRT tumor invasion and regional lymph node involvement (p<0.001 and p=0.005). Notably, patients with strong SLC28A2 immunointensity had no tumor downstaging (p<0.001). Univariate analysis revealed that high SLC28A2 immunoexpression was considerably unfavorably linked to all three endpoints: local recurrence-free survival (LRFS), metastasis-free survival (MeFS), and disease-specific survival (DSS) (all p≤0.0333). Moreover, both high SLC28A2 immunoexpression and low tumor regression grade were independently unfavorable prognostic factors for all three endpoints (all p≤0.013) in the multivariate analysis. Utilizing function prediction analysis, SLC28A2 upregulation was more likely to be linked with stem cell homeostasis in rectal cancer. In brief, we demonstrated that high SLC28A2 immunoexpression is substantially linked to an advanced stage, poor response to CCRT, and worse patient survival. Consequently, SLC28A2 expression can be a valuable predictive and prognostic marker for rectal cancer patients and be an encouraging therapeutic target for those with CCRT resistance.