Development and validation of a novel classification scheme for combining pathological T stage and log odds of positive lymph nodes for colon cancer.

AIM: Log Odds of Positive Lymph Nodes (LODDS) have a better predictive ability than N stage for colon cancer. However, the prognostic value of developing a novel prognostic classification by combining T stage and LODDS (TLODDS) for colon cancer remains unknown. Therefore, in the present study, we aimed to develop a TLODDS classification for colon cancer, and assess whether or not the novel TLODDS classification could improve survival stratification by comparing its discrimination, model-fitting, and net benefits, with the American Joint Committee on Cancer (AJCC) Tumor/Node/Metastasis (TNM) classification. METHODS: 45,558 Western colon cancers were identified in the Surveillance, Epidemiology, and End Results database as a training set. A novel LODDS stage was established and patients with similar survival rates were grouped by combining T and LODDS stages to develop a novel TLODDS classification. The TLODDS classification was further assessed in a Chinese validation set of 3,515 colon cancers and an application set of 3,053 rectal cancers. RESULTS: We developed a novel TLODDS classification that incorporated 7 stages: stage I (T1LODDS1), IIA (T2LODDS1, T1LODDS2, T1LODDS3), IIB (T2LODDS2-3, T3LODDS1, T1LODDS4), IIC (T3LODDS2, T2LODDS4, T4aLODDS1), IIIA (T3LODDS3, T1-2LODDS5, T4bLODDS1, T4aLODDS2), IIIB (T3LODDS4-5, T4aLODDS3-4, T4bLODDS2) and IIIC (T4bLODDS3-5, T4aLODDS5). In the training set, it showed significantly better discrimination (area under the receiver operating characteristic (ROC) curve, 0.691 vs. 0.664, P < 0.001), better model-fitting (Akaike information criteria, 265,644 vs. 267,410), and superior net benefits, than the latest AJCC TNM classification. The predictive performance of the TLODDS classification was further validated in colon cancers and was successfully applied in rectal cancers with regards to both overall and disease-free survival. CONCLUSIONS: The TLODDS classification has better discriminatory ability, model-fitting, and net benefits than the existing TNM classification, and represents an alternative to the current TNM classifications for colon and rectal cancers.

Evaluation of a 55-gene classifier as a prognostic biomarker for adjuvant chemotherapy in stage III colon cancer patients.

BACKGROUND: Adjuvant chemotherapy reduces the risk of recurrence of stage III colon cancer (CC). However, more effective prognostic and predictive biomarkers are needed for better treatment stratification of affected patients. Here, we constructed a 55-gene classifier (55GC) and investigated its utility for classifying patients with stage III CC. METHODS: We retrospectively identified patients aged 20-79 years, with stage III CC, who received adjuvant chemotherapy with or without oxaliplatin, between the years 2009 and 2012. RESULTS: Among 938 eligible patients, 203 and 201 patients who received adjuvant chemotherapy with and without oxaliplatin, respectively, were selected by propensity score matching. Of these, 95 patients from each group were analyzed, and their 5-year relapse-free survival (RFS) rates with and without oxaliplatin were 73.7 and 77.1%, respectively. The hazard ratios for 5-year RFS following adjuvant chemotherapy (fluoropyrimidine), with and without oxaliplatin, were 1.241 (95% CI, 0.465-3.308; P = 0.67) and 0.791 (95% CI, 0.329-1.901; P = 0.60), respectively. Stratification using the 55GC revealed that 52 (27.3%), 78 (41.1%), and 60 (31.6%) patients had microsatellite instability (MSI)-like, chromosomal instability (CIN)-like, and stromal subtypes, respectively. The 5-year RFS rates were 84.3 and 72.0% in patients treated with and without oxaliplatin, respectively, for the MSI-like subtype (HR, 0.495; 95% CI, 0.145-1.692; P = 0.25). No differences in RFS rates were noted in the CIN-like or stromal subtypes. Stratification by cancer sidedness for each subtype showed improved RFS only in patients with left-sided primary cancer treated with oxaliplatin for the MSI-like subtype (P = 0.007). The 5-year RFS rates of the MSI-like subtype in left-sided cancer patients were 100 and 53.9% with and without oxaliplatin, respectively. CONCLUSIONS: Subclassification using 55GC and tumor sidedness revealed increased RFS in patients within the MSI-like subtype with stage III left-sided CC treated with fluoropyrimidine and oxaliplatin compared to those treated without oxaliplatin. However, the predictive power of 55GC subtyping alone did not reach statistical significance in this cohort, warranting larger prospective studies. TRIAL REGISTRATION: The study protocol was registered in the University Hospital Medical Education Network (UMIN) clinical trial registry (UMIN study ID: 000023879 ).

Comparison of model fit and discriminatory ability of M category as defined by the 7th and 8th editions of the tumor-node-metastasis classification of colorectal cancer and the 9th edition of the Japanese classification.

BACKGROUND: In transitioning from the 7th edition of the tumor-node-metastasis classification (TNM-7) to the 8th edition (TNM-8), colorectal cancer with peritoneal metastasis was newly categorized as M1c. In the 9th edition of the Japanese Classification of colorectal, appendiceal, and anal carcinoma (JPC-9), M1c is further subdivided into M1c1 (without other organ involvement) and M1c2 (with other organ involvement). This study aimed to compare the model fit and discriminatory ability of the M category of these three classification systems, as no study to date has made this comparison. METHODS: The study population consisted of stage IV colorectal cancer patients who were referred to the National Cancer Center Hospital from 2000 to 2017. The Akaike information criterion (AIC), Harrell's concordance index (C-index), and time-dependent receiver operating characteristic (ROC) curves were used to compare the three classification systems. Subgroup analyses, stratified by initial treatment year, were also performed. RESULTS: According to TNM-8, 670 (55%) patients had M1a, 273 (22%) had M1b, and 279 (23%) had M1c (87 M1c1 and 192 M1c2 using JPC-9) tumors. Among the three classification systems, JPC-9 had the lowest AIC value (JPC-9: 10546.3; TNM-7: 10555.9; TNM-8: 10585.5), highest C-index (JPC-9: 0.608; TNM-7: 0.598; TNM-8: 0.599), and superior time-dependent ROC curves throughout the observation period. Subgroup analyses were consistent with these results. CONCLUSIONS: While the revised M category definition did not improve model fit and discriminatory ability from TNM-7 to TNM-8, further subdivision of M1c in JPC-9 improved these parameters. These results support further revisions to M1 subcategories in future editions of the TNM classification system.

Intratumor CMS Heterogeneity Impacts Patient Prognosis in Localized Colon Cancer.

PURPOSE: The consensus molecular subtypes (CMS) represent a significant advance in the understanding of intertumor heterogeneity in colon cancer. Intratumor heterogeneity (ITH) is the new frontier for refining prognostication and understanding treatment resistance. This study aims at deciphering the transcriptomic ITH of colon cancer and understanding its potential prognostic implications. EXPERIMENTAL DESIGN: We deconvoluted the transcriptomic profiles of 1,779 tumors from the PETACC8 trial and 155 colon cancer cell lines as weighted sums of the four CMSs, using the Weighted In Silico Pathology (WISP) algorithm. We assigned to each tumor and cell line a combination of up to three CMS subtypes with a threshold above 20%. RESULTS: Over 55% of tumors corresponded to mixtures of at least two CMSs, demonstrating pervasive ITH in colon cancer. Of note, ITH was associated with shorter disease-free survival (DFS) and overall survival, [HR, 1.34; 95% confidence interval (CI; 1.12-1.59), 1.40, 95% CI (1.14-1.71), respectively]. Moreover, we uncovered specific combinations of CMS associated with dismal prognosis. In multivariate analysis, ITH represents the third parameter explaining DFS variance, after T and N stages. At a cellular level, combined WISP and single-cell transcriptomic analysis revealed that most colon cancer cell lines are a mixture of cells falling into different CMSs, indicating that ITH may correspond to distinct functional statuses of colon cancer cells. CONCLUSIONS: This study shows that CMS-based transcriptomic ITH is frequent in colon cancer and impacts its prognosis. CMS-based transcriptomic ITH may correspond to distinct functional statuses of colon cancer cells, suggesting plasticity between CMS-related cell populations. Transcriptomic ITH deserves further assessment in the context of personalized medicine.

A framework model using multifilter feature selection to enhance colon cancer classification.

Gene expression profiles can be utilized in the diagnosis of critical diseases such as cancer. The selection of biomarker genes from these profiles is significant and crucial for cancer detection. This paper presents a framework proposing a two-stage multifilter hybrid model of feature selection for colon cancer classification. Colon cancer is being extremely common nowadays among other types of cancer. There is a need to find fast and an accurate method to detect the tissues, and enhance the diagnostic process and the drug discovery. This paper reports on a study whose objective has been to improve the diagnosis of cancer of the colon through a two-stage, multifilter model of feature selection. The model described deals with feature selection using a combination of Information Gain and a Genetic Algorithm. The next stage is to filter and rank the genes identified through this method using the minimum Redundancy Maximum Relevance (mRMR) technique. The final phase is to further analyze the data using correlated machine learning algorithms. This two-stage approach, which involves the selection of genes before classification techniques are used, improves success rates for the identification of cancer cells. It is found that Decision Tree, K-Nearest Neighbor, and Naïve Bayes classifiers had showed promising accurate results using the developed hybrid framework model. It is concluded that the performance of our proposed method has achieved a higher accuracy in comparison with the existing methods reported in the literatures. This study can be used as a clue to enhance treatment and drug discovery for the colon cancer cure.

Human polyomaviruses genomes in clinical specimens of colon cancer patients.

Colon cancer is the third cause of cancer death in the developed countries. Some environmental factors are involved in its pathogenesis, including viral infections. The possible involvement of human polyomaviruses (HPyVs) in colon cancer pathogenesis has been previously reported, leading to inconsistent conclusions. Clinical specimens were collected from 125 colon cancer patients. Specifically, 110 tumor tissues, 55 negative surgical margins, and 39 peripheral blood samples were analyzed for the presence of six HPyVs: JC polyomavirus (JCPyV), BK polyomavirus (BKPyV), Merkel cell PyV (MCPyV), HPyV -6, -7, and -9 by means of DNA isolation and subsequent duplex Real Time quantitative polymerase chain reaction. HPyVs genome was detected in 33/204 samples (16.2%): the significant higher positivity was found in tumor tissues (26/110, 23.6%), followed by negative surgical margins (3/55, 5.5%, p < .05), and peripheral blood mononuclear cells (PBMCs) (4/39; 10.3%). HPyVs load was statistically higher only in the tumor tissues compared to negative surgical margins (p < .05). Specifically, MCPyV was detected in 19.1% (21/110) of tumor tissues, 3.6% (2/55) of negative surgical margins (p < .05), and 7.7% (3/39) of PBMCs; HPyV-6 in 2.7% (3/110) of tumor tissues, and 1.8% (1/55) of negative surgical margins; one tumor tissue (1/110, 0.9%) and one PBMCs sample (1/39, 2.6%) were positive for BKPyV; JCPyV was present in 0.9% (1/110) of tumor tissues. HPyV-7 and 9 were not detected in any sample. High prevalence and load of MCPyV genome in the tumor tissues might be indicative of a relevant rather than bystander role of the virus in the colon tumorigenesis.

Reproducibility of AJCC Criteria for Classifying Deeply Invasive Colon Cancers Is Suboptimal for Consistent Cancer Staging.

The eighth edition of the American Joint Committee on Cancer (AJCC) Staging Manual attempts to address ambiguity in the pT category assignment for colon cancer from prior editions. Despite modifications, the distinction between the pT3 and pT4a categories continues to be a source of diagnostic confusion. In this study, we assessed interobserver agreement among pathologists from different institutions in the application of AJCC eighth edition criteria for categorizing deeply invasive colonic adenocarcinomas. We identified morphologic patterns that produce diagnostic confusion. We assessed 47 colon cancers that closely approached the serosal surface. Six pathologists with interest in gastrointestinal pathology and 4 focused in other subspecialties classified each case as pT3 or pT4a, based on examination of low-magnification and high-magnification images of the most deeply invasive area. Interobserver agreement was assessed using Fleiss' κ. Cases displayed 3 morphologic patterns at the advancing tumor edge, namely, (1) continuous invasion through an inflammatory focus, (2) pushing border, and (3) infiltrative glands and cell clusters with serosal reaction. Gastrointestinal pathologists achieved slight (κ=0.21) or moderate (κ=0.46) and (κ=0.51) agreement in each category, whereas agreement among nongastrointestinal pathologist was fair (0.31) and (0.39), or moderate (0.57) for each category, respectively. In 10 (21%) cases, the distinction between pT3 and pT4a would have changed the overall clinical stage. We conclude that histologic criteria for serosal penetration is a persistent source of diagnostic ambiguity for gastrointestinal and general pathologists in the pT categorization of colon cancers. Clarification of these criteria will help ensure uniform reporting of pathologic and clinical stage.

Proposal of a Classification System for Adenocarcinoma Involving the Ileocecal Valve (Bauhin's Valve): A Retrospective Study of 689 Right Hemicolectomized Patients.

BACKGROUND/AIM: As of 2020, adenocarcinoma arising in the ileocecal valve (ICV-A) has been examined along with cecal and right colon cancer (RCC) under the collective heading "ileocecal" tumor. We propose a new classification system for this cancer. PATIENTS AND METHODS: We retrospectively analyzed RCC patients from 2003 to 2019. The scheme was: i) Type I cancer for adenocarcinomas residing in ICV; ii) Type II, if they reside 1 to 5 mm from ICV; iii) Type III, 6 mm to 10 mm from ICV; iv) Type IV, at 1,1 to 5 cm; v) Type V, at more than 5 cm (ascending colon cancer). RESULTS: Of 689 hemicolectomized patients, there were 91 (13.2%) Type I, 87 Type II (12.6%), 38 (5.5%) Type III, 157 (22.8%) Type IV and 314 (45.6%) Type V. Each type was associated with at least one clinicopathologic feature. CONCLUSION: ICV-A was classified into five types (I-V) according to the distance from ICV. Further studies are needed in order to corroborate our findings.

Deep Learning Models for Histopathological Classification of Gastric and Colonic Epithelial Tumours.

Histopathological classification of gastric and colonic epithelial tumours is one of the routine pathological diagnosis tasks for pathologists. Computational pathology techniques based on Artificial intelligence (AI) would be of high benefit in easing the ever increasing workloads on pathologists, especially in regions that have shortages in access to pathological diagnosis services. In this study, we trained convolutional neural networks (CNNs) and recurrent neural networks (RNNs) on biopsy histopathology whole-slide images (WSIs) of stomach and colon. The models were trained to classify WSI into adenocarcinoma, adenoma, and non-neoplastic. We evaluated our models on three independent test sets each, achieving area under the curves (AUCs) up to 0.97 and 0.99 for gastric adenocarcinoma and adenoma, respectively, and 0.96 and 0.99 for colonic adenocarcinoma and adenoma respectively. The results demonstrate the generalisation ability of our models and the high promising potential of deployment in a practical histopathological diagnostic workflow system.

Cosecha ganglionar en cáncer de colon. Mitos y magia / Lymph node harvest in colon cancer. Myths and magic / Contagem de linfonodos no câncer de cólon. Mitos e magia

Resumen: El compromiso ganglionar es crítico en la estadificación del cáncer de colon como factor pronóstico y como determinante de tratamiento adyuvante. Se sigue discutiendo el número de ganglios adecuados a resecar, cuáles son los factores que inciden en la cosecha ganglionar y el significado biológico de ésta. Se revisan las variables clínicas y de la propia biología tumoral que hacen que la definición de un número determinado de ganglios, como gold standard de cosecha ganglionar adecuada, sea controversial. El número 12 no necesariamente es un número "mágico" marcador de calidad. Extender la resección para aumentar la cosecha ganglionar no mejora la estadificación, expone al paciente a riesgos innecesarios, sin efecto terapéutico comprobado. La "magia" sigue siendo realizar resecciones regladas, que incluyan el pedículo vascular y el meso satélite al tumor, ajustando la resección a las características del paciente. Menos no es más, pero más no es necesariamente mejor.

Summary: Lymph node compromise is critical in colon cancer staging, as a prognostic factor and to determine adjuvant therapy. The number of lymph nodes to be resected is still under discussion, as well as the factor that have an impact on lymph node harvest and its biological significance. We reviewed clinical variables and variables that are specific to the tumor, what results in the definition of a certain number of lymph nodes, as the adequate Gold Standard for lymph node harvest being controversial. 12 is not necessarily a "magic" number that marks quality. Extending resection to increase lymph node harvest does not improve staging, it exposes patients to unnecessary risks, there being no therapeutic effect guaranteed. The "Magic" continues to be routine resection that includes the cystic pedicle and the area around the tumour, adjusting resection to the patient's characteristics. Less is not best, but more is not necessarily better.

Resumo: O compromisso ganglionar é crítico no estadiamento do câncer de cólon, como fator prognóstico e como determinante do tratamento adjuvante. A discussão sobre o número de gânglios adequados a ressecar, quais são os fatores que incidem sobre a definição do número de linfonodos a ser retirados e seu significado biológico. Faz-se uma revisão das variáveis clínicas e da própria biologia tumoral, que fazem com que a definição de um número determinado de gânglios como Gold Standard do número adequado de linfonodos a remover seja controversa. O número 12 não é necessariamente um número "mágico", um marcador de qualidade. Ampliar a ressecção para aumentar o número de linfonodos que serão retirados não melhora o estadiamento, expõe o paciente a riscos desnecessários, sem um efeito terapêutico comprovado. A "Magia" continua sendo realizar ressecções de acordo com parâmetros definidos, que incluam o pedículo vascular e o mesocólon satélite ao tumor, ajustando a ressecção às características do paciente. Menos não é mais, porém mais não é necessariamente melhor.

Histotype influences emergency presentation and prognosis in colon cancer surgery.

AIM: To investigate whether differences in histotype in colon cancer correlate with clinical presentation and if they might influence oncological outcomes and survival. METHODS: Data regarding colon cancer patients operated both electively or in emergency between 2009 and 2014 were retrospectively collected from a prospectively maintained database and analyzed for the purpose of this study. Rectal cancer was excluded from this analysis. Statistical univariate and multivariate analyses were performed to investigate possible significant variables influencing clinical presentation, as well as oncological outcomes and survival. RESULTS: Data from 219 patients undergoing colorectal resection for cancer of the colon only were retrieved. One hundred seventy-four patients had an elective procedure and forty-five had an emergency colectomy. Emergency presentation was more likely to occur in mucinous (p < 0.05) and signet ring cell (p < 0.01) tumors. No definitive differences in 5-year overall (44.7% vs. 60.6%, p = 0.078) and disease-free (51.2% vs. 64.4%, p = 0.09) survival were found between the two groups as a whole, but the T3 emergency patients showed worse prognosis than the elective (p < 0.03). Lymph node invasion, laparoscopy, histology, and blood transfusions were independent variables found to influence survival. Distribution assessed for pTNM stage showed T3 cancers were more common in emergency (p < 0.01). CONCLUSIONS AND DISCUSSION: Mucinous and signet ring cell tumors are related to emergency presentation, pT3 stage, poorest outcomes, and survival. Disease-free survival in patients who had emergency surgery for T3 colon cancer seems related to the histotype.

Computer-assisted assessment of colonic polyp histopathology using probe-based confocal laser endomicroscopy.

INTRODUCTION: Probe-based confocal laser endomicroscopy (pCLE) is a promising modality for classifying polyp histology in vivo, but decision making in real-time is hampered by high-magnification targeting and by the learning curve for image interpretation. The aim of this study is to test the feasibility of a system combining the use of a low-magnification, wider field-of-view pCLE probe and a computer-assisted diagnosis (CAD) algorithm that automatically classifies colonic polyps. METHODS: This feasibility study utilized images of polyps from 26 patients who underwent colonoscopy with pCLE. The pCLE images were reviewed offline by two expert and five junior endoscopists blinded to index histopathology. A subset of images was used to train classification software based on the consensus of two GI histopathologists. Images were processed to extract image features as inputs to a linear support vector machine classifier. We compared the CAD algorithm's prediction accuracy against the classification accuracy of the endoscopists. RESULTS: We utilized 96 neoplastic and 93 non-neoplastic confocal images from 27 neoplastic and 20 non-neoplastic polyps. The CAD algorithm had sensitivity of 95%, specificity of 94%, and accuracy of 94%. The expert endoscopists had sensitivities of 98% and 95%, specificities of 98% and 96%, and accuracies of 98% and 96%, while the junior endoscopists had, on average, a sensitivity of 60%, specificity of 85%, and accuracy of 73%. CONCLUSION: The CAD algorithm showed comparable performance to offline review by expert endoscopists and improved performance when compared to junior endoscopists and may be useful for assisting clinical decision making in real time.

Deep transfer learning methods for colon cancer classification in confocal laser microscopy images.

PURPOSE: The gold standard for colorectal cancer metastases detection in the peritoneum is histological evaluation of a removed tissue sample. For feedback during interventions, real-time in vivo imaging with confocal laser microscopy has been proposed for differentiation of benign and malignant tissue by manual expert evaluation. Automatic image classification could improve the surgical workflow further by providing immediate feedback. METHODS: We analyze the feasibility of classifying tissue from confocal laser microscopy in the colon and peritoneum. For this purpose, we adopt both classical and state-of-the-art convolutional neural networks to directly learn from the images. As the available dataset is small, we investigate several transfer learning strategies including partial freezing variants and full fine-tuning. We address the distinction of different tissue types, as well as benign and malignant tissue. RESULTS: We present a thorough analysis of transfer learning strategies for colorectal cancer with confocal laser microscopy. In the peritoneum, metastases are classified with an AUC of 97.1, and in the colon the primarius is classified with an AUC of 73.1. In general, transfer learning substantially improves performance over training from scratch. We find that the optimal transfer learning strategy differs for models and classification tasks. CONCLUSIONS: We demonstrate that convolutional neural networks and transfer learning can be used to identify cancer tissue with confocal laser microscopy. We show that there is no generally optimal transfer learning strategy and model as well as task-specific engineering is required. Given the high performance for the peritoneum, even with a small dataset, application for intraoperative decision support could be feasible.

Colon Neuroendocrine Tumors: A New Lymph Node Staging Classification.

BACKGROUND: The American Joint Commission on Cancer, the European Neuroendocrine Tumor Society, and the North American Neuroendocrine Tumor Society all classify colon neuroendocrine tumor (NET) nodal metastasis as N0 or N1. This binary classification does not allow for further prognostication by the total number of positive lymph nodes. This study aimed to evaluate whether the total number of positive lymph nodes affects the overall survival for patients with colon NET. METHODS: The National Cancer Database was used to identify patients with colon NET. Nearest-neighborhood grouping was performed to classify patients by survival to create a new nodal staging system. The Surveillance, Epidemiology, and End Results database was used to validate the new nodal staging classification. RESULTS: Colon NETs were identified in 2472 patients. Distinct 5-year survival rates were estimated for the patients with N0 (no positive lymph nodes; 69.8%; 95% confidence interval [CI], 66.7-72.7%), N1a (1 positive lymph node; 63.9%; 95% CI, 59.6-68.0%), N1b (2-9 positive lymph nodes; 38.9%; 95% CI, 35.4-42.3%), and N2 (≥ 10 positive lymph nodes; 15.7%; 95% CI, 11.9-20.0%; p < 0.001) nodal classifications. The validation population showed distinct 5-year survival rates with the new nodal staging. In multivariable Cox regression, the new nodal stage was a significant independent predictor of overall survival. CONCLUSIONS: The number of positive locoregional lymph nodes in colon NETs is an independent prognostic factor. For patients with colon NETs, N0, N1a, N1b, and N2 classifications for nodal metastasis more accurately predict survival than current staging systems.

Cancer diagnosis through a tandem of classifiers for digitized histopathological slides.

The current research study is concerned with the automated differentiation between histopathological slides from colon tissues with respect to four classes (healthy tissue and cancerous of grades 1, 2 or 3) through an optimized ensemble of predictors. Six distinct classifiers with prediction accuracies ranging from 87% to 95% are considered for the task. The proposed method of combining them takes into account the probabilities of the individual classifiers for each sample to be assigned to any of the four classes, optimizes weights for each technique by differential evolution and attains an accuracy that is significantly better than the individual results. Moreover, a degree of confidence is defined that would allow the pathologists to separate the data into two distinct sets, one that is correctly classified with a high level of confidence and the rest that would need their further attention. The tandem is also validated on other benchmark data sets. The proposed methodology proves to be efficient in improving the classification accuracy of each algorithm taken separately and performs reasonably well on other data sets, even with default weights. In addition, by establishing a degree of confidence the method becomes more viable for use by actual practitioners.

Survivability prediction of colon cancer patients using neural networks.

We utilize deep neural networks to develop prediction models for patient survival and conditional survival of colon cancer. Our models are trained and validated on data obtained from the Surveillance, Epidemiology, and End Results Program. We provide an online outcome calculator for 1, 2, and 5 years survival periods. We experimented with multiple neural network structures and found that a network with five hidden layers produces the best results for these data. Moreover, the online outcome calculator provides conditional survival of 1, 2, and 5 years after surviving the mentioned survival periods. In this article, we report an approximate 0.87 area under the receiver operating characteristic curve measurements, higher than the 0.85 reported by Stojadinovic et al.

Risks of Colorectal Cancer and Cancer-Related Mortality in Familial Colorectal Cancer Type X and Lynch Syndrome Families.

BACKGROUND: The risk of cancers is well characterized in Lynch syndrome (LS) families but has been less studied in familial colorectal cancer type X (FCCTX) families. METHODS: In this article, we compare the risk estimates of first and second colorectal cancers (CRCs) in 168 FCTTX and 780 LS families recruited through the Colon Cancer Family Registry as well as the risk of cancer-related deaths and disease-free survival (DFS) after a first CRC. Our methodology is based on a survival analysis approach, developed specifically to model the occurrence of successive cancers (ie, first and second CRCs) in the presence of competing risk events (ie, death from any causes). RESULTS: We found an excess risk of first and second CRC in individuals with LS compared to FCCTX family members. However, for an average age at first CRC of 60 years in FCCTX families and 50 years in LS families, the DFS rates were comparable in men but lower in women from FCCTX vs LS families, eg , 75.1% (95% confidence interval [CI] = 69.0% to 80.9%) vs 78.9% (95% CI = 76.3% to 81.3%) for the 10-year DFS. The 10-year risk of cancer-related death was higher in FCCTX families vs LS families, eg, 15.4% in men (95% CI = 10.9% to 19.8%) and 19.3% in women (95% CI = 13.6% to 24.7%) vs 8.9% (95% CI = 7.5% to 11.4%) and 8.7% (95% CI = 7.1% to 10.8%), respectively. CONCLUSIONS: Individuals with CRCs arising in the context of FCCTX do not experience the same improved DFS and overall survival of those with LS, and that difference may be relevant in management decisions.

Clinicopathologic analysis and subclassification of benign lipomatous lesions of the colon.

Benign lipomatous lesions of the colon are generally asymptomatic. A few histologic subtypes are appreciable, but this is poorly studied. We categorized 404 benign colonic lipomatous lesions as vascular lipoma, fibrolipoma, mucosal lipoma, or lipoma not otherwise specified (NOS). We compared patient age and sex, tumor site and size, symptoms, whether the lesion was flat or pedunculated, and whether an overlying epithelial lesion was present. Symptomatic cases (4%) were larger on average than non-symptomatic ones (mean 3.70 cm vs. 1.30 cm, P < 0.0001). Lipoma NOS was commonly right-sided (P < 0.001) and commonly had an associated epithelial proliferation (P = 0.0004). Vascular lipomas were larger (mean 1.93 cm, P < 0.0001) than other types; they were the most commonly symptomatic, though this was not statistically significant. Mucosal lipomas were smallest on average (mean 0.48 cm) and were not associated with any clinical syndromes. Some colonic lipomas are non-incidental. Vascular lipomas are more often large, while lipomas NOS more often have an associated epithelial proliferation. Colonic lipomas are generally sporadic.

A multimodal (FACILE) classification for optical diagnosis of inflammatory bowel disease associated neoplasia.

BACKGROUND: Characterization of colonic lesions in inflammatory bowel disease (IBD) remains challenging. We developed an endoscopic classification of visual characteristics to identify colitis-associated neoplasia using multimodal advanced endoscopic imaging (Frankfurt Advanced Chromoendoscopic IBD LEsions [FACILE] classification). METHODS: The study was conducted in three phases: 1) development - an expert panel defined endoscopic signs and predictors of dysplasia in IBD and, using multivariable logistic regression created the FACILE classification; 2) validation - using 60 IBD lesions from an image library, two assessments of diagnostic accuracy for neoplasia were performed and interobserver agreement between experts using FACILE was determined; 3) reproducibility - the reproducibility of the FACILE classification was tested in gastroenterologists, trainees, and junior doctors after completion of a training module. RESULTS: The experts initially selected criteria such as morphology, color, surface, vessel architecture, signs of inflammation, and lesion border. Multivariable logistic regression confirmed that nonpolypoid lesion, irregular vessel architecture, irregular surface pattern, and signs of inflammation within the lesion were predictors of dysplasia. Area under the curve of this logistic model using a bootstrapped estimate was 0.76 (0.73 - 0.78). The training module resulted in improved accuracy and kappa agreement in all nonexperts, though in trainees and junior doctors the kappa agreement was still moderate and poor, respectively. CONCLUSION: We developed, validated, and demonstrated reproducibility of a new endoscopic classification (FACILE) for the diagnosis of dysplasia in IBD using all imaging modalities. Flat shape, irregular surface and vascular patterns, and signs of inflammation predicted dysplasia. The diagnostic performance of all nonexpert participants improved after a training module.

The lymph node status as a prognostic factor in colon cancer: comparative population study of classifications using the logarithm of the ratio between metastatic and nonmetastatic nodes (LODDS) versus the pN-TNM classification and ganglion ratio systems.

BACKGROUND: pN stage in the TNM classification has been the "gold standard" for lymph node staging of colorectal carcinomas, but this system recommends collecting at least 12 lymph nodes for the staging to be reliable. However, new prognostic staging systems have been devised, such as the ganglion quotients or lymph node ratios and natural logarithms of the lymph node odds methods. The aim of this study was to establish and validate the predictive and prognostic ability of the lymph node ratios and natural logarithms of the lymph node odds staging systems and to compare them to the pN nodal classification of the TNM system in a population sample of patients with colon cancer. METHODS: A multicentric population study between January 2004 and December 2007. The inclusion criteria were that the patients were: diagnosed with colon cancer, undergoing surgery with curative intent, and had a complete anatomopathological report. We excluded patients with cancer of the rectum or caecal appendix with metastases at diagnosis. Survival analysis was performed using the Kaplan-Meier actuarial method and the Log-Rank test was implemented to estimate the differences between groups in terms of overall survival and disease-free survival. Multivariate survival analysis was performed using Cox regression. RESULTS: We analysed 548 patients. For the overall survival, the lymph node ratios and natural logarithms of the lymph node odds curves were easier to discriminate because their separation was clearer and more balanced. For disease-free survival, the discrimination between the pN0 and pN1 groups was poor, but this phenomenon was adequately corrected for the lymph node ratios and natural logarithms of the lymph node odds curves which could be sufficiently discriminated to be able to estimate the survival prognosis. CONCLUSIONS: Lymph node ratios and natural logarithms of the lymph node odds techniques can more precisely differentiate risk subgroups from within the pN groups. Of the three methods tested in this study, the natural logarithms of the lymph node odds was the most accurate for staging non-metastatic colon cancer. Thus helping to more precisely adjust and individualise the indication for adjuvant treatments in these patients.