Colorectal Cancer Histology Research Articles

Abstract 2631: Deep learning-based analysis of tissue segmentation in histopathology images of colorectal cancer

Abstract Objective: Stromal elements in the tumor microenvironment (TME) impact prognosis and response to therapy, creating a barrier to absorption and penetration of therapeutic drugs and modulating immune cell infiltration. Few models exist to analyze the spatial distribution and mapping of stroma and cancer lesions composing the complex TME in association with mRNA levels via high throughput deep learning algorithm. Methods: The histopathology images (H&E stain) and mRNA-seq of 625 CRC patients (pts) were obtained from the Cancer Genome Atlas (TCGA). A deep learning-based model of tissue classification enabling segmentation of tumor (malignant glands composed of adenocarcinoma cells) and stroma in histopathology images was established using published data sets [1-2]. We defined the following parameters based on the pixel count of stroma and tumor: the stroma-to-tumor ratio (STR) defined as the pixel count ratio of stroma to stroma plus tumor; STRx, STR within the pixel distance x from the tumor; tumor-tissue-ratio (TTR), a pixel count ratio of tumor to stroma plus tumor. The expressivity of genes enriched in cellular and structural components of TME and its correlation with STR and TTR were analyzed. Results: The profiling results demonstrate that histopathology images of consensus molecular subtype (CMS) 4 of colorectal cancer have statistically significantly larger STR, STR10, STR20, STR30, STR40, and STR50 than those in other CMS groups (p<0.0001); TTR, smaller in CMS 4 than in other CMS groups (p<0.0001). Out of two patient groups clustered via k-means clustering (high and low STR), the group having higher STR has significantly decreased expression of genes related to antigen presentation, natural killer cells, and plasmacytoid dendritic cells, as well as significantly elevated expression of genes associated with epithelial-mesenchymal transition, desmoplastic reaction, fibroblastic cytokines, and angiogenesis (p<0.05) (refer to presentation, regarding the list of genes). Conclusion: Our analysis of histopathology images via the deep learning-based algorithm enables automated segmentation of stroma and malignant lesions. Parameters representing spatial and architectural distribution of stroma and cancer lesions are shown to have close correlation with varying expression of genes associated with immune system activation and suppression in the tumor microenvironment. [1] Kather et al. Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLoS Medicine. 2019 [2] Kumar et al. A Multi-organ nucleus segmentation challenge. IEEE Trans Med Imaging. 2019 Citation Format: Yeongwon Kim, Kyungdoc Kim, Jeonghyuk Park, Hyunho Park, Kyu-Hwan Jung, Sunyoung S. Lee. Deep learning-based analysis of tissue segmentation in histopathology images of colorectal cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2631.

Cellular community detection for tissue phenotyping in colorectal cancer histology images.

Classification of various types of tissue in cancer histology images based on the cellular compositions is an important step towards the development of computational pathology tools for systematic digital profiling of the spatial tumor microenvironment. Most existing methods for tissue phenotyping are limited to the classification of tumor and stroma and require large amount of annotated histology images which are often not available. In the current work, we pose the problem of identifying distinct tissue phenotypes as finding communities in cellular graphs or networks. First, we train a deep neural network for cell detection and classification into five distinct cellular components. Considering the detected nuclei as nodes, potential cell-cell connections are assigned using Delaunay triangulation resulting in a cell-level graph. Based on this cell graph, a feature vector capturing potential cell-cell connection of different types of cells is computed. These feature vectors are used to construct a patch-level graph based on chi-square distance. We map patch-level nodes to the geometric space by representing each node as a vector of geodesic distances from other nodes in the network and iteratively drifting the patch nodes in the direction of positive density gradients towards maximum density regions. The proposed algorithm is evaluated on a publicly available dataset and another new large-scale dataset consisting of 280K patches of seven tissue phenotypes. The estimated communities have significant biological meanings as verified by the expert pathologists. A comparison with current state-of-the-art methods reveals significant performance improvement in tissue phenotyping.

Improved convolutional neural network based histopathological image classification

Histopathological image classification is one of the important application areas of medical imaging. However, an accurate and efficient classification is still an open-ended research due to the complexity in histopathological images. For the same, this paper presents an efficient architecture of convolutional neural network for the classification of histopathological images. The proposed method consists of five subsequent blocks of layers, each having convolutional, drop-out, and max-pooling layers. The performance of the introduced classification system is validated on colorectal cancer histology image dataset which consists of RGB-colored images belonging to eight different classes. The experimental results confirm the higher performance of the proposed convolutional neural network against existing different machine learning models with the lowest error rate of 22.7%.

Multiplex Cellular Communities in Multi-Gigapixel Colorectal Cancer Histology Images for Tissue Phenotyping.

In computational pathology, automated tissue phenotyping in cancer histology images is a fundamental tool for profiling tumor microenvironments. Current tissue phenotyping methods use features derived from image patches which may not carry biological significance. In this work, we propose a novel multiplex cellular community-based algorithm for tissue phenotyping integrating cell-level features within a graph-based hierarchical framework. We demonstrate that such integration offers better performance compared to prior deep learning and texture-based methods as well as to cellular community based methods using uniplex networks. To this end, we construct celllevel graphs using texture, alpha diversity and multi-resolution deep features. Using these graphs, we compute cellular connectivity features which are then employed for the construction of a patch-level multiplex network. Over this network, we compute multiplex cellular communities using a novel objective function. The proposed objective function computes a low-dimensional subspace from each cellular network and subsequently seeks a common low-dimensional subspace using the Grassmann manifold. We evaluate our proposed algorithm on three publicly available datasets for tissue phenotyping, demonstrating a significant improvement over existing state-of-the-art methods.

Colorectal Cancer Histology Image Classification Using Stacked Ensembles

Colorectal Cancer Histology Image Classification Using Stacked Ensembles

Integrated Analysis of Oncogenic Networks in Colorectal Cancer Identifies GUCA2A as a Molecular Marker.

Colorectal cancer (CRC) is one of the most common and deadly malignancies in the world. In China, the morbidity rate of CRC has increased during the period 2000 to 2011. Biomarker detection for early CRC diagnosis can effectively reduce the mortality of patients with CRC. To explore the underlying mechanisms of effective biomarkers and identify more of them, we performed weighted correlation network analysis (WGCNA) on a GSE68468 dataset generated from 378 CRC tissue samples. We screened the gene set (module), which was significantly associated with CRC histology, and analyzed the hub genes. The key genes were identified by obtaining six colorectal raw data (i.e., GSE25070, GSE44076, GSE44861, GSE21510, GSE9348, and GSE21815) from the GEO database (https://www.ncbi.nlm.nih.gov/geo). The robust differentially expressed genes (DEGs) in all six datasets were calculated and obtained using the library “RobustRankAggreg” package in R 3.5.1. An integrated analysis of CRC based on the top 50 downregulated DEGs and hub genes in the red module from WGCNA was conducted, and the intersecting genes were screened. The Kaplan–Meier plot was further analyzed, and the genes associated with CRC prognosis based on patients from the TCGA database were determined. Finally, we validated the candidate gene in our clinical CRC specimens. We postulated that the candidate genes screened from the database and verified by our clinical pathological data may contribute to understanding the molecular mechanisms of tumorigenesis and may serve as potential biomarkers for CRC diagnosis and treatment.

Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study.

BackgroundFor virtually every patient with colorectal cancer (CRC), hematoxylin–eosin (HE)–stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images.Methods and findingsWe hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I–IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a “deep stroma score,” which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio [HR] with 95% confidence interval [CI]: 1.99 [1.27–3.12], p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I–IV CRC patients from the “Darmkrebs: Chancen der Verhütung durch Screening” (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 [1.14–2.33], p = 0.008), CRC-specific OS (HR 2.29 [1.5–3.48], p = 0.0004), and relapse-free survival (RFS; HR 1.92 [1.34–2.76], p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows.ConclusionsIn our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images.

Clinical Influence of Ploidy and Cancer Stem Cells and Other Parameters in Stage IV Colorectal Cancer.

The aim of the present study was the evaluation of the influence of cancer stem cells and other parameters in stage IV colorectal cancer patients. One hundred patients were retrospectively included in the study and 24 variables were examined for their relation with response to treatment and survival. A low ploidy score in the histology of colorectal cancer was associated with improvement of performance status and response to therapy. No significant correlations between the percentage of cancer stem cells from the same tissue and the remaining clinical parameters was revealed. In the multivariate analysis of all the examined parameters in Cox models, independent unfavorable prognostic factors were increased ploidy score, existence of bone metastases, use of epoetin, and existence of side-effects such as anorexia, mucositis, and weight loss. Our findings emphasize on the prognostic role of ploidy in advanced colorectal cancer, but further analysis is required to evaluate the role of cancer stem cells.

An Analysis in Tissue Classification for Colorectal Cancer Histology Using Convolution Neural Network and Colour Models

Computer vision-based identification of different tissue categories in histological images is a critical application of the computer-assisted diagnosis (CAD). Computer-assisted diagnosis systems support to reduce the cost and increase the efficiency of this process. Traditional image classification approaches depend on feature extraction methods designed for a specific problem based on domain information. Deep learning approaches are becoming important alternatives with advance of machine learning technologies to overcome the numerous difficulties of the feature-based approaches. A method for the classification of histological images of human colorectal cancer containing seven different types of tissue using convolutional neural network (CNN) is proposed in this article. The method is evaluated using four different colour models in absence and presence of Gaussian noise. The highest classification accuracies are achieved with HVI colour model, which is 95.8% in nonexistence and 78.5% in existence of noise respectively.

Abstract A17: Novel patient-derived xenograft (PDX) models from peritoneal metastasis of colorectal carcinoma for drug testing and biomarker analysis

Abstract Peritoneal metastasis (PM) represents a terminal tumor stage with limited therapy options for patients with colorectal cancer (CRC). To improve therapeutic efficacy and overall survival, availability of appropriate in vivo models of PM could improve the evaluation of chemosensitivity as well as identification of novel biomarkers and therapeutic targets to improve the therapeutic outcome of PM patients. In this context, we generated the first patient-derived xenograft (PDX) models of PM from CRC as platform to test chemotherapy response and to potentially analyze biomarkers. For the PDX establishment colorectal surgical specimens were subcutaneously (s.c.) transplanted onto immunocompromised NOG mice. The engrafted tumors were transferred to NMRI nu/nu mice for further passaging. They were characterized by histopathology, immunohistochemistry, and gene expression analyses using real-time RT-PCR. Chemosensitivity of PDX models was evaluated in vivo by application of a panel of conventional chemotherapeutic and of targeted drugs. For PDX establishment 68 CRC surgical specimens (taken from peritoneum and omentum) were transplanted, from which 22 PDX have engrafted. From those, 13 models of 10 patients have been analyzed. Their tumor doubling times ranged between 4 to 28 days. The histopathologic evaluation revealed maintenance of the original CRC histology in the PDX. The chemosensitivity testing of conventional and of targeted drugs in the 13 PM PDX models revealed the individual, diverging response of the PDX, such as for 5-FU, irinotecan, oxaliplatin, cetuximab, and erlotinib. More interestingly, different responses were observed in PDX of the omentum vs. peritoneum, which were derived from the same patient. Our results demonstrate that this novel panel of PDX maintains the morphology of the patient tumor in early passages, reflect heterogeneous response rates, and can be used as a preclinical in vivo platform for translational studies of potential clinical use. In conclusion, this PDX panel of PM of the CRC can be used for further studies to evaluate new biomarkers and to test novel therapies or combinations for PM of CRC. Citation Format: Wolfgang Walther, Eva Pachmayr, Bernadette Brzezicha, Britta Büttner, Beate Rau, Ulrike Stein. Novel patient-derived xenograft (PDX) models from peritoneal metastasis of colorectal carcinoma for drug testing and biomarker analysis [abstract]. In: Proceedings of the AACR Special Conference: Advances in Modeling Cancer in Mice: Technology, Biology, and Beyond; 2017 Sep 24-27; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(10 Suppl):Abstract nr A17.

Dosimetric Parameters Predicting Local Failure after Stereotactic Body Radiation Therapy Using the Robotic Radiosurgery System for Oligometastatic Lesions in the Lung and Liver

Stereotactic body radiation therapy (SBRT) is an option for local therapy for lung or liver oligometastases. The purpose of this study was to investigate safety and efficacy of SBRT using the robotic radiosurgery system for lung and liver oligometastases and evaluate which dosimetric parameters predict local failure (LF). In total, 76 patients with 114 lesions (lung: 70, liver: 44) were treated with SBRT using the robotic radiosurgery system between May 2013 and July 2016 at our institute. The study cohort included patients with 1–3 synchronous lung/liver metastases and a controlled primary lesion. Patients with less than 6 months of follow-up or Eastern Cooperative Oncology Group performance status (PS) >2 were excluded. Total radiation dose range was 48–64 Gy in 3, 4, or 8 equal fractions. The radiation dose delivered to the planning target volume (PTV) was prescribed to the 75–85% isodose line, covering ≥95% of the PTV. Median follow-up and age were 19 months (range, 7–43) and 69 years (range, 37–91), respectively. Forty-four patients (57.9%) were male and 32 patients (42.1%) were female. Four patients (5.3%) had a PS of 2. Primary tumors were observed in colorectum (n = 73 lesions; 64.0%), gastric region (n = 6 lesions; 5.3%), ovary (n = 6 lesions; 5.3%), esophagus (n = 5 lesions; 4.4%), and others (n = 24 lesions; 21.0%). Eighty-three lesions (72.8%) were treated with chemotherapy before SBRT. At SBRT consultation, 46 patients (60.5%) had one, 22 (29.0%) patients had two, and 8 (10.5%) patients had three metastatic lesions. The median maximum tumor diameter (MTD) was 19 mm (range, 5–57) and median gross tumor volume (GTV) was 3.2 mL (range, 0.2–106.8). The median PTV biological effective dose (BED10), mean GTV BED10, and minimum GTV BED10 were 122.0 Gy (range, 43.3–181.0), 178.0 Gy (range, 92.6–239.0), and 144.5 Gy (range, 36.6–208.5), respectively. Nine lesions (7.9%) were treated with 3 fractions, 80 lesions (70.2%) with 4 fractions, and 25 lesions (21.9%) with 8 fractions. Thirty lesions (26.3%) were treated with chemotherapy after SBRT. The 1- and 2-year LC rates were 88.8% and 78.0%, respectively. Absence of post SBRT chemotherapy (p=0.006), MTD >20 mm (p=0.016) and minimum GTV BED10 <112.5 Gy (p=0.038) were significant predictors of LF. Histology of colorectal cancer (p=0.542) was not a significant predictive factor of LF. The 2- and 3-year overall survival rates were 76.0% and 51.2%, respectively. Grade 3, grade 2, and grade 1 adverse events were observed in 1, 2 and 53 patients, respectively. SBRT with the robotic radiosurgery system is a safe and effective treatment for lung or liver oligometastases. Among dosimetric parameters, minimum GTV BED10 <112.5 Gy was a significant predictive factor for LF. A larger patient cohort or a prospective study is needed to validate our results.

Abstract 4918: Is Livin a protagonist of mucinous adenocarcinoma histology in colorectal cancer

Abstract The mechanism of cancer resistance to chemotherapy regimen remains uncertain. Colorectal mucinous adenocarcinoma is one of the distinct histological subtypes of the disease implicated in chemotherapeutic resistance associated with nodal and peritoneal metastases and worse disease-free survival as an index of poor prognosis. One of the important acquired capabilities used by the cancer cells to resist anticancer therapies is evasion of apoptosis possibly via inhibitor of apoptosis proteins for which Livin is one. This protein contains baculoviral IAP repeat domains in addition to a RING finger, a protein-protein motif important for binding and inhibition of active caspases that interfere with intrinsic and/or extrinsic pathway and the ensuing blockade of apoptosis. Thus, understanding the molecular events on how cancer cells of mucinous histology evade apoptotic death may provide a novel paradigm for a molecular targeted therapy in the management of colorectal mucinous adenocarcinoma. In this study, the expression pattern and prognostic value of IAP family protein, Livin, in colorectal cancer patients with mucinous histology pre and post-chemotherapy regimen was investigated. Tissue sections from advanced stage colorectal cancer patients who were treated or untreated with neo-adjuvant FOLFOX chemotherapy before curative resection were included in this study. The tissue sections were grouped according to colorectal adenocarcinomas showing mucinous histology and non-mucinous component. Histological study including Haematoxylin and Eosin, and immunohistochemistry for Anti-Livin and DNA mismatched repair proteins were carried out. Immunofluorescence study was performed to clarify the expression pattern of the protein using Anti-Livin antibody. Protein expression quantification study was also used. Results show significant cytoplasmic localisation and expression of Livin protein in the colorectal cancer cells. The Livin protein expression was found to be increased by more than a one-fold post-chemotherapy treatment when compared with pre-chemotherapy treated patients with mucinous histology of colorectal cancer. Younger patients were found to have a greater probability of colorectal mucinous adenocarcinoma diagnosis and worse prognosis. Our findings show that Livin-induced inhibition of apoptosis activity can be a target for novel approaches to treatment and prevention of chemotherapy associated drug resistance in mucinous histology colorectal cancer since Livin overexpression has been associated with metastases and worse disease-free survival. More study is needed Citation Format: Mohammed Faruk, Abdulmumini Hassan Rafindadi, Sani Ibrahim, Surajo Mohammed Aminu, Surajo Mohammed Aminu, Ahmed Adamu, Ahmed Adamu, Yawale Iliyasu, Adamu Abdullahi, Mohammed Sani Shehu, Abdullahi Mohammed, John Idoko, Abdullahi Jibril Randawa, Abdullahi Jibril Randawa, Atara Ntekim, Saad Aliyu Ahmed, Aishatu Suleiman Maude, Almustapha Aliyu Liman, Abubakar Sani, Khalid Zahir Shah, Yahaya Ukwenya, Yahaya Ukwenya, Cheh Augustine Awasum, Kasimu Umar Adoke, James Olowu Enemari, James Olowu Enemari, James Olowu Enemari, Andrew Jonathan Nok. Is Livin a protagonist of mucinous adenocarcinoma histology in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4918. doi:10.1158/1538-7445.AM2017-4918

Radiobiological parameters of liver and lung metastases derived from tumor control data of 3719 metastases

Background and purposeThe radiobiological parameters for liver and lung metastases treated with stereotactic body radiation therapy (SBRT) are poorly defined. This project aimed at estimating these parameters from published tumor control probability (TCP) data, and separately for metastases with colorectal cancer (CRC) and non-CRC histology. Materials and methodsA total of 62 studies with 89 different treatment prescriptions for a total of 3719 metastases were analyzed in a Bayesian framework using four different radiobiological models: The LQ, mLQ, LQ-L and the regrowth model which accounts for tumor regrowth after SBRT. ResultsDepending on the particular model, α/β ratios in the range 13–23Gy for pulmonary metastases and 16–28Gy for hepatic metastases were estimated. For CRC metastases the estimated α/β ratio was 43.1±4.7Gy compared to 21.6±7.8Gy for non-CRC metastases. Typical isocenter dose prescriptions of 3×12Gy, 3×14.5Gy and 3×17Gy applied within 5days were predicted sufficient to control 90% of lung, liver and CRC metastases after 1yr, respectively. Conclusionsα/β ratios for liver and lung metastases are higher than the usually assumed 10Gy. Differences between CRC and non-CRC histology were found. Future studies confirming these findings in individual patient data are needed.

Stereotactic Ablative Radiation Therapy for Pulmonary Metastases: Histology, Dose, and Indication Matter

To assess the association between colorectal cancer (CRC) histology, dose, and local failure (LF) after stereotactic ablative radiation therapy (SABR) for pulmonary metastases, and to describe subsequent cancer progression, change of systemic therapy (CST), survival, and their association with treatment indications. From a prospective SABR cohort, 180 pulmonary metastases in 120 patients were identified. Treatment indications were single metastasis, oligometastases, oligoprogression, and dominant areas of progression. Doses of 48 to 52Gy/4 to 5 fractions were delivered. Since 2010 the dose for peripheral CRC metastases was increased to 60Gy/4 fractions. Cumulative incidence function (CIF) was used to report LF, progression probability, and CST. The Kaplan-Meier method estimated overall survival (OS). Univariate and multivariable analyses to assess variable associations were conducted. Median follow-up was 22months (interquartile range, 14-33months). At 24months, the CIF of LF was 23.6% (95% confidence interval [CI] 15.1%-33.3%) and 8.3% (95% CI 2.6%-18.6%), respectively, for CRC and non-CRC metastases (P<.001). This association remained significant after adjusting for confounders (subdistribution hazard ratio [SHR] 13.6, 95% CI 4.2-44.1, P<.001). Among CRC metastases, 56 and 45 received <60Gy and 60Gy, respectively. Delivering 60Gy was independently associated with a lower hazard of LF (SHR 0.271, 95% CI 0.078-0.940, P=.040). At 12months the CIF of progression was 41.67% (95% CI 21.69%-60.56%), 42.51% (95% CI 29.09%-55.29%), 62.96% (95% CI 41.25%-78.53%), and 78.57% (95% CI 42.20%-93.48%), respectively, for patients treated for single metastasis, oligometastases, oligoprogression, and dominant area of progression (P<.001). A CST was observed, respectively, in 4 (17%), 17 (31%), 12 (44%), and 10 (71%) patients with a median time of 13.1, 11.1, 8.4, and 8.4months. Colorectal cancer lung metastases are associated with a higher hazard of LF and require higher SABR doses. Outcomes for patients with oligometastases and oligoprogression treated with SABR seem favorable. Prospective clinical trials are needed to confirm these benefits.

Multi-class texture analysis in colorectal cancer histology.

Automatic recognition of different tissue types in histological images is an essential part in the digital pathology toolbox. Texture analysis is commonly used to address this problem; mainly in the context of estimating the tumour/stroma ratio on histological samples. However, although histological images typically contain more than two tissue types, only few studies have addressed the multi-class problem. For colorectal cancer, one of the most prevalent tumour types, there are in fact no published results on multiclass texture separation. In this paper we present a new dataset of 5,000 histological images of human colorectal cancer including eight different types of tissue. We used this set to assess the classification performance of a wide range of texture descriptors and classifiers. As a result, we found an optimal classification strategy that markedly outperformed traditional methods, improving the state of the art for tumour-stroma separation from 96.9% to 98.6% accuracy and setting a new standard for multiclass tissue separation (87.4% accuracy for eight classes). We make our dataset of histological images publicly available under a Creative Commons license and encourage other researchers to use it as a benchmark for their studies.

A Two-year review of Colorectal Cancer at Tikur Anbessa Specialized Hospital, Addis Ababa, Ethiopia

Background: Colorectal Cancer is the third most common cause of admission and death in the West; and till recently it was thought to be an uncommon disease in Africa and Asia. Although data from Ethiopia is unavailable, a recent report from Global cancer statistics center shows an increasing incidence in the sub-Saharan Africa which is also our observation. The objective of this study was to assess the hospital prevalence, common presenting symptoms, common presenting signs, site, stage and histology of colorectal cancer. Methods: A retrospective cross-sectional study design, by reviewing of patient records collected from 2010 to 2011was used. Patient card numbers with colorectal and anal cancer were retrieved from registry log book in the department of surgery. Individual patient card was retrieved from the hospital record office. A total of 120 out of 142 patient cards were included in the study. Data were entered using epi-data, version 3.0 and was analyzed using SPSS for windows version 21, and descriptive data analysis using frequency was made to answer the research question. Results: The mean age of the study subjects was 47 + 16 years, ranging between 18 and 83 years. A third was below the age of 40 years, 58% were men, nearly half came from Addis Ababa, and 20% presented as an emergency. Study subjects presented with rectal bleeding (63.0%), abdominal pain (54.3%), weight loss (44.9%), tenesmus (39.4%), change in bowel habit (48.0%) and symptoms of obstruction (17.3%). The study revealed mass on Digital Rectal Examination in 50%, anemia in 24.4%, abdominal mass in 22.8% and signs of obstruction among 11.8% of patients. The common sites of cancer were the rectum in 48.3% of cases followed by caecum (12.5%), sigmoid colon (11.5%) and recto-sigmoid junction (10.8%). More than half of the study subjects had stage III to IV disease. More than 94% of the patients had histologically proven adenocarcinoma. Thirty four percent of the cases had either inoperable or unresectable tumor indicative of delayed presentation. The hospital mortality was 8.0%. Recommendation: Colorectal Cancer is a problem of significant magnitude and as the outcome of treatment depends largely on the early detection of cases a detailed research should be done to see the factors which hamper early detection and referral of cases to the tertiary health facilities where multidisciplinary management is available. This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source (including a link to the formal publication), provide a link to the Creative Commons license, and indicate if changes were made.

Estrogen and colorectal cancer incidence and mortality.

The preponderance of observational studies describe an association between the use of estrogen alone and a lower incidence of colorectal cancer. In contrast, no difference in the incidence of colorectal cancer was seen in the Women's Health Initiative (WHI) randomized, placebo-controlled trial with estrogen alone after a mean intervention of 7.1 years and cumulative follow-up of 13.2 years. This study extends these findings by providing detailed analyses of the effects of estrogen alone on the histology, grade, and stage of colorectal cancer, relevant subgroups, and deaths from and after colorectal cancer. The WHI study was a randomized, double-blind, placebo-controlled trial involving 10,739 postmenopausal women with prior hysterectomy. Participants were assigned to conjugated equine estrogen at 0.625 mg/d (n = 5279) or a matching placebo (n = 5409). Rates of colorectal cancer diagnoses and deaths from and after colorectal cancer were assessed throughout the study. Colorectal cancer rates in the estrogen-alone and placebo groups were comparable: 0.14% and 0.12% per year, respectively (hazard ratio [HR], 1.13; 95% confidence interval [CI], 0.83-1.58; P = .43). Bowel screening examinations were comparable between the 2 groups throughout the study. The grade, stage, and location of colorectal cancer did not differ between the randomization groups. There were more colorectal cancer deaths in the estrogen-alone group (34 [0.05%] vs 24 [0.03%]; HR, 1.46, 95% CI, 0.86-2.46; P = .16), but the difference was not statistically significant. The colorectal cancer incidence was higher for participants with a history of colon polyp removal in the estrogen-alone group (0.23% vs 0.02%; HR, 13.47; nominal 95% CI, 1.76-103.0; P < .001). The use of estrogen alone in postmenopausal women with prior hysterectomy does not influence the incidence of colorectal cancer or deaths from or after colorectal cancer. A possibly higher risk of colorectal cancer in women with prior colon polyp removal who use estrogen alone requires confirmation.

Can a gastrointestinal pathologist identify microsatellite instability in colorectal cancer with reproducibility and a high degree of specificity?

Clinical features usually initiate evaluation for Lynch Syndrome (LS) but some colorectal cancer (CRC) histopathology findings are compatible with high microsatellite instability (MSI-H) that also occurs in LS. This led to the suggestion that pathologists request MSI analysis, which is an expensive addition to routine histology. We aimed to see if a Gastrointestinal Pathologist could identify MSI-H features with reproducibility and high (95%) specificity (MSI-H 95%). Histopathology of all CRCs received during 2005 and 4 MSI-H controls were scored using 2 published methods, "MsScore" and "PathScore". MSI analysis was performed on CRCs scored by either method as probable MSI-H 95% and results compared. To examine reproducibility of histopathology, 100 coded slides, including 25 scored MSI-H 95% and 75 scored low, were re-examined to now identify those needing MSI analysis. Costs were evaluated for identifying MSI-H with or without scoring. All 227 CRCs were scored for possible MSI-H 95%; 24 had high scores and MSI analysis. DNA analysis proved 14 MSI-H, PathScore identified 13 (95%), MsPath identified 9 (64%), histopathology alone identified 7 (50%). Reproducibility for identifying histopathology characteristics of MSI-H at re-examination, without scoring, was "moderate agreement" (Kappa statistic=0.4615). Costs for identifying MSI-H by PathScore were the lowest, $436/identification. Conclusions; PathScore identified the most proven MSI-H CRCs at lowest cost and even an experienced gastrointestinal pathologist has difficulties identify MSI-H without scoring. So, scoring can be facilitated by a computerized evaluation form for routine CRC histology, prompting score computation and recommendation for MSI analysis with high specificity.

Mucinous adenocarcinomas: Poor prognosis in metastatic colorectal cancer

PurposeMucinous histology of metastatic colorectal cancer (CRC) has been associated with poor prognosis, however this has never been assessed in large well-defined study populations treated with the current used systemic agents. We investigated the prognostic value of mucinous histology in two large phase III studies in metastatic CRC. Patients and methodsThe study population included 1010 metastatic CRC patients who were treated with chemotherapy and targeted therapies in two phase III studies. Patients were classified according to the histology of the primary tumour in mucinous adenocarcinomas (MC) and non-mucinous adenocarcinomas (AC). ResultsPatients with MC (n=99) were older, had more often a normal serum lactate dehydrogenase (LDH), extrahepatic localisation of metastases, larger primary tumour diameter and a higher T classification compared to patients with AC (n=911). A deficient mismatch repair system and BRAF mutations were observed in 17% and 22% of patients with MC, compared to 3% and 7% in patients with AC, respectively. Clinical outcome was investigated in both studies separately, showing a worse overall survival (OS), progression free survival and overall response rate in patients with MC compared to patients with AC. Patients with MC received less cycles of treatment compared to AC, but did not suffer from a higher incidence of grade 3/4 toxicity. In multivariate analysis, mucinous histology was as an independent negative prognostic factor for OS, resulting in a combined hazard ratio of 1.78 (95%confidence interval (CI) 1.35–2.35). ConclusionsPatients with metastatic mucinous CRC have distinct clinicopathological features and poor response to chemotherapy and targeted agents. The strong negative prognostic value of MC warrants the use of this pathological feature as a stratification factor for clinical trials in metastatic CRC.

Prognosis of mucinous histology for patients with radically resected stage II and III colon cancer

Previous studies investigating the prognostic role of mucinous histology of colorectal cancer produced conflicting results. This retrospective analysis was carried out in order to explore whether mucinous adenocarcinoma (MC) is associated with a comparatively worse prognosis than that of nonmucinous adenocarcinoma (NMC) for patients undergoing curative resection for stage II and III colon cancer. This study involved 1025 unselected patients who underwent curative surgery for sporadic colon cancer and follow-up procedures at six different oncology departments. MCs accounted for 17.4% (n=178) of tumours. Patients with MC had 5- and 8-year overall survival rates of 78.6% and 68.8%, respectively, compared with 72.3% and 63.8%, respectively, for patients with nonmucinous tumours. Multivariate analysis using the Cox proportional hazards model showed that the clinically significant prognostic factors were stage of disease and adjuvant chemotherapy. No statistically significant interaction between mucinous histology and adjuvant chemotherapy was found. For patients with stage II and III colon cancer who underwent curative surgery, mucinous histology has no significant correlation with prognosis compared with NMC. This retrospective analysis suggests a comparable benefit from adjuvant chemotherapy for MC compared with NMC.