Classification with respect to colon adenocarcinoma and colon benign tissue of colon histopathological images with a new CNN model: MA_ColonNET

Author(s):  
Muhammed Yildirim ◽  
Ahmet Cinar
2020 ◽  
Author(s):  
Hui Li ◽  
Hao Zeng ◽  
Linyan Chen ◽  
Qimeng Liao ◽  
Jianrui Ji ◽  
...  

Abstract Background: Colon adenocarcinoma (COAD) is one of the highest morbidity cancers all over the world. Its 5-year survival is no more than 60% even in European countries with the highest survival rates. The histopathological information is crucial for the prognosis and therapy of COAD. Application of the digital whole slide imaging system enables us to read histopathological sections digitally. Apart from that, cancer genomics is also an important prognostic factor.Methods: To identify prognosis biomarkers of COAD, we downloaded whole-slide histopathological images from TCIA database. After processing these images, histopathological features were extracted by CellProfiler. Least Absolute Shrinkage and Selection Operator and Support Vector Machine Recursive Feature Elimination were followed applied, screening out 5 prognosis-related features. Weighted gene co-expression network analysis (WGCNA) was operated to find co-expression gene module correlated with prognosis-related features. The samples were divided into a training set and a testing set on a scale of 70% and 30%. Random forest was applied to construct histopathologic-genomic prognosis factor (HGPF) using prognosis-related features and genomic data. After that, we combined HGPF and clinical characteristics with nomogram and verify its predictive efficacy.Results: The time-dependent ROC was drawn to evaluate the efficacy of prognostic model. In the training set, 1-year, 3-year and 5-year AUCs are respectively 0.948, 0.916, 0.933. In the testing set, 1-year, 3-year and 5-year AUCs are respectively 0.913, 0.894, 0.924. In addition, patients were separated into high-risk survival group and low-risk survival group by HGPF. Survival analysis indicates that the low-risk patients’ survival was significantly better than high-risk patients’ in both training set and testing set. It is suggested that histopathological image features have certain ability to predict COAD survival, which can be further improved by means of multi-omics combination.Conclusions: In conclusion, this study constructs an integrative prognosis model based on histopathological and genomic features, which may have some guidance effect on prognosis and clinical decision of COAD patients. Furthermore, the underlying biological mechanisms of this multi-omics model require further study.


2021 ◽  
Vol 11 ◽  
Author(s):  
Hui Li ◽  
Linyan Chen ◽  
Hao Zeng ◽  
Qimeng Liao ◽  
Jianrui Ji ◽  
...  

BackgroundColon adenocarcinoma (COAD) is one of the most common malignant tumors in the world. The histopathological features are crucial for the diagnosis, prognosis, and therapy of COAD.MethodsWe downloaded 719 whole-slide histopathological images from TCIA, and 459 corresponding HTSeq-counts mRNA expression and clinical data were obtained from TCGA. Histopathological image features were extracted by CellProfiler. Prognostic image features were selected by the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) algorithms. The co-expression gene module correlated with prognostic image features was identified by weighted gene co-expression network analysis (WGCNA). Random forest was employed to construct an integrative prognostic model and calculate the histopathological-genomic prognosis factor (HGPF).ResultsThere were five prognostic image features and one co-expression gene module involved in the model construction. The time-dependent receiver operating curve showed that the prognostic model had a significant prognostic value. Patients were divided into high-risk group and low-risk group based on the HGPF. Kaplan-Meier analysis indicated that the overall survival of the low-risk group was significantly better than the high-risk group.ConclusionsThese results suggested that the histopathological image features had a certain ability to predict the survival of COAD patients. The integrative prognostic model based on the histopathological images and genomic features could further improve the prognosis prediction in COAD, which may assist the clinical decision in the future.


1999 ◽  
Vol 38 (04) ◽  
pp. 115-119
Author(s):  
N. Oriuchi ◽  
S. Sugiyama ◽  
M. Kuroki ◽  
Y. Matsuoka ◽  
S. Tanada ◽  
...  

Summary Aim: The purpose of this study was to assess the potential for radioimmunodetection (RAID) of murine anti-carcinoembryonic antigen (CEA) monoclonal antibody (MAb) F33-104 labeled with technetium-99m (99m-Tc) by a reduction-mediated labeling method. Methods: The binding capacity of 99m-Tc-labeled anti-CEA MAb F33-104 with CEA by means of in vitro procedures such as immunoradiometric assay and cell binding assay and the biodistribution of 99m-Tc-labeled anti-CEA MAb F33-104 in normal nude mice and nude mice bearing human colon adenocarcinoma LS180 tumor were investigated and compared with 99m-Tc-labeled anti-CEA MAb BW431/26. Results: The in vitro binding rate of 99m-Tc-labeled anti-CEA MAb F33-104 with CEA in solution and attached to the cell membrane was significantly higher than 99m-Tclabeled anti-CEA MAb BW431/261 (31.4 ± 0.95% vs. 11.9 ± 0.55% at 100 ng/mL of soluble CEA, 83.5 ± 2.84% vs. 54.0 ± 2.54% at 107 of LS 180 cells). In vivo, accumulation of 99m-Tc-labeled anti-CEA MAb F33-104 was higher at 18 h postinjection than 99m-Tc-labeled anti-CEA MAb BW431/26 (20.1 ± 3.50% ID/g vs. 14.4 ± 3.30% ID/g). 99m-Tcactivity in the kidneys of nude mice bearing tumor was higher at 18 h postinjection than at 3 h (12.8 ± 2.10% ID/g vs. 8.01 ± 2.40% ID/g of 99m-Tc-labeled anti-CEA MAb F33-104, 10.7 ± 1.70% ID/g vs. 8.10 ± 1.75% ID/g of 99m-Tc-labeled anti-CEA MAb BW431/26). Conclusion: 99m-Tc-labeled anti-CEA MAb F33-104 is a potential novel agent for RAID of recurrent colorectal cancer.


Author(s):  
Inzamam Mashood Nasir ◽  
Muhammad Rashid ◽  
Jamal Hussain Shah ◽  
Muhammad Sharif ◽  
Muhammad Yahiya Haider Awan ◽  
...  

Background: Breast cancer is considered as the most perilous sickness among females worldwide and the ratio of new cases is expanding yearly. Many researchers have proposed efficient algorithms to diagnose breast cancer at early stages, which have increased the efficiency and performance by utilizing the learned features of gold standard histopathological images. Objective: Most of these systems have either used traditional handcrafted features or deep features which had a lot of noise and redundancy, which ultimately decrease the performance of the system. Methods: A hybrid approach is proposed by fusing and optimizing the properties of handcrafted and deep features to classify the breast cancer images. HOG and LBP features are serially fused with pretrained models VGG19 and InceptionV3. PCR and ICR are used to evaluate the classification performance of proposed method. Results: The method concentrates on histopathological images to classify the breast cancer. The performance is compared with state-of-the-art techniques, where an overall patient-level accuracy of 97.2% and image-level accuracy of 96.7% is recorded. Conclusion: The proposed hybrid method achieves the best performance as compared to previous methods and it can be used for the intelligent healthcare systems and early breast cancer detection.


2018 ◽  
Vol 18 (8) ◽  
pp. 1184-1196 ◽  
Author(s):  
Abdel-Ghany A. El-Helby ◽  
Helmy Sakr ◽  
Rezk R.A. Ayyad ◽  
Khaled El-Adl ◽  
Mamdouh M. Ali ◽  
...  

Background: Extensive studies were reported in the synthesis of several phthalazine derivatives as promising anticancer agents as potent VEGFR-2 inhibitors. Vatalanib (PTK787) was the first anilinophthalazine published derivative as a potent inhibitor of VEGFR. The discovery of vatalanib as a clinical candidate led to the design and synthesis of different anilinophthalazine derivatives as potent inhibitors for VEGFR-2. The objective of present research work is the synthesis of new agents with the same essential pharmacophoric features of the reported and clinically used VEGFR-2 inhibitors (e.g vatalanib and sorafenib). The main core of our molecular design rationale comprised bioisosteric modification strategies of VEGFR-2 inhibitors at four different positions. </P><P> Material and Methods: A correlation between structure and biological activity of our designed phthalazines was established using molecular docking and VEGFR-2 kinase assay. Results and Discussion: In view of their expected anticancer activity, novel triazolo[3,4-a]phthalazine derivatives 5-6a-o and 3-substituted-bis([1,2,4]triazolo)[3,4-a:4',3'-c]phthalazines 9a-b were designed, synthesized and evaluated for their anti-proliferative activity against two human tumor cell lines HCT-116 human colon adenocarcinoma and MCF-7 breast cancer. It was found that, compound 6o the most potent derivative against both HCT116 and MCF-7 cancer cell lines. Compounds 6o, 6m, 6d and 9b showed the highest anticancer activities against HCT116 human colon adenocarcinoma with IC50 of 7±0.06, 13±0.11, 15±0.14 and 23±0.22 µM respectively while compounds 6o, 6d, 6a and 6n showed the highest anticancer activities against MCF-7 breast cancer with IC50 of 16.98±0.15, 18.2±0.17, 57.54±0.53 and 66.45±0.67 µM respectively. Sorafenib as a highly potent VEGFR-2 inhibitor was used as a reference drug with IC50 of 5.47±0.3 and 7.26±0.3 µM respectively. Nine compounds were further evaluated for their VEGFR-2 inhibitory activity. Compounds 6o, 6m, 6d and 9b emerged as the most active counterparts against VEGFR-2 with IC50 values of 0.1±0.01, 0.15±0.02, 0.28±0.03 and 0.38±0.04 µM, respectively comparable to that of sorafenib (IC50 = 0.1±0.02) µM. Furthermore, molecular docking studies were carried out for all synthesized compounds to investigate their binding pattern and predict their binding affinities towards VEGFR-2 active site. In silico ADMET studies were calculated for the tested compounds. Most of our designed compounds exhibited good ADMET profile. Conclusion: The obtained results showed that, the most active compounds could be useful as a template for future design, optimization, adaptation and investigation to produce more potent and selective VEGFR-2 inhibitors with higher anticancer analogs.


2020 ◽  
Vol 14 ◽  
Author(s):  
Lahari Tipirneni ◽  
Rizwan Patan

Abstract:: Millions of deaths all over the world are caused by breast cancer every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increases the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. The current paper presents an ensemble Convolutional neural network for multi class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from publicly available BreakHis dataset and classified between 8 classes. The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification.


2013 ◽  
Vol 217 (3) ◽  
pp. S127
Author(s):  
Karen K. Lo ◽  
Carlton C. Barnett ◽  
Sean P. Colgan ◽  
Richard D. Schulick ◽  
Denis D. Bensard ◽  
...  

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