A comparative study on feature selection for a risk prediction model for colorectal cancer
Catalan Institute of Oncology-IDIBELL（加泰罗尼亚肿瘤研究所-IDIBELL）；University of Barcelona（巴塞罗那大学）；Public Health Division, Department of Health of Madrid（马德里卫生部公共卫生司）
N. Cueto-López, M. T. García-Ordás, V. Dávila-Batista, V. Moreno, N. Aragonés, R. Alaiz-Rodríguez
Background and objective
Risk prediction models aim at identifying people at higher risk of developing a target disease. Feature selection is particularly important to improve the prediction model performance avoiding overfitting and to identify the leading cancer risk (and protective) factors. Assessing the stability of feature selection/ranking algorithms becomes an important issue when the aim is to analyze the features with more prediction power. Methods
This work is focused on colorectal cancer, assessing several feature ranking algorithms in terms of performance for a set of risk prediction models (Neural Networks, Support Vector Machines (SVM), Logistic Regression, k-Nearest Neighbors and Boosted Trees). Additionally, their robustness is evaluated following a conventional approach with scalar stability metrics and a visual approach proposed in this work to study both similarity among feature ranking techniques as well as their individual stability. A comparative analysis is carried out between the most relevant features found out in this study and features provided by the experts according to the state-of-the-art knowledge. Results
The two best performance results in terms of Area Under the ROC Curve (AUC) are achieved with a SVM classifier using the top-41 features selected by the SVM wrapper approach (AUC=0.693) and Logistic Regression with the top-40 features selected by the Pearson (AUC=0.689). Experiments showed that performing feature selection contributes to classification performance with a 3.9% and 1.9% improvement in AUC for the SVM and Logistic Regression classifier, respectively, with respect to the results using the full feature set. The visual approach proposed in this work allows to see that the Neural Network-based wrapper ranking is the most unstable while the Random Forest is the most stable.
MorphDistill: Distilling Unified Morphological Knowledge from Pathology Foundation Models for Colorectal Cancer Survival Prediction
Department of Pathology, College of Medicine, The Ohio State University Wexner Medical Center（俄亥俄州立大学医学学院病理学系，韦克斯纳医学中心）；Center for Artificial Intelligence Research, Wake Forest University School of Medicine（威克森林大学医学院人工智能研究中心）
Princeton University（普林斯顿大学）；New Mexico State University（新墨西哥州立大学）
In this work, we investigate the importance of ethnicity in colorectal cancer survivability prediction using machine learning techniques and the SEER cancer incidence database. We compare model performances for 2-year survivability prediction and feature importance rankings between Hispanic, White, and mixed patient populations. Our models consistently perform better on single-ethnicity populations and provide different feature importance rankings when trained in different populations. Additionally, we show our models achieve higher Area Under Curve (AUC) score than the best reported in the li…
Microscopic examination of tissues or histopathology is one of the diagnostic procedures for detecting colorectal cancer. The pathologist involved in such an examination usually identifies tissue type based on texture analysis, especially focusing on tumour-stroma ratio. In this work, we automate the task of tissue classification within colorectal cancer histology samples using deep transfer learning. We use discriminative fine-tuning with one-cycle-policy and apply structure-preserving colour normalization to boost our results. We also provide visual explanations of the deep neural network's …
University of Science and Technology of China（中国科学技术大学）；UNSW Sydney（悉尼新南威尔士大学）；Genmab Inc.（Genmab公司）
Most early-stage colorectal cancer (CRC) patients can be cured by surgery alone, and only certain high-risk early-stage CRC patients benefit from adjuvant chemotherapies. However, very few validated biomarkers are available to accurately predict survival benefit from postoperative chemotherapy. We developed a novel deep-learning algorithm (CRCNet) using whole-slide images from Molecular and Cellular Oncology (MCO) to predict survival benefit of adjuvant chemotherapy in stage II/III CRC. We validated CRCNet both internally through cross-validation and externally using an independent cohort from…
Indian Institute of Technology Jodhpur（印度理工学院焦特布尔分校）；Shri Guruji Gobind Singhji Institute of Engineering and Technology（什里古鲁吉·戈宾德·辛格吉工程技术学院）
Colorectal cancer is a leading cause of death worldwide. However, early diagnosis dramatically increases the chances of survival, for which it is crucial to identify the tumor in the body. Since its imaging uses high-resolution techniques, annotating the tumor is time-consuming and requires particular expertise. Lately, methods built upon Convolutional Neural Networks(CNNs) have proven to be at par, if not better in many biomedical segmentation tasks. For the task at hand, we propose another CNN-based approach, which uses atrous convolutions and residual connections besides the conventional fi…
Colorectal cancer is a leading cause of cancer death for both men and women. For this reason, histopathological characterization of colorectal polyps is the major instrument for the pathologist in order to infer the actual risk for cancer and to guide further follow-up. Colorectal polyps diagnosis includes the evaluation of the polyp type, and more importantly, the grade of dysplasia. This latter evaluation represents a critical step for the clinical follow-up. The proposed deep learning-based classification pipeline is based on state-of-the-art convolutional neural network, trained using prop…
Data imbalance remains one of the open challenges in the contemporary machine learning. It is especially prevalent in case of medical data, such as histopathological images. Traditional data-level approaches for dealing with data imbalance are ill-suited for image data: oversampling methods such as SMOTE and its derivatives lead to creation of unrealistic synthetic observations, whereas undersampling reduces the amount of available data, critical for successful training of convolutional neural networks. To alleviate the problems associated with over- and undersampling we propose a novel two-st…
Dealing with the application of grading colorectal cancer images, this work proposes a 3 step pipeline for prediction of cancer levels from a histopathology image. The overall model performs better compared to other state of the art methods on the colorectal cancer grading data set and shows excellent performance for the extended colorectal cancer grading set. The performance improvements can be attributed to two main factors: The feature selection and graph augmentation method described here are spatially aware, but overall pixel position independent. Further, the graph size in terms of nodes…
Domain-specific transfer learning in the automated scoring of tumor-stroma ratio from histopathological images of colorectal cancer
University of Jyväskylä（于韦斯屈莱大学）；Oulu University Hospital（奥卢大学医院）；University of Oulu（奥卢大学）；Tampere University（坦佩雷大学）；University of Turku（图尔库大学）；Turku University Hospital（图尔库大学医院）；Hospital Nova of Central Finland（中芬兰新星医院）
本文提出基于卷积神经网络的结直肠癌组织病理图像 TSR 自动估算方法，评估领域特定迁移学习，最高三类分类准确率达 96.1%，与病理学家评估相关性为 0.57。
Neural networks are capable of learning rich, nonlinear feature representations shown to be beneficial in many predictive tasks. In this work, we use such models to explore different geographical feature representations in the context of predicting colorectal cancer survival curves for patients in the state of Iowa, spanning the years 1989 to 2013. Specifically, we compare model performance using "area between the curves" (ABC) to assess (a) whether survival curves can be reasonably predicted for colorectal cancer patients in the state of Iowa, (b) whether geographical features improve predict…
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；New York University（纽约大学）
In this study, with the goal of reducing the early detection miss rate of colorectal cancer (CRC) polyps, we propose utilizing a novel hyper-sensitive vision-based tactile sensor called HySenSe and a complementary and novel machine learning (ML) architecture that explores the potentials of utilizing dilated convolutions, the beneficial features of the ResNet architecture, and the transfer learning concept applied on a small dataset with the scale of hundreds of images. The proposed tactile sensor provides high-resolution 3D textural images of CRC polyps that will be used for their accurate cla…
University of Warwick（华威大学）；National University of Sciences and Technology（国立科技大学）；University Hospitals Coventry（考文垂大学医院）；The Alan Turing Institute（艾伦·图灵研究所）
Digital histology images are amenable to the application of convolutional neural network (CNN) for analysis due to the sheer size of pixel data present in them. CNNs are generally used for representation learning from small image patches (e.g. 224x224) extracted from digital histology images due to computational and memory constraints. However, this approach does not incorporate high-resolution contextual information in histology images. We propose a novel way to incorporate larger context by a context-aware neural network based on images with a dimension of 1,792x1,792 pixels. The proposed fr…
Quantum Machine Learning for Colorectal Cancer Data: Anastomotic Leak Classification and Risk Factors
Department of Computer Science, Technical University of Ostrava, Czech Republic IT4Innovations National Supercomputing Center, Ostrava, Czech Republic Department of Applied Mathematics, Technical University of Ostrava, Czech Republic Department of Surgical Studies, Faculty of Medicine, University of Ostrava, Czech Republic Department of Surgery, University Hospital Ostrava, Czech Republic Dipartimento di Ingegneria e Scienze dell'Informazione e Matematica, Universit\` a dell'Aquila, Via Vetoio, I-67010 Coppito, L'Aquila, Italy
University of Central Florida（中佛罗里达大学）；Mayo Clinic（妙佑医疗国际（梅奥诊所））；University of Catania（卡塔尼亚大学）
Colorectal cancer, largely arising from precursor lesions called polyps, remains one of the leading causes of cancer-related death worldwide. Current clinical standards require the resection and histopathological analysis of polyps due to test accuracy and sensitivity of optical biopsy methods falling substantially below recommended levels. In this study, we design a novel capsule network architecture (D-Caps) to improve the viability of optical biopsy of colorectal polyps. Our proposed method introduces several technical novelties including a novel capsule architecture with a capsule-average …
Improving Colorectal Cancer Screening and Risk Assessment through Predictive Modeling on Medical Images and Records
Geisel School of Medicine at Dartmouth（达特茅斯盖泽尔医学院）；Dartmouth-Hitchcock Medical Center（达特茅斯-希区柯克医学中心）；New Hampshire Colonoscopy Registry（新罕布什尔结肠镜检查登记处）；White River Junction VA Medical Center（怀特河交界处退伍军人事务医疗中心）；Dartmouth College（达特茅斯学院）
Qatar University（卡塔尔大学）；Izmir University of Economics（伊兹密尔经济大学）；Tampere University of Technology（坦佩雷理工大学）
Computer-aided diagnosis (CAD) based on histopathological imaging has progressed rapidly in recent years with the rise of machine learning based methodologies. Traditional approaches consist of training a classification model using features extracted from the images, based on textures or morphological properties. Recently, deep-learning based methods have been applied directly to the raw (unprocessed) data. However, their usability is impacted by the paucity of annotated data in the biomedical sector. In order to leverage the learning capabilities of deep Convolutional Neural Nets (CNNs) withi…
Department Of Computer Science University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校计算机科学系）
Colorectal cancer (CRC) is a common and lethal disease. Globally, CRC is the third most commonly diagnosed cancer in males and the second in females. For colorectal cancer, the best screening test available is the colonoscopy. During a colonoscopic procedure, a tiny camera at the tip of the endoscope generates a video of the internal mucosa of the colon. The video data are displayed on a monitor for the physician to examine the lining of the entire colon and check for colorectal polyps. Detection and removal of colorectal polyps are associated with a reduction in mortality from colorectal canc…
MedBravo；Alicante University General Hospital（阿利坎特大学综合医院）；Alicante Institute for Health and Biomedical Research（阿利坎特健康与生物医学研究所）；Yale University（耶鲁大学）；Hospital del Mar（马尔医院）；Hospital del Mar Medical Research Institute（马尔医院医学研究所）；Hospital Clínic（医院诊所医院）；University of Barcelona（巴塞罗那大学）
We present a system for the prediction of microsatellite instability (MSI) from H&E images of colorectal cancer using deep learning (DL) techniques customized for tissue microarrays (TMAs). The system incorporates an end-to-end image preprocessing module that produces tiles at multiple magnifications in the regions of interest as guided by a tissue classifier module, and a multiple-bias rejecting module. The training and validation TMA samples were obtained from the EPICOLON project and further enriched with samples from a single institution. A systematic study of biases at tile level identifi…
University of Saskatchewan（萨斯喀彻温大学）；University of Stanford（斯坦福大学）
Colorectal cancer is the third most common cancer-related death after lung cancer and breast cancer worldwide. The risk of developing colorectal cancer could be reduced by early diagnosis of polyps during a colonoscopy. Computer-aided diagnosis systems have the potential to be applied for polyp screening and reduce the number of missing polyps. In this paper, we compare the performance of different deep learning architectures as feature extractors, i.e. ResNet, DenseNet, InceptionV3, InceptionResNetV2 and SE-ResNeXt in the encoder part of a U-Net architecture. We validated the performance of p…
Colorectal Cancer Histopathological Grading using Multi-Scale Federated Learning
Department of Computer Science and Engineering, European University of Bangladesh（欧洲 bangladesh 大学 计算机科学与工程系）；Department of Computer Science and Engineering , BRAC University,Dhaka, Bangladesh（brac 大学 计算机科学与工程系）；Department of Computer Science and Engineering , Daffodil International University, Dhaka, Bangladesh（daffodil 国际大学 计算机科学与工程系）；Department of Institute of Information Technology, University of Dhaka, Bangladesh（达卡大学 信息科技学院）；Department of Computer Science and Engineering , BRAC University,Dhaka（brac 大学 计算机科学与工程系）；Ochsner Center for Outcomes Research, Ochsner Research, New Orleans, LA 70121, USA（ochsner 成果研究中心，ochsner 研究）
Predicting microsatellite instability and key biomarkers in colorectal cancer from H&E-stained images: Achieving SOTA predictive performance with fewer data using Swin Transformer
University of Science and Technology of China（中国科学技术大学）；UNSW Sydney（悉尼新南威尔士大学）；Genmab Inc.（Genmab公司）
Northwestern University Feinberg School of Medicine（西北大学范伯格医学院）
Background: The increasing adoption of electronic health records (EHR) across the US has created troves of computable data, to which machine learning methods have been applied to extract useful insights. EHR data, represented as a three-dimensional analogue of a matrix (tensor), is decomposed into two-dimensional factors that can be interpreted as computational phenotypes. Methods: We apply constrained tensor factorization to derive computational phenotypes and predict mortality in cohorts of patients with breast, prostate, colorectal, or lung cancer in the Northwestern Medicine Enterprise Dat…
State University of Ponta Grossa（蓬塔格罗萨州立大学）；Pontifical Catholic University of Paraná（巴拉那天主教大学）；Federal University of Parana（巴拉那联邦大学）
This work proposes a classification approach for breast cancer histopathologic images (HI) that uses transfer learning to extract features from HI using an Inception-v3 CNN pre-trained with ImageNet dataset. We also use transfer learning on training a support vector machine (SVM) classifier on a tissue labeled colorectal cancer dataset aiming to filter the patches from a breast cancer HI and remove the irrelevant ones. We show that removing irrelevant patches before training a second SVM classifier, improves the accuracy for classifying malign and benign tumors on breast cancer images. We are …
Decoding Future Risk: Deep Learning Analysis of Tubular Adenoma Whole-Slide Images
Dept. of Pathology（病理学系）；Honeywell International Inc.（霍尼韦尔国际公司）；South Bend Medical Foundation（南本德医疗基金会）；Dept. of Electrical and Computer Engineering（电气与计算机工程系）
The past years have seen a considerable increase in cancer cases. However, a cancer diagnosis is often complex and depends on the types of images provided for analysis. It requires highly skilled practitioners but is often time-consuming and error-prone. If Machine Learning and deep learning algorithms have been widely used, a comprehensive review of the techniques used from the pre-processing steps to the final prediction is lacking. With this review, we aim to provide a comprehensive overview of the current steps required in building efficient and accurate machine learning algorithm for canc…
