An early stage cervix cancer diagnosis system

Tumor-derived exosomes have been recognized as promising biomarkers for early-stage cancer diagnosis, tumor prognosis monitoring and individual medical treatment. However, separating exosomes from trace biological samples is a huge challenge...

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From Bench to Bedside: New Technologies for Cancer Diagnosis and Treatment

Journal of International Biotechnology Law ◽

10.1515/jibl.2008.14 ◽

2008 ◽

Vol 5 (2) ◽

Author(s):

Michal-Ruth Schweiger ◽

Hans Lehrach

Keyword(s):

Cancer Diagnosis ◽

New Technologies ◽

Early Stage ◽

Predictive Biomarkers ◽

Patient Treatment ◽

Malignant Neoplasms ◽

Advanced Stage ◽

Common Cause ◽

The Us ◽

Risk Patients

According to the centre for disease control (CDC) malignant neoplasms are the second most common cause of death in the US in 2004 (1). One of the major problems is that most of the cancers are diagnosed in an advanced stage, which prohibits curative treatment. In order to circumvent these problems, we need to develop strategies that allow identification of risk patients and tumors at an early stage. In addition, it is necessary to identify prognostic and predictive biomarkers that guide patient treatment at different stages of the disease.

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Lung Cancer Diagnosis System Based on Support Vector Machines and Image Processing Technique

2006 International Conference on Intelligent Information Hiding and Multimedia ◽

10.1109/iih-msp.2006.264974 ◽

2006 ◽

Author(s):

Kewen Xia ◽

Guan Xu ◽

Naixun Xu

Keyword(s):

Lung Cancer ◽

Image Processing ◽

Support Vector Machines ◽

Cancer Diagnosis ◽

Processing Technique ◽

Image Processing Technique ◽

Support Vector ◽

Diagnosis System ◽

Lung Cancer Diagnosis ◽

Vector Machines

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CNN-based Diagnosis System on Skin Cancer using Ensemble Method Weighted by Cubic Precision

10.36227/techrxiv.16964893.v1 ◽

2021 ◽

Author(s):

Pengcheng Jiang

Keyword(s):

User Interface ◽

Skin Cancer ◽

Language Processing ◽

Classification Accuracy ◽

Model Updating ◽

Early Stage ◽

Model Performance ◽

Diagnosis System ◽

Cancer Symptoms ◽

User Friendly

<i>Abstract</i>— One of the most prevalent diseases, skin cancer, has been proven to be treatable at an early stage. Thus, techniques that allow individuals to identify skin cancer symptoms early are in great demand. This paper proposed an interactive skin lesion diagnosis system based on the ensemble of multiple sophisticated CNN models for image classification. The performance of ResNet50, ResNeXt50, ResNeXt101, EfficientNetB4, Mobile-NetV2, MobileNetV3, and MnasNet are investigated separately as ensemble components. Then, using various criteria, we constructed ensembles and compared the accuracy they achieved. Moreover, we designed a method to update the ensemble for new data and examined its performance. In addition, a few natural language processing (NLP) techniques were used to make our system more user-friendly. To integrate all the functionalities, we built a user interface with PyQt5. As a result, MobileNetV3 achieved 91.02% as the best accuracy among all single models; ensemble weighted by cubic precision achieved 92.84% accuracy as the highest one in this study; a notable improvement in accuracy demonstrated the effectiveness of the model updating approach, and a system with all of the desired features was successfully developed. These findings benefit in two aspects. For model performance, applying cubic precisions can increase ensemble learning classification accuracy. For the developed diagnosis system, it can aid in the

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Longitudinal Changes in Sports Activity From Pre-diagnosis to First Five Years Post-diagnosis: a Prospective Chinese Breast Cancer Cohort Study

10.21203/rs.3.rs-35306/v1 ◽

2020 ◽

Author(s):

Yuanyuan Lei ◽

Suzanne C. Ho ◽

Carol Kwok ◽

Ashley Cheng ◽

Ka Li Cheung ◽

...

Keyword(s):

Breast Cancer ◽

Physical Activity ◽

Cancer Survivors ◽

Cancer Diagnosis ◽

Physical Activity Level ◽

Chinese Women ◽

Early Stage ◽

Activity Level ◽

Significant Difference ◽

After Cancer

Abstract Background: To compare change in level of physical activitybetween pre-and post- diagnosis of breast cancer in Chinese women.Methods:Based on an on-going prospective study consisting of 1462 Chinese women with early-stage breast cancer, a validated modified Chinese Baecke questionnaire was used to measure physical activity at baseline (12 months before cancer diagnosis), 18-, 36- and 60-months after diagnosis (over the previous 12 months before each interview). Results:The overall physical activity level at post-diagnosis was 5.8 MET-hours/week, which was significantly higher than that at pre-diagnosis at a median level of 0.6 MET-hours/week (P <0.001).The median levels of physical activity at 18-, 36- and 60-months follow-up were5.3, 4.4 and 3.9 MET-hours/week, respectively. There was no significant difference between any two of the three follow-ups at post-diagnosis. The proportions of participant who met WCRF/AICR recommendation before and after cancer diagnosis were both low, being 20.7% and 35.1%, respectively.Compared to pre-diagnosis, most of the patients improved or had no change on level of physical activity at post-diagnosis, with the respective proportion being 48.2% and 43.8%. Conclusions:Adherence to current lifestyle recommendation for cancer survivors, Chinese women with breast cancer significantly increasedlevel of physical activity level after cancer diagnosis, and such improvement was sustained to five years post-diagnosis. The proportion of patients who met the exercise recommendation for cancer survivors was still low. Encouraging patients on the importance of durable high level of physical activity in breast cancer survivorship is warranted.

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Diagnostic value of macrophage inhibitory cytokine 1 as a novel prognostic biomarkers for early gastric cancer screening

10.21203/rs.3.rs-17962/v1 ◽

2020 ◽

Author(s):

Xin Ge ◽

Xiaolei Zhang ◽

Yanling Ma ◽

Shaohua Chen ◽

Zhaowu Chen ◽

...

Keyword(s):

Gastric Cancer ◽

Early Gastric Cancer ◽

Cancer Diagnosis ◽

Early Stage ◽

Precancerous Lesions ◽

Diagnostic Value ◽

Low Grade ◽

Serum Samples ◽

Early Screening ◽

Roc Curve Analysis

Abstract BACKGROUND Early diagnosis is very important to improve the survival rate of patients with gastric cancer, especially in asymptomatic participants. However, low sensitivity of common biomarkers has caused difficulties in early screening of gastric cancer. In this study, we explored whether MIC-1 can improve the detection rate of early gastric cancer.METHODS We screened 8,257 participants based on risk factors such as age, gender, and family history for physical examination including gastroscopy. Participant blood samples were taken for measure MIC-1, CA-199, CA72-4 and PG1/PG2 levels. The diagnostic performance of MIC-1 was assessed and compared with CA-199, CA72-4 and PG1/PG2, and its role in early gastric cancer diagnosis and the assessment of the risk of precancerous lesions have also been studied.RESULTS Based on endoscopic and histopathological findings, 55 participants had gastric cancer, 566 participants had low-grade neoplasia, 2605 participants had chronic gastritis. MIC-1 levels were significantly elevated in gastric cancer serum samples as compared to controls (p<0.001). The sensitivity of serum MIC-1 for gastric cancer diagnosis was much higher than that of CA-199 (49.1% vs. 20.0%) with similar specificities. Moreover, receiver operating characteristic (ROC) curve analysis also showed that serum MIC-1 had a better performance compared with CA-199, CA72-4 and PG1/PG2 in distinguishing early-stage gastric cancer (AUC: 72.9% vs. 69.5%, 67.5%, 44.0% respectively).CONCLUSIONS Serum MIC-1 is significantly elevated in most patients with early gastric cancer. MIC-1 can serve as a novel diagnostic marker of early gastric cancer and value the risk of gastric cancer.

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A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis

Computational and Mathematical Methods in Medicine ◽

10.1155/2019/6509357 ◽

2019 ◽

Vol 2019 ◽

pp. 1-16 ◽

Cited By ~ 15

Author(s):

Lian Zou ◽

Shaode Yu ◽

Tiebao Meng ◽

Zhicheng Zhang ◽

Xiaokun Liang ◽

...

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Convolutional Neural Network ◽

Cancer Diagnosis ◽

Early Stage ◽

Breast Cancer Diagnosis ◽

Fine Tuning ◽

Learning Tools ◽

Pros And Cons ◽

Future Work

This study reviews the technique of convolutional neural network (CNN) applied in a specific field of mammographic breast cancer diagnosis (MBCD). It aims to provide several clues on how to use CNN for related tasks. MBCD is a long-standing problem, and massive computer-aided diagnosis models have been proposed. The models of CNN-based MBCD can be broadly categorized into three groups. One is to design shallow or to modify existing models to decrease the time cost as well as the number of instances for training; another is to make the best use of a pretrained CNN by transfer learning and fine-tuning; the third is to take advantage of CNN models for feature extraction, and the differentiation of malignant lesions from benign ones is fulfilled by using machine learning classifiers. This study enrolls peer-reviewed journal publications and presents technical details and pros and cons of each model. Furthermore, the findings, challenges and limitations are summarized and some clues on the future work are also given. Conclusively, CNN-based MBCD is at its early stage, and there is still a long way ahead in achieving the ultimate goal of using deep learning tools to facilitate clinical practice. This review benefits scientific researchers, industrial engineers, and those who are devoted to intelligent cancer diagnosis.

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