scholarly journals Decoding intra-tumoral spatial heterogeneity on radiological images using the Hilbert curve

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lu Wang ◽  
Nan Xu ◽  
Jiangdian Song
Keyword(s):  
Dimensionality Reduction ◽  
Spatial Heterogeneity ◽  
Lung Tumor ◽  
Axial Plane ◽  
Lung Nodules ◽  
Hilbert Curve ◽  
Spatial Correspondence ◽  
Spatial Locality ◽  
Radiological Images ◽  
Correspondence Mapping

Abstract Background Current intra-tumoral heterogeneous feature extraction in radiology is limited to the use of a single slice or the region of interest within a few context-associated slices, and the decoding of intra-tumoral spatial heterogeneity using whole tumor samples is rare. We aim to propose a mathematical model of space-filling curve-based spatial correspondence mapping to interpret intra-tumoral spatial locality and heterogeneity. Methods A Hilbert curve-based approach was employed to decode and visualize intra-tumoral spatial heterogeneity by expanding the tumor volume to a two-dimensional (2D) matrix in voxels while preserving the spatial locality of the neighboring voxels. The proposed method was validated using three-dimensional (3D) volumes constructed from lung nodules from the LIDC-IDRI dataset, regular axial plane images, and 3D blocks. Results Dimensionality reduction of the Hilbert volume with a single regular axial plane image showed a sparse and scattered pixel distribution on the corresponding 2D matrix. However, for 3D blocks and lung tumor inside the volume, the dimensionality reduction to the 2D matrix indicated regular and concentrated squares and rectangles. For classification into benign and malignant masses using lung nodules from the LIDC-IDRI dataset, the Inception-V4 indicated that the Hilbert matrix images improved accuracy (85.54% vs. 73.22%, p < 0.001) compared to the original CT images of the test dataset. Conclusions Our study indicates that Hilbert curve-based spatial correspondence mapping is promising for decoding intra-tumoral spatial heterogeneity of partial or whole tumor samples on radiological images. This spatial-locality-preserving approach for voxel expansion enables existing radiomics and convolution neural networks to filter structured and spatially correlated high-dimensional intra-tumoral heterogeneity.

scholarly journals Evaluation of undiagnosed solitary lung nodules according to the probability of malignancy in the American College of Chest Physicians (ACCP) evidence-based clinical practice guidelines

2014 ◽  
Vol 48 (1) ◽  
pp. 50-55 ◽  
Author(s):  
Shinji Shinohara ◽  
Takeshi Hanagiri ◽  
Masaru Takenaka ◽  
Yasuhiro Chikaishi ◽  
Soich Oka ◽  
...  
Keyword(s):  
Negative Predictive Value ◽  
Roc Curve ◽  
Predictive Value ◽  
Lung Tumor ◽  
Malignant Tumors ◽  
Final Diagnosis ◽  
Benign Disease ◽  
Smoking History ◽  
Lung Nodules ◽  
Nodule Size

Abstract Background. This study retrospectively investigated the clinical significance of undiagnosed solitary lung nodules removed by surgical resection. Patients and methods. We retrospectively collected data on the age, smoking, cancer history, nodule size, location and spiculation of 241 patients who had nodules measuring 7 mm to 30 mm and a final diagnosis established by histopathology. We compared the final diagnosis of each patient with the probability of malignancy (POM) which was proposed by the American College of Chest Physicians (ACCP) guidelines. Results. Of the 241 patients, 203 patients were diagnosed to have a malignant lung tumor, while 38 patients were diagnosed with benign disease. There were significant differences in the patients with malignant and benign disease in terms of their age, smoking history, nodule size and spiculation. The mean value and the standard deviation of the POM in patients with malignant tumors were 51.7 + 26.1%, and that of patients with benign lesions was 34.6 + 26.7%. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.67. The best cut-off value provided from the ROC curve was 22.6. When the cut-off value was set at 22.6, the sensitivity was 83%, specificity 52%, positive predictive value 90%, negative predictive value 36% and accuracy 77%, respectively. Conclusions. The clinical prediction model proposed in the ACCP guidelines showed unsatisfactory results in terms of the differential diagnosis between malignant disease and benign disease of solitary lung nodules in our study, because the specificity, negative predictive value and AUC were relatively low.

A Hybrid deep learning model for effective segmentation and classification of lung nodules from CT images

2021 ◽  
pp. 1-13
Author(s):  
Malathi Murugesan ◽  
Kalaiselvi Kaliannan ◽  
Shankarlal Balraj ◽  
Kokila Singaram ◽  
Thenmalar Kaliannan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Lung Tumor ◽  
Median Filter ◽  
Lung Nodule ◽  
Rapid Identification ◽  
Lung Nodules ◽  
Classification Models ◽  
Deep Learning Model

Deep learning algorithms will be used to detect lung nodule anomalies at an earlier stage. The primary goal of this effort is to properly identify lung cancer, which is critical in preserving a person’s life. Lung cancer has been a source of concern for people all around the world for decades. Several researchers presented numerous issues and solutions for various stages of a computer-aided system for diagnosing lung cancer in its early stages, as well as information about lung cancer. Computer vision is one of the field of artificial intelligence this is a better way to detect and prevent the lung cancer. This study focuses on the stages involved in detecting lung tumor regions, namely pre-processing, segmentation, and classification models. An adaptive median filter is used in pre-processing to identify the noise. The work’s originality seeks to create a simple yet effective model for the rapid identification and U-net architecture based segmentation of lung nodules. This approach focuses on the identification and segmentation of lung cancer by detecting picture normalcy and abnormalities.

scholarly journals Screening of Radiological Images Suspected of Containing Lung Nodules

2022 ◽  
Vol 12 (1) ◽  
pp. 1-12
Author(s):  
Raúl Pedro Aceñero Eixarch ◽  
Raúl Díaz-Usechi Laplaza ◽  
Rafael Berlanga Llavori
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Final Diagnosis ◽  
Lung Nodules ◽  
Radiological Images ◽  
Classification Tasks ◽  
Radiological Image ◽  
Image Streams ◽  
Different Sources ◽  
And Training

This paper presents a study about screening large radiological image streams produced in hospitals for earlier detection of lung nodules. Being one of the most difficult classification tasks in the literature, our objective is to measure how well state-of-the-art classifiers can screen out the images stream to keep as many positive cases as possible in an output stream to be inspected by clinicians. We performed several experiments with different image resolutions and training datasets from different sources, always taking ResNet-152 as the base neural network. Results over existing datasets show that, contrary to other diseases like pneumonia, detecting nodules is a hard task when using only radiographies. Indeed, final diagnosis by clinicians is usually performed with much more precise images like computed tomographies.

scholarly journals The Effect of Low Carbohydrate Diets on Preventing and Treating Carcinogen-Induced Lung Cancer in Mice

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 323-323
Author(s):  
Ingrid Elisia ◽  
Brandon Cho ◽  
Mariah Hay ◽  
Michelle Yeung ◽  
Sara Kowalski ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Soy Protein ◽  
Lung Tumor ◽  
Lung Nodule ◽  
Lung Tumors ◽  
Western Diet ◽  
Lung Nodules ◽  
Nodule Formation ◽  
Significant Difference ◽  
Low Carbohydrate

Abstract Objectives Since cancer cells typically rely more on glycolysis than normal cells, we hypothesized that lowering carbohydrate intake may reduce cancer risk. We aimed to investigate the efficacy of low-carbohydrate (CHO) diets in preventing and treating a tobacco-specific carcinogen-induced lung cancer in female A/J mice. Methods We evaluated the role of different types of CHO (easily digestible vs resistant), protein (casein vs. soy) and fat (fish vs. coconut vs. a mixture of oils) in modulating 4-(N-methyl-N-nitrosamino)-1-(3- pyridyl)-1-butanone (NNK)-induced lung nodule formation in these mice. To assess the efficacy of these diets in preventing NNK-induced lung nodule formation, we put these mice in the different diets for 2 weeks, intraperitoneally-injected NNK once a week for two weeks to initiate lung nodule formation. After 5 months, the lung nodules in these mice were counted. Results The lowering of easily digestible CHO significantly reduced constitutive blood glucose levels and lung nodule formation in the mice. Interestingly, diets low in easily digestible starch, high in fish oil (FO) and soy protein (15%Amylose/Soy/FO) were the most effective at preventing the formation of NNK-induced lung nodules. To determine if this 15%Amylose/Soy/FO is also effective at slowing tumor progression, we fed NNK-injected A/J mice a Western diet until tumors were established (5 months post NNK) and then either switched them to the 15%Amylose/Soy/FO or kept them on the Western diet for 5 additional months. The 15%Amylose/Soy/FO diet prevented the formation of additional lung tumor nodules and reduced the size of the tumors, although no significant difference was observed in tumor stage.  The reduction in size of the lung tumors on the 15%Amylose/Soy/FO diet was not due to a lower tumor proliferation (Ki67 index) but an increase in apoptosis, as determined by TUNEL assays. Conclusions We conclude that a diet change that lowers glucose intake, incorporates FO and soy protein may be effective not only in preventing lung cancer formation but also in slowing the growth of established lung tumors. Funding Sources Lotte & John Hecht Memorial Foundation.

scholarly journals Evaluation of an AI-Powered Lung Nodule Algorithm for Detection and 3D Segmentation of Primary Lung Tumors

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Thomas Weikert ◽  
Tugba Akinci D’Antonoli ◽  
Jens Bremerich ◽  
Bram Stieltjes ◽  
Gregor Sommer ◽  
...  
Keyword(s):  
Lung Cancer ◽  
False Positive ◽  
Fdg Pet ◽  
Lung Tumor ◽  
Lung Nodule ◽  
Lung Tumors ◽  
Lung Nodules ◽  
3D Segmentation ◽  
Detection Rates ◽  
Primary Lung Tumors

Automated detection and segmentation is a prerequisite for the deployment of image-based secondary analyses, especially for lung tumors. However, currently only applications for lung nodules ≤3 cm exist. Therefore, we tested the performance of a fully automated AI-based lung nodule algorithm for detection and 3D segmentation of primary lung tumors in the context of tumor staging using the CT component of FDG-PET/CT and including all T-categories (T1–T4). FDG-PET/CTs of 320 patients with histologically confirmed lung cancer performed between 01/2010 and 06/2016 were selected. First, the main primary lung tumor within each scan was manually segmented using the CT component of the PET/CTs as reference. Second, the CT series were transferred to a platform with AI-based algorithms trained on chest CTs for detection and segmentation of lung nodules. Detection and segmentation performance were analyzed. Factors influencing detection rates were explored with binominal logistic regression and radiomic analysis. We also processed 94 PET/CTs negative for pulmonary nodules to investigate frequency and reasons of false-positive findings. The ratio of detected tumors was best in the T1-category (90.4%) and decreased continuously: T2 (70.8%), T3 (29.4%), and T4 (8.8%). Tumor contact with the pleura was a strong predictor of misdetection. Segmentation performance was excellent for T1 tumors (r = 0.908, p<0.001) and tumors without pleural contact (r = 0.971, p<0.001). Volumes of larger tumors were systematically underestimated. There were 0.41 false-positive findings per exam. The algorithm tested facilitates a reliable detection and 3D segmentation of T1/T2 lung tumors on FDG-PET/CTs. The detection and segmentation of more advanced lung tumors is currently imprecise due to the conception of the algorithm for lung nodules <3 cm. Future efforts should therefore focus on this collective to facilitate segmentation of all tumor types and sizes to bridge the gap between CAD applications for screening and staging of lung cancer.

Reinterpretation of radiological images of patients with the suspicion of a hepatic, pancreatic or biliary malignancy

2001 ◽  
Vol 120 (5) ◽  
pp. A734-A734
Author(s):  
E TILLEMAN ◽  
O DELDEN ◽  
E RAUWS ◽  
J LAMERIS ◽  
D GOUMA
Keyword(s):  
Biliary Malignancy ◽  
Radiological Images

Lung Tumor Resection Ups Melanoma Survival

2007 ◽  
Vol 38 (7) ◽  
pp. 46
Author(s):  
ALICIA AULT
Keyword(s):  
Lung Tumor ◽  
Tumor Resection

Analysis on Research Paper Publication Recommendation System with Composition of Papers and Conferences Matrices

2020 ◽  
pp. 120-127
Author(s):  
Htay Htay Win ◽  
Aye Thida Myint ◽  
Mi Cho Cho
Keyword(s):  
Social Network ◽  
Dimensionality Reduction ◽  
Correspondence Analysis ◽  
Recommendation System ◽  
Research Paper ◽  
Topic Modelling ◽  
Research Papers ◽  
The Right ◽  
Publication Recommendation

For years, achievements and discoveries made by researcher are made aware through research papers published in appropriate journals or conferences. Many a time, established s researcher and mainly new user are caught up in the predicament of choosing an appropriate conference to get their work all the time. Every scienti?c conference and journal is inclined towards a particular ?eld of research and there is a extensive group of them for any particular ?eld. Choosing an appropriate venue is needed as it helps in reaching out to the right listener and also to further one’s chance of getting their paper published. In this work, we address the problem of recommending appropriate conferences to the authors to increase their chances of receipt. We present three di?erent approaches for the same involving the use of social network of the authors and the content of the paper in the settings of dimensionality reduction and topic modelling. In all these approaches, we apply Correspondence Analysis (CA) to obtain appropriate relationships between the entities in question, such as conferences and papers. Our models show hopeful results when compared with existing methods such as content-based ?ltering, collaborative ?ltering and hybrid ?ltering.

Acquisition and archiving of radiological images

1991 ◽  
Vol 27 (1) ◽  
pp. 163
Author(s):  
Hyung Sik Choi ◽  
Kyu Ok Choe ◽  
Jung Ho Suh ◽  
Jong Tae Lee
Keyword(s):  
Radiological Images
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