Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge

Balagurunathan, Yoganand; Beers, Andrew; McNitt-Gray, Michael F.; Hadjiiski, Lubomir M.; Napel, Sandy; Goldgof, Dmitry B.; Pérez, Gustavo; Pont-Tuset, Jordi; Mehrtash, Alireza; Kapur, Tina; Yang, Ehwa; Moon, Jung Won; Bernardino, Gabriel; Delgado-Gonzalo, Ricard; Farhangi, M. Mehdi; Amini, Amir A.; Ni, Renkun; Feng, Xue; Bagari, Aditya; Vaidhya, Kiran; Veasey, Benjamin; Safta, Wiem; Frigui, Hichem; Enguehard, Joseph; Gholipour, Ali; Castillo, Laura Silvana; Daza, Laura; Pinsky, Paul F.; Kalpathy–Cramer, Jayashree; Farahani, Keyvan

doi:10.1109/tmi.2021.3097665

Cited by 24 publications

(4 citation statements)

References 78 publications

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“…Radiology: CXR [17][18][19][20][21] CXR [17][18][19][20][21] LDCT [22][23][24][25][26][27][28][29][30][31][32][33] Novel tools: Genomics [34] Genomics [34] Proteomics [35,36] Exhaled breath [37][38][39] Risk prediction: Radiomics [40][41][42][43][44][45][46] WSI [47][48][49][50][51][52][53] WSI [47][48][49][50][51][52][53] Genomics [50,…”

Section: Screening Diagnosis Treatmentmentioning

confidence: 99%

“…The technology advanced rapidly in the ISBI 2018 Lung Nodule Malignancy Prediction Challenge, and 11 participants completed the challenge with an AUC between 0.70-0.91. The top five participants used deep learning models with AUC between 0.87-0.91 without significant differences from each other [31]. The accuracy was 93% with a sensitivity of 82% and precision of 84% based on the weighted voting method of the autoencoder, ResNet, and handicraft features [32], and 96% with deep convolutional network learning (DCN) [33].…”

Section: Chest Ctmentioning

confidence: 99%

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Application of Artificial Intelligence in Lung Cancer

Chiu

Chao

Chen

2022

Cancers

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Lung cancer is the leading cause of malignancy-related mortality worldwide due to its heterogeneous features and diagnosis at a late stage. Artificial intelligence (AI) is good at handling a large volume of computational and repeated labor work and is suitable for assisting doctors in analyzing image-dominant diseases like lung cancer. Scientists have shown long-standing efforts to apply AI in lung cancer screening via CXR and chest CT since the 1960s. Several grand challenges were held to find the best AI model. Currently, the FDA have approved several AI programs in CXR and chest CT reading, which enables AI systems to take part in lung cancer detection. Following the success of AI application in the radiology field, AI was applied to digitalized whole slide imaging (WSI) annotation. Integrating with more information, like demographics and clinical data, the AI systems could play a role in decision-making by classifying EGFR mutations and PD-L1 expression. AI systems also help clinicians to estimate the patient’s prognosis by predicting drug response, the tumor recurrence rate after surgery, radiotherapy response, and side effects. Though there are still some obstacles, deploying AI systems in the clinical workflow is vital for the foreseeable future.

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Section: Screening Diagnosis Treatmentmentioning

confidence: 99%

Section: Chest Ctmentioning

confidence: 99%

Application of Artificial Intelligence in Lung Cancer

Chiu

Chao

Chen

2022

Cancers

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“…In addition, compared with a single point in time image, longitudinal image data can better dig out the correlation characteristics between nodules at different time points and analyze the trend of changes in nodules. Therefore, the analysis of longitudinal images is a more effective way to study whether the nodules are cancerous 25 .…”

Section: Introductionmentioning

confidence: 99%

Predicting Lung Cancer Development using a Multimodal Longitudinal Feature Analysis Framework

Xiao,

Han,

Qiang

et al. 2024

Preprint

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The prediction of pulmonary nodules being canceration is of vital importance to the diagnosis and treatment of early lungadenocarcinoma. However, most traditional research paradigms only focus on image data at a single point in time, whichmakes it easy to ignore the correlation of lung computer tomography (CT) at multiple points in time. Based on the the correlationbetween longitudinal images and medical characteristics of patients with pulmonary nodules, a multimodal feature analysisframework is proposed in this paper.The multimodal analysis framework systematically evaluates the risk of cancer progressionin patients by incorporating diverse sources of information, including radiological features, depth features, and relevant riskfactors within lung cancer imaging. The experiment result show that the accuracy of nodule canceration prediction reached89.15%, which proved the effectiveness of the method proposed in this study. This comprehensive framework holds greatpromise in contributing to more precise diagnostics and personalized treatment strategies in the context of lung cancer.

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“…Accurately diagnosing malignant nodules is of critical importance in improving the prognosis of lung cancer through the use of intelligent data analytics in healthcare, which significantly increases survival rates [1][2][3][4]. Access to accurate, complete, and timely relevant lung cancer data is essential for investigating the causes of lung cancer, detecting cancer early, evaluating the efficacy of treatment, identifying the causes of treatment failure, and conducting lung cancer control programs.…”

Section: Introductionmentioning

confidence: 99%

An Innovative Method for Lung Cancer Identification Using Machine Learning Algorithms

Kavita Singh

2023

IJRITCC

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Biological community and the healthcare sector have greatly benefited from technological advancements in biomedical imaging. These advantages include early cancer identification and categorization, prognostication of patients' clinical outcomes following cancer surgery, and prognostication of survival for various cancer types. Medical professionals must spend a lot of time and effort gathering, analyzing, and evaluating enormous amounts of wellness data, such as scan results. Although radiologists spend a lot of time carefully reviewing several scans, tiny nodule diagnosis is incredibly prone to inaccuracy. Low dose computed tomography (LDCT) scans are used to categorize benign (Noncancerous) and malignant (Cancerous) nodules in order to study the issue of lung cancer (LC) diagnosis. Machine learning (ML), Deep learning (DL), and Artificial intelligence (AI) applications aid in the rapid identification of a number of infectious and malignant diseases, including lung cancer, using cutting-edge convolutional neural network (CNN) and Deep CNN architectures, we propose three unique detection models in this study: SEQUENTIAL 1 (Model-1), SEQUENTIAL 2 (Model-2), and transfer learning model Visual Geometry Group, VGG 16 (Model-3). The best accuracy model and methodology that are proposedas an effective and non-invasive diagnostic tool, outperforms other models trained with similar labels using lung CT scans to identify malignant nodules. Using a standard LIDC-IDRI data set that is freely available, the deep learning models are verified. The results of the experiment show a decrease in false positives while an increase in accuracy.

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Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge

Cited by 24 publications

References 78 publications

Application of Artificial Intelligence in Lung Cancer

Application of Artificial Intelligence in Lung Cancer

Predicting Lung Cancer Development using a Multimodal Longitudinal Feature Analysis Framework

An Innovative Method for Lung Cancer Identification Using Machine Learning Algorithms

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