Detection of Incidental Esophageal Cancers on Chest CT by Deep Learning

Sui, He; Ma, Ruhang; Lin, Lizhu; Gao, Yaozong; Zhang, Wenhai; Mo, Zhanhao

doi:10.3389/fonc.2021.700210

Cited by 16 publications

(12 citation statements)

References 33 publications

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“…However, our experimental results suggested that the DL can also assist radiologists in improving the workups of esophageal cancers by reducing FPs and FNs in non-contrast chest CT scans. In particular, the DL model may improve the performance of junior radiologists to the senior level, which resonates with the conclusion of the studies ( 33 , 35 ). Accordingly, this may shed light on the early detection of esophageal cancers, especially in the context of health check-up examinations.…”

Section: Discussionsupporting

confidence: 72%

Esophageal cancer detection via non-contrast CT and deep learning

Lin,

Guo,

Huang

et al. 2024

Front. Med.

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BackgroundEsophageal cancer is the seventh most frequently diagnosed cancer with a high mortality rate and the sixth leading cause of cancer deaths in the world. Early detection of esophageal cancer is very vital for the patients. Traditionally, contrast computed tomography (CT) was used to detect esophageal carcinomas, but with the development of deep learning (DL) technology, it may now be possible for non-contrast CT to detect esophageal carcinomas. In this study, we aimed to establish a DL-based diagnostic system to stage esophageal cancer from non-contrast chest CT images.MethodsIn this retrospective dual-center study, we included 397 primary esophageal cancer patients with pathologically confirmed non-contrast chest CT images, as well as 250 healthy individuals without esophageal tumors, confirmed through endoscopic examination. The images of these participants were treated as the training data. Additionally, images from 100 esophageal cancer patients and 100 healthy individuals were enrolled for model validation. The esophagus segmentation was performed using the no-new-Net (nnU-Net) model; based on the segmentation result and feature extraction, a decision tree was employed to classify whether cancer is present or not. We compared the diagnostic efficacy of the DL-based method with the performance of radiologists with various levels of experience. Meanwhile, a diagnostic performance comparison of radiologists with and without the aid of the DL-based method was also conducted.ResultsIn this study, the DL-based method demonstrated a high level of diagnostic efficacy in the detection of esophageal cancer, with a performance of AUC of 0.890, sensitivity of 0.900, specificity of 0.880, accuracy of 0.882, and F-score of 0.891. Furthermore, the incorporation of the DL-based method resulted in a significant improvement of the AUC values w.r.t. of three radiologists from 0.855/0.820/0.930 to 0.910/0.955/0.965 (p = 0.0004/<0.0001/0.0068, with DeLong’s test).ConclusionThe DL-based method shows a satisfactory performance of sensitivity and specificity for detecting esophageal cancers from non-contrast chest CT images. With the aid of the DL-based method, radiologists can attain better diagnostic workup for esophageal cancer and minimize the chance of missing esophageal cancers in reading the CT scans acquired for health check-up purposes.

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Section: Discussionsupporting

confidence: 72%

Esophageal cancer detection via non-contrast CT and deep learning

Lin,

Guo,

Huang

et al. 2024

Front. Med.

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“…Therefore, a more complex survival model is required to better fit survival data to the nonlinear risk function. Neural networks are widely used in the diagnosis of endoscopic and radiological imaging of EC ( 19 – 22 ), evaluation of the depth of tumor invasion and lymph node metastasis ( 23 , 24 ), treatment response prediction ( 25 ), and in other fields. To date, there have been few studies on the application of deep learning neural networks to survival prediction in patients with EC.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a deep learning model to predict survival of patients with esophageal cancer

et al. 2022

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ObjectiveTo compare the performance of a deep learning survival network with the tumor, node, and metastasis (TNM) staging system in survival prediction and test the reliability of individual treatment recommendations provided by the network.MethodsIn this population-based cohort study, we developed and validated a deep learning survival model using consecutive cases of newly diagnosed stage I to IV esophageal cancer between January 2004 and December 2015 in a Surveillance, Epidemiology, and End Results (SEER) database. The model was externally validated in an independent cohort from Fujian Provincial Hospital. The C statistic was used to compare the performance of the deep learning survival model and TNM staging system. Two other deep learning risk prediction models were trained for treatment recommendations. A Kaplan–Meier survival curve was used to compare survival between the population that followed the recommended therapy and those who did not.ResultsA total of 9069 patients were included in this study. The deep learning network showed more promising results in predicting esophageal cancer-specific survival than the TNM stage in the internal test dataset (C-index=0.753 vs. 0.638) and external validation dataset (C-index=0.687 vs. 0.643). The population who received the recommended treatments had superior survival compared to those who did not, based on the internal test dataset (hazard ratio, 0.753; 95% CI, 0.556-0.987; P=0.042) and the external validation dataset (hazard ratio, 0.633; 95% CI, 0.459-0.834; P=0.0003).ConclusionDeep learning neural networks have potential advantages over traditional linear models in prognostic assessment and treatment recommendations. This novel analytical approach may provide reliable information on individual survival and treatment recommendations for patients with esophageal cancer.

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“…It was also observed in the survey that the highest average accuracy (98%) among the types of modalities used is related to the algorithms that used a combination of WLI and NBI (18, 23,34,35). Among the algorithms that used only one type of modality, the average accuracy was 96.5%, which was related to NBI (40), and then 96.3% and 84.2% were related to WLI (11, 25-28, 30, 31, 51) and CT (38,39), respectively. In one case, an average accuracy of 98% was achieved using Optical coherence tomography (OCT) images (Fig.…”

Section: Ec Image Segmentationmentioning

confidence: 98%

“…Deep transfer learning algorithm They achieved the highest level of accuracy at 99.7% with the Deep transfer learning algorithm, while the lowest accuracy rate was reported by Sui et al at 65% with the V-Net algorithm(11,29,39,42,43,49).…”

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confidence: 93%

Machine Learning Applications for Early Detection of Esophageal Cancer: A Systematic Review

Hosseini

Asadi

Emami

et al. 2023

Preprint

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Introduction: Esophageal cancer (EC) is a significant global health problem, with an estimated 7th highest incidence and 6th highest mortality rate. Timely diagnosis and treatment are critical for improving patients' outcomes, as over 40% of patients with EC are diagnosed after metastasis. Recent advances in machine learning (ML) techniques, particularly in computer vision, have demonstrated promising applications in medical image processing, assisting clinicians in making more accurate and faster diagnostic decisions. Given the significance of early detection of EC, this systematic review aims to summarize and discuss the current state of research on ML-based methods for the early detection of EC. Methods: We conducted a comprehensive systematic search of five databases (PubMed, Scopus, Web of Science, Wiley, and IEEE) using search terms such as "ML", "Deep Learning (DL(", "Neural Networks (NN)", "Esophagus”, “EC" and “Early Detection”. After applying inclusion and exclusion criteria, 31 articles were retained for full review. Results: The results of this review highlight the potential of ML-based methods in the early detection of EC. The average accuracy of the reviewed methods in the analysis of endoscopic and computed tomography (CT (images of the esophagus was over 89%, indicating a high impact on early detection of EC. Additionally, the highest percentage of clinical images used in the early detection of EC with the use of ML was related to white light imaging (WLI) images. Among all ML techniques, methods based on convolutional neural networks (CNN) achieved higher accuracy and sensitivity in the early detection of EC compared to other methods. Conclusion: Our findings suggest that ML methods may improve accuracy in the early detection of EC, potentially supporting radiologists, endoscopists, and pathologists in diagnosis and treatment planning. However, the current literature is limited, and more studies are needed to investigate the clinical applications of these methods in early detection of EC. Furthermore, many studies suffer from class imbalance and biases, highlighting the need for validation of detection algorithms across organizations in longitudinal studies.

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Detection of Incidental Esophageal Cancers on Chest CT by Deep Learning

Cited by 16 publications

References 33 publications

Esophageal cancer detection via non-contrast CT and deep learning

Esophageal cancer detection via non-contrast CT and deep learning

Development and validation of a deep learning model to predict survival of patients with esophageal cancer

Machine Learning Applications for Early Detection of Esophageal Cancer: A Systematic Review

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