Predicting Malignant Nodules from Screening CT Scans

Hawkins, Samuel; Wang, Hua; Liu, Ying; García, Alberto López; Stringfield, Olya; Krewer, Henry; Li, Qian; Cherezov, Dmitry; Gatenby, Robert A.; Balagurunathan, Yoganand; Goldgof, Dmitry B.; Schabath, Matthew B.; Hall, Lawrence O.; Gillies, Robert J.

doi:10.1016/j.jtho.2016.07.002

Cited by 238 publications

(199 citation statements)

References 24 publications

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“…In order to achieve greater computational efficiency and generalizability to other cancers, the proposed CAD system has shorter pipeline and only requires the following data during training: a dataset of CT scans with true nodules labeled, and a dataset of CT scans with an overall malignancy label. State-of-the-art CAD systems that predict malignancy from CT scans achieve AUC of up to 0.83 [16]. However, as mentioned above, these systems take as input various labeled data that is not used in this framework.…”

Section: Methodsmentioning

confidence: 99%

“…The activation function fReLU(·) here is chosen to be a Rectified Linear Unit (ReLU) with fReLU(a) = max(0, a). This activation function has been widely used in a number of domains [24,16] and is believed to be particularly helpful in classification tasks as the sparsity it induces in the outputs helps create separation between classes during learning [17,3]. The last fully connected layer is used as input to the output layer.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

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Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN)

Alakwaa¹,

Nassef²,

Badr³

2017

ijacsa

168

100

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Abstract-This paper demonstrates a computer-aided diagnosis (CAD) system for lung cancer classification of CT scans with unmarked nodules, a dataset from the Kaggle Data Science Bowl, 2017. Thresholding was used as an initial segmentation approach to segment out lung tissue from the rest of the CT scan. Thresholding produced the next best lung segmentation. The initial approach was to directly feed the segmented CT scans into 3D CNNs for classification, but this proved to be inadequate. Instead, a modified U-Net trained on LUNA16 data (CT scans with labeled nodules) was used to first detect nodule candidates in the Kaggle CT scans. The U-Net nodule detection produced many false positives, so regions of CTs with segmented lungs where the most likely nodule candidates were located as determined by the U-Net output were fed into 3D Convolutional Neural Networks (CNNs) to ultimately classify the CT scan as positive or negative for lung cancer. The 3D CNNs produced a test set Accuracy of 86.6%. The performance of our CAD system outperforms the current CAD systems in literature which have several training and testing phases that each requires a lot of labeled data, while our CAD system has only three major phases (segmentation, nodule candidate detection, and malignancy classification), allowing more efficient training and detection and more generalizability to other cancers.

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Section: Methodsmentioning

confidence: 99%

Section: Convolutional Neural Networkmentioning

confidence: 99%

Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN)

Alakwaa¹,

Nassef²,

Badr³

2017

ijacsa

168

100

View full text Add to dashboard Cite

show abstract

“…Because early stages are often curable, this could drastically improve patient outcomes, minimize overtreatment, and even save lives. Furthermore, AI also can enhance lung cancer staging and characterization for treatment selection, as well as monitoring treatment response (Table ) …”

Section: Lung Cancer Imagingmentioning

confidence: 99%

Artificial intelligence in cancer imaging: Clinical challenges and applications

Hosny

Schabath

et al. 2019

CA A Cancer J Clinicians

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1,051

820

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Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.

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“…Radiomic biomarkers have been shown to be associated with several clinical endpoints, including survival (11,14–16), nodule malignancy (17,18), pathological response (19,20), recurrence and distant metastasis (21–23), as well as tumor gene expression patterns (11,14,21). A natural extension of this observation is that tumor phenotype should be linked to the tumor genotype.…”

Section: Introductionmentioning

confidence: 99%

Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer

et al. 2017

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Tumors are characterized by somatic mutations that drive biological processes ultimately reflected in tumor phenotype. With regard to radiographic phenotypes, generally unconnected through present understanding to the presence of specific mutations, artificial intelligence methods can automatically quantify phenotypic characters by using predefined, engineered algorithms or automatic deep-learning methods, a process also known as radiomics. Here we demonstrate how imaging phenotypes can be connected to somatic mutations through an integrated analysis of independent datasets of 763 lung adenocarcinoma patients with somatic mutation testing and engineered CT image analytics. We developed radiomic signatures capable of distinguishing between tumor genotypes in a discovery cohort (n ¼ 353) and verified them in an independent validation cohort (n ¼ 352). All radiomic signatures significantly outperformed conventional radiographic predictors (tumor volume and maximum diameter).We found a radiomic signature related to radiographic heterogeneity that successfully discriminated between EGFR þ and EGFR Our results argue that somatic mutations drive distinct radiographic phenotypes that can be predicted by radiomics. This work has implications for the use of imaging-based biomarkers in the clinic, as applied noninvasively, repeatedly, and at low cost.

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Predicting Malignant Nodules from Screening CT Scans

Cited by 238 publications

References 24 publications

Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN)

Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN)

Artificial intelligence in cancer imaging: Clinical challenges and applications

Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer

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