Machine learning to predict lung nodule biopsy method using CT image features: A pilot study
Introduction
Following the National Lung Screening Trial (NLST), which demonstrated the mortality benefit of screening computed tomography (CT) among patients at high risk for lung cancer, management of incidental lung nodules is poised to become a rapidly growing public health problem (Aberle et al., 2011; Marcus et al., 2016; National Lung Screening Trial Research Team, 2011). Biopsy and pathologic analysis of suspicious nodules is frequently necessary to ensure accurate diagnosis and appropriate intervention. Biopsy techniques vary as do the specialists that perform them and the ways lung nodule patients are referred and triaged. The largest dichotomy is between minimally invasive biopsy (MIB) and surgical biopsy (SB). SB is a more definitive but risky diagnostic modality that is characterized by surgical resection followed by histopathological analysis of the resected tissue (Reck et al., 2014; Rivera et al., 2013). Cases of unsuccessful MIB preceding a SB can potentially delay definitive case, adversely impact prognoses, and incur avoidable healthcare expenditures. An automated model that objectively predicts the optimal biopsy method for a given lung nodule could improve patient referral patterns, thereby saving time, improve outcomes, and reducing healthcare costs.
MIB is often the initial diagnostic modality of choice despite comparatively lower diagnostic yield rates in view of lower cost, no need for hospitalization, and lower risks of procedural complications such as bleeding, infection, pneumothorax, and death (Belanger and Akulian, 2017; Rivera et al., 2013). This becomes all the more relevant considering that 96% of lung nodules detected in the NLST were benign (DR Aberle et al., 2011; Marcus et al., 2016; National Lung Screening Trial Research Team, 2011). MIB can be performed percutaneously under image guidance (Conces et al., 1987), or via fiber-optic bronchoscopy (Herth, 2011).
Even though clinicians carefully review both radiologic and patient characteristics to determine the feasibility of MIB, a significant proportion of biopsies still fail to lead to a diagnosis (Belanger and Akulian, 2017). Plausible reasons include the inability to reach the target site with biopsy instruments and the inability to collect a sufficient amount of tissue for pathological analyses. This can often lead to either a second MIB procedure or a move to surgical resection (SB). In addition to accruing healthcare costs, this can lead to loss of valuable time before the institution of potentially life-saving therapy (Taleb et al., 2017).
The ability to prospectively predict biopsy success would be useful, as it would help identify when a MIB procedure would likely be non-diagnostic and enable patients and providers to proceed straight to SB. Previous research has demonstrated that two-dimensional size on CT imaging can help predict biopsy success. Two studies showed that manual identification of a bronchus sign on a thoracic CT—the presence of a lung bronchus in close proximity directly leading to a pulmonary lesion—can predict the success of a bronchoscopic biopsy (Evison et al., 2014; Gaeta et al., 1991). No prior studies, to our knowledge, have used machine learning of radiomic features to predict the biopsy method.
Considerable work has been done creating machine learning methods to assist doctors perform percutaneous biopsies. All these tools show the position of biopsy instruments relative to the suspicious nodule and thereby improve the accuracy of needle placement. Yaniv et al. used a registration algorithm to directly map a biopsy point drawn on a diagnostic CT to perioperative CT scan (Yaniv et al., 2010). He et al. segmented the nodule and pulmonary vessels from the diagnostic radiologic imaging modality and overlaid them onto the intra-procedural interventional radiology CT using electromagnetic guidance (He et al., 2010). Hagmann et al. generated a type of virtual reality display on operating CT images which can be paired with haptic feedback to further improve biopsy needle placement (Hagmann et al., 2004). However this research solves a problem (percutaneous biopsy needle placement) inherently different from the task considered here: predicting whether a minimally invasive biopsy (percutaneous or bronchoscopic) or a surgical biopsy should be performed on a patient, based on CT scans.
In this study, we have developed a novel algorithm to objectively predict the optimal biopsy method for a given lung nodule through an automated prediction model incorporating computational and semantic image features. We demonstrate proof of concept utilizing the largest publicly available dataset with biopsy information available. We theorized that such a model could save time and healthcare costs by facilitating referral and triage patterns. To facilitate additional research in this field, all our image feature extraction methods are available online in an open-source Python Package on BitBucket (https://bitbucket.org/connorbrinton/lcat_lung_biopsy).
Section snippets
Data source
We harnessed public domain imaging data from the Lung Image Database Consortium image collection (LIDC-IDRI), which is available through the Cancer Imaging Archive (Armato et al., 2015; Clark et al., 2013). LIDC-IDRI contains thoracic computed tomography (CT) images from 1018 patients with lung nodules in DICOM format. For every patient, four expert radiologists identified any and all lung nodules. The seed pixels corresponding to each identified nodule, and additional semantic notes reported
Tracheal distance computation
An expert pulmonologist (MS) visually inspected the tracheal and bronchiole mappings produced by our algorithm and found it performed well on all anatomically normal CT scans tested. An example of each stage of the algorithm for an example patient from our dataset is given in Fig. 2. In the case of one patient, the model was unable to identify the trachea. On manual review of images, the trachea was noted to communicate with the body surface near the thoracic inlet. In the medical expertise of
Discussion
In this study, we built a model incorporating both computational and semantic features from the CT scans of 30 patients to predict the optimal biopsy modality for a given lung nodule (i.e. MIB versus SB). We calculated CT image features: 3D volume, tracheal distance, and distance to outer body. We extracted semantic features from radiologists’ notes: sphericity, lobulation, spiculation, calcification, internal structure, and texture. These features were used to train both logistic regression
Conflicts of interest
None of the authors of this manuscript have any financial or personal relationships with other people or organizations that could inappropriately influence and/or bias this work.
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Authors contributed equally to this research.