In recent years, there has been a marked increase in the use of low-dose computed tomography (CT) screening for lung cancers. This, together with the subsequent early diagnosis and treatment of patients, has dramatically increased radiologists’ workload. According to the white paper on medical imaging using artificial intelligence (AI) in China that was issued in 2019, the number of chest CT examinations in China has been increasing at a rate of 30% every year, whereas the number of radiologists has only been increasing at a rate of 4% (1). Therefore, there is a strong demand for the development of accurate computer-aided detection (CAD) tools in this context. At present, CAD products based on AI can detect pulmonary nodules with low false-positive rates (2,3) and provide quantitative information on nodules, such as size, volume, consistency, location, and probability of malignancy, and even compare images from multiple time points to automatically calculate the volume doubling time (1,4-6). AI products are often used as a first-line or second-line radiography reader (7-9). This greatly improves the efficiency of radiologists and the detection rate of pulmonary nodules and reduces the number of missed nodules and misdiagnoses (3,10-13).
AI-driven computer-aided diagnosis (CADx) is a non-invasive, objective solution for assisting radiologists in diagnosing lung nodules. Existing CADx methods fall into two categories: classification models based on hand-crafted features (14) and deep neural networks with automatic feature extraction (15-17). Approaches in the first category typically measure radiological traits, such as nodule size, location, shape, and texture, and adopt a classifier to determine malignancy status. In the second category, models based on deep neural networks can automatically learn features for diagnosis from lung CT images. Both two-dimensional (2D) convolutional neural networks (CNNs) and three-dimensional (3D) CNNs are commonly used deep learning models in AI-driven CADx systems. While 2D CNNs have lower computational complexity, 3D CNNs can better analyze pulmonary nodules’ spatial structure (4). Indeed, CADx systems have shown promising prediction accuracy for lung nodules’ risk stratification (4,5).
The availability of AI-driven lung nodule detection products has gradually increased and is now widely used in the clinical frontline to reduce radiologists’ workload and improve diagnostic efficiency. On July 3rd, 2020, a deep learning-based AI product developed by Beijing Infervision Technology Co., Ltd. (China) became the first U.S. FDA-approved automated lung nodule detection product. Understanding the advantages and disadvantages of this AI product is crucial to its clinical applicability. The use of AI products in the clinic has primarily focused on automatic pulmonary nodule detection (3,5,11) and classification (2,4,5,10) to identify primary lung tumors (4,5). Furthermore, AI products have mainly been trained using screening cohorts or public databases, such as the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database. However, their performance and accuracy in risk prediction of nodules or mass lesions with different underlying pathology, especially metastases and benign lesions, has not been examined in clinical practice.
This investigation used real-world data to evaluate risk prediction accuracy for a wide range of abnormalities in a clinical cohort using an AI-driven lung nodule algorithm provided by Beijing Infervision Technology Co., Ltd. This AI model was based on a 3D CNN with DenseNet architecture as a backbone. The accuracy of risk prediction by the algorithm was compared to that of two resident doctors in our department. This data will help to improve our understanding of the current AI products available and their use in the clinic.
The ethics committee approved this retrospective study of the Fifth Affiliated Hospital of Sun Yat-sen University. Patient informed consent was waived as the study had minimal risk and would not adversely affect the patient’s rights or welfare. Two datasets were compiled using an in-house-developed Radiology Information System/Picture Archiving and Communication System search engine. Patients who underwent lung resection in our institution between September 2015 and November 2018 were identified. The following inclusion criteria were applied: (I) patients with surgically resected and histologically proven lung nodules or masses; and (II) surgery within one month from the last CT scan. The following exclusion criteria were applied: (I) no available presurgical non-contrast thin-section (≤ 2 mm) chest CT scan; (II) diffuse disease; (III) CT images with severe breathing or other motion artifacts; (IV) presurgical chemotherapy or radiotherapy treatment; (V) lesions not detected by the AI algorithm; and (VI) lesions with detection errors when using the AI algorithm (for example, one large lesion was mis-detected as multiple lesions). Moreover, this study utilized the Faster RCNN model to detect lesions. The study workflow is displayed in Figure 1.
The management protocols were as follows: (I) the I-ELCAP protocol was used for nodule screening and detection, and the Fleischner society and the National Comprehensive Cancer Network (NCCN) guidelines were applied for incidental nodules; (II) a multidisciplinary team (MDT) discussion on growing subsolid nodules was conducted during follow-up; (III) the patient’s wishes were accommodated where possible, especially those who are nervous and anxious. Subsolid nodules <10 mm were resected for the following reasons: (I) the smaller accompanying lesions were removed together with, the larger lesions, and this accounted for most cases; and (II) the patients strongly requested surgery.
CT image acquisition
Non-contrast CT scans were acquired in a single-breath hold during full inspiration using three CT scanners, Somatom Sensation 16 (S16), Definition Flash (DF) (Siemens Medical Solutions, Forchheim, Germany), and Uct760 (United Imaging; Shanghai, China). The scanning range was from the lung apex to the base. All images were obtained with a standard dose scanning protocol and reconstructed at 1.0-mm or 2.0-mm slice-thicknesses with 0.7-mm to 1-mm increments, 512×512 matrix, and a moderate or high reconstruction kernel (b50f, b60f, b70f for S16 and DF; sharp for Uct760). The lung window setting used a window level of -600 Hounsfield units (HU) and a window width of 1,500 HU. The mediastinal window setting used a window level of 40 HU and a window width of 400 Hu.
Evaluation by a computer-aided diagnostic system
The AI-driven commercial CAD product (InferRead CT Lung Research, ICLR), which utilizes a detection and risk prediction model, was provided by Beijing Infervision Technology Co., Ltd. (Beijing, China).
To train the detection model, a total of 11,205 CT scans with 3,527,048 image slices were collected from multiple hospitals in China. The scanning devices included GE MEDICAL SYSTEMS, Siemens, Philips, and TOSHIBA. Two radiologists (radiologists A and B) with approximately 10 years’ experience independently reviewed the CT scans and the corresponding radiology reports to label the locations and specific attributes of the pulmonary nodules or mass lesions in the image slices. In the case of disagreements, the annotation was checked by a third radiologist (radiologist C) with 15 years’ experience, and a consensus was reached by discussion. This study utilized the Faster RCNN model (21) to detect the nodules. The AI model utilized in the current study was a Region-based CNN for object detection, which consisted of two modules. The first was the regional proposal network (RPN), a fully convolutional network for generating object proposals. The second was the Fast R-CNN detector which aimed to refine the proposals generated by the first module. Compared to the previous two versions (i.e., R-CNN and Fast R-CNN), the Faster R-CNN had better performance and faster processing speed, with the less computational burden (21,22).
For training of the risk prediction model, training data including 5,000 benign and 3,604 malignant nodules or mass lesions were prepared. Among the malignant training samples, adenocarcinomas accounted for 93.8% of all lesions. Squamous cell carcinomas, other primary malignant tumors, and metastases accounted for 4.2%, 1.0%, and 1.0% of all lesions, respectively. Biopsy or surgery was performed in all malignant cases to determine the characteristics of the lesion. Among benign samples, approximately 21% were highly suspected of being malignant and were biopsy- or surgery-proven. The remaining 79% were confirmed through long-term follow-up and judged as benign by experienced radiologists. These lesions had obvious benign features, and most were small nodules less than 20 mm. This study utilized a CNN based on the ResNet-34 framework as the risk prediction model. The fully connected layer’s output was fed into the sigmoid layer to acquire the risk probability of lung nodule malignancy. A detailed description relating to the risk prediction model can be found in our previous study (23).
When radiologists used the CAD product, the imaging data were transmitted to an ICLR workstation post-anonymization. After processing by the workstation, the probability of malignancy was recorded. The probability was divided into three levels, namely, low-risk (<50%), moderate-risk (50–70%), and high-risk (>70%). The division criteria were determined based on the training model results derived from the larger data set and internal verification.
Image interpretation by radiologists
Two thoracic radiologists, each with 12 and 17 years of experience, were blinded to the ICLR system’s results. Each independently interpreted the CT images using an institutional digital database system (PACS, V126.96.36.199720, Neusoft, Shenyang, China). Any disagreements were resolved by a third radiologist with 30 years of experience.
Consistency, size, and distribution were recorded for each lesion. If the lung parenchyma within the entire nodule was obscured, it was classified as a SN, even if there was external or internal cystic airspace or internal cavitation. If the underlying parenchyma was visible except for branching blood vessels within the nodule, it was classified as a nonsolid nodule (NS). If the nodule had nonsolid components and solid components, it was classified as a part-solid nodule (PS) (24). The overall size was determined based on the maximum diameter on 3D images. Lesions were divided into four subgroups (size10mm, size20mm, size30mm, and size>30mm) based on size, representing lesions of ≤10, 10–20, 20–30, and >30 mm, respectively. Lesions in the lower lobes were regarded as lower lobe lesions, and lesions in both the upper and right middle lobes were classified as upper lobe lesions.
To compare the deep learning system with human performance, two radiology resident doctors [one with 3 years of chest radiology training (R1) and one with 3 years of general radiology training (R2)], were blinded to the results of the ICLR system and pathology. Both independently reviewed and graded each lesion. Lesions were divided into three categories, namely, malignant (high-risk, >70%), suspicious for malignancy (moderate-risk, 50–70%), and benign (low-risk, <50%). The doctors were given access to associated patient demographics, clinical history, and prior CT images.
Four histological types, including adenocarcinoma, other malignancy, metastatic, and benign, were evaluated in our study. The histopathologic classification of adenocarcinomas was based on the IASLC/American Thoracic Society/ERS classification (25) and included preinvasive lesions [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS)], minimally invasive adenocarcinomas (MIA), and invasive adenocarcinomas (IA). Another malignancy refers to the 2015 World Health Organization classification of malignant lung tumors, except for adenocarcinomas and metastatic tumors (26).
Categorical variables were summarized as percentages. Continuous variables were summarized as means ± standard deviations. Differences were evaluated with Chi-square tests for categorical variables. Differences between groups were tested with Bonferroni correction. To calculate malignancy risk prediction accuracy, a true result was defined as a benign lesion that was predicted as low-risk or a malignant lesion that was predicted as moderate- or high-risk. Otherwise, it was defined as a false result. Cochran’s Q test was used to compare the malignancy risk prediction performance between ICLR, R1, and R2. A P value <0.05 was considered statistically significant. Statistical analysis was performed using IBM SPSS Statistics for Windows, Version 25.0 (IBM Corp., Armonk, NY, USA).
The detection rate of nodules
A total of 485 patients with 568 lesions met the inclusion criteria. Among them, 29 lesions in 29 patients without available presurgical non-contrast thin-section chest CT, 14 lesions in 14 patients with diffuse disease, 20 non-detectable lesions in 14 patients, and 19 lesions in 14 patients with detection errors in the AI algorithm were excluded. The non-detectable lesions included 6 endobronchial lesions, 3 perihilar lesions, 7 adhesive lesions (lesions were attached to the mediastinum in 3 cases, attached to the pleural effusion in 2 cases, attached to the inflammatory exudate in 2 cases), a large mass attached to the costal pleura in 1 case, 2 cases with patchy appearance, and 1 case with a 3-mm NS nodule. Detailed information regarding the 20 non-detectable lesions and 19 lesions with detection errors are shown in Tables S1 and S2, respectively. Examples are shown in Figures S1 and S2.
The remaining 486 lesions in 414 patients (including 218 males and 196 females) were used for malignancy risk evaluation using the ICLR. The detection rate was 92.6% (486/525), suggesting that the Faster RCNN utilized in this study had good lung nodule detection performance. There were 342, 52, and 10 patients with 1, 2, and 3 resected lesions, respectively. The average age was 58.4±11.0 years (range, 28 to 81 years). The average nodule size was 20.7±13.7 mm (range, 3.0–78.7 mm), and there were 90 NS, 75 PS, and 321 SN lesions. A total of 320 adenocarcinomas, 40 other malignancies, 55 metastases, and 71 benign lesions were detected, with mean sizes of 20.1±12.1 mm (3.0–78.7 mm), 37.7±16.6 mm (11.0–77.6 mm), 15.3±10.5 mm (3.0–69.0 mm), and 17.3±13.7 mm (3.0–73.0 mm), respectively. Characteristics of the patient cohort are shown in Table 1.
Accuracy of malignancy-risk prediction by the ICLR
Out of the 486 lesions, 79 were classified by the ICLR as low-risk, 40 were moderate-risk, and 367 were high-risk. The malignancy risk prediction categories obtained from the ICLR and the corresponding pathological classification (benign and malignant) for each category are shown in Table 2. The overall accuracy of risk prediction was 81.1% (394/486). The accuracy of risk prediction was significantly different based on size, consistency, and pathology, but no significant difference was observed among different scanners, slice thicknesses, and lobe distributions. Factors affecting the accuracy of malignancy-risk prediction by the ICLR are summarized in Table 3.
The accuracy of malignancy-risk prediction in the size10mm, size20mm, size30mm, and size>30mm subgroups was 74.5% (82/110), 77.5% (145/187), 87.8% (86/98), and 89.0% (81/91), respectively. The risk prediction accuracy for lesions larger than 20 mm was slightly higher than that for lesions smaller than 20 mm. However, there was no significant difference in the accuracy of prediction in any of the size subgroups.
The accuracy of malignancy-risk prediction for NS, PS, and SN lesions was 94.4% (85/90), 94.7% (71/75), and 74.1% (238/321), respectively. The risk prediction accuracy for NS and PS lesions was significantly higher than that for SN lesions (NS vs. SN, P<0.05; PS vs. SN, P<0.05).
The accuracy of malignancy-risk prediction for adenocarcinomas, other malignancies, metastases, and benign lesions was 93.4% (299/320), 95.0% (38/40), 50.9% (28/55), and 40.8% (29/71), respectively. The risk prediction accuracy for primary lung cancer was significantly higher than that for metastases and benign lesions (all P<0.05).
Accuracy of risk prediction of the ICLR in lesions with different histological types and sizes
The accuracy of risk prediction for adenocarcinomas in the size10mm, size20mm, size30mm, and size>30mm subgroups was 87% (59/68), 91% (116/127), 99% (72/73), and 100% (52/52), respectively. For other malignancies, the accuracy of risk prediction for the different size subgroups was 0% (0/0), 88% (7/8), 100% (6/6), and 96% (25/26), respectively. For metastases, the accuracy of risk prediction for the different size subgroups was 20% (4/17), 60% (15/27), 70% (5/7), and 100% (4/4), respectively, and for benign lesions, it was 76% (19/25), 28% (7/25), 25% (3/12), and 0% (0/9), respectively.
For adenocarcinomas and metastases, the accuracy of risk prediction increased significantly with an increase in lesion size (P=0.005 and P=0.016, respectively). For adenocarcinomas, the accuracy of risk prediction was significantly higher in the size30mm and size>30mm subgroups than the size10mm subgroup (both P<0.05). For benign lesions, the accuracy of risk prediction decreased significantly with an increase in lesion size (P<0.001). For other malignant lesions, there was no significant difference among the size subgroups (P=0.513) (Figure 2).
Accuracy of risk prediction of the ICLR in lesions with different histological types and consistencies
The respective accuracy of risk prediction in NS, PS, and SN lesions was 97% (84/87), 100% (71/71), and 89% (144/162) for adenocarcinomas; and 33.3% (1/3), 0% (0/4), and 43.8% (28/64) for benign lesions. For adenocarcinoma, the accuracy of risk prediction was slightly lower in SN lesions than in PS and NS lesions. However, the difference was only significant between SN and PS lesions (P<0.05). No significant difference based on consistency was observed in benign lesions (P=0.277). Only SN lesions were observed among other malignancies and metastases.
A comparison of the accuracy of risk prediction between the ICLR and the radiologists
The overall accuracy of risk prediction for R1 and R2 was 93.6% (455/486) and 87.4% (425/486), respectively, which were significantly higher than that for the ICLR (P<0.001 and P=0.001, respectively). R1 performed better than R2 (P=0.001). Representative examples of the performance of the ICLR and the radiologists are shown in Figure 3. A comparison of the performance between the ICLR and the two radiologists based on size, consistency, and pathology is shown in Table 4.
There were significant differences in the accuracy of malignancy risk prediction between the ICLR and the radiologists in the size10mm, size20mm, and size30mm subgroups (P=0.005, P<0.001, and P=0.017, respectively; Figure 4A). R1 significantly outperformed the ICLR in all three size subgroups (P=0.003, P<0.001, and P=0.014, respectively). R2 only outperformed ICLR in the size20mm subgroup P=0.015. There was no significant difference in accuracy between the radiologists and the ICLR in the size>30mm subgroup (P=0.062).
In terms of lesion consistency, only SN lesions showed a significant difference in the accuracy of risk prediction between the ICLR and the radiologists (Figure 4B). The performance of the ICLR was again poorer than that of the two radiologists (both P<0.001). There was no significant difference in accuracy among lesions of other consistencies (NS, P=0.459; PS, P=0.247).
There were significant differences in the radiologists’ accuracy and the ICLR in predicting the risks of adenocarcinomas, metastases, and benign lesions (P<0.001, P<0.001, and P=0.012, respectively; Figure 4C). R1 significantly outperformed the ICLR in all three histological types (P=0.001, P=0.001, and P=0.010, respectively). R2 only outperformed the ICLR in metastases (P<0.001). There was no significant difference in the accuracy of other malignancies (P=0.247). For adenocarcinomas, a significant difference was observed between the ICLR and the radiologists in assessing solid type lesions (P<0.001) but not in subsolid type lesions (P=0.078).
R1 outperformed R2 in three subgroups (size20mm, P=0.027; SN lesions, P=0.008; and adenocarcinoma, P=0.004).
The AI-driven commercial CADx product used in this study applied a 3D CNN with DenseNet architecture as a backbone to determine the malignant probability of pulmonary lesions. The experimental results showed that the AI model had promising applications in clinical practice. The model achieved high performance for detection (92.6%) and risk probability prediction (81.1% with metastasis, 84.9% without metastasis) of pulmonary nodules and masses. This AI product was accurate in the risk prediction of primary lung cancers (93.6%). The accuracy was especially high in adenocarcinomas manifesting as NS or PS nodules (98.1%, 155/158), whereas it was less accurate in solid adenocarcinomas (89%, 139/157). This study is an important supplement to prior investigations using the same software (23). In a previous report, researchers used the National Lung Screening Trial (NLST) database as the training set to build the model and the LIDC-IDRI and Infervision Multi-Center (IMC) databases for verification. They showed excellent performances for the classification of malignancy of lung nodules with the area under the curve (AUC) values of 0.91, 0.86, and 0.95 for receiver operating characteristic curves on the NLST dataset, LIDC-IDRI dataset, and IMC dataset, respectively. The accuracy for classification of benign and malignant lesions using deep learning-based CADx systems reported in the LIDC-IDRI was between 86.84% and 92.3%, with an average AUC of 0.956 (4).
However, this AI product’s performance for risk prediction of metastases and benign lesions was poor, with accuracy rates of 50.9% and 40.8%, respectively. The major reason for this poor performance is likely due to bias in the training dataset. With the increase in the availability of CT equipment and lung cancer screening in recent years, the detection of asymptomatic lung cancer has increased. Adenocarcinoma accounts for the vast majority of surgically resected cases, and thus, adenocarcinomas are the main component of the training set. As computer output always comes from input, CADx performance heavily depends on the difficulty and diversity of the training and testing datasets (27). The low accuracy may also be due to the lack of malignant features of metastases, especially when they are small, round and regular, less lobular, and less spiculated, which does not match the AI’s malignant features. Additionally, it appears that the AI has learned that large lesions have a high probability of being malignant. Our results showed that the accuracy of prediction of metastases was proportional to size, whereas accuracy was inversely proportional to size in benign lesions.
The observer study in this report showed that the resident doctor undergoing chest training (R1) had a higher diagnostic accuracy compared to the doctor undergoing general radiology training (R2), although both were more accurate than the AI, especially for metastases. The predictive power was roughly equivalent for subsolid adenocarcinomas and other malignancies. Usually, a resident’s diagnostic level is considered to be relatively low (13,28). However, we believe the residents performed well for several reasons. First, they were not required to distinguish between different histological types of adenocarcinoma (for example, AAH, AIS, MIA, and partial IA), which have similar imaging characteristics and are difficult to distinguish, even by experienced chest radiologists (29,30). This greatly reduced the difficulty of differential diagnosis. The reason for not distinguishing between histological types is that nodule consistency on CT is a more significant prognostic indicator than either pleural invasion or parenchymal invasion (angiolymphatic and/or vascular). Lung adenocarcinomas that appear as subsolid or nonsolid, have a much better prognosis than that of lung adenocarcinomas manifesting as SNs (31-33). Second, doctors had access to clinical information and prior images, which were especially beneficial for diagnosing metastases, but the AI did not have access to this information. Ardila and colleagues also demonstrated the importance of prior images. They proposed a deep learning algorithm to predict lung cancer risk and found that when prior imaging was not available, the AI model outperformed radiologists. In contrast, in cases where prior imaging was available, the model performance was on par with that of the same radiologists (34). Third, a resident doctor’s participation in research and review of related literature could strengthen their understanding of pulmonary lesions and improve their ability to diagnose lung lesions.
This study had some limitations. First, not all module-specific features were analyzed. However, these features, such as calcification, internal structure, lobulation, spiculation, texture, and subtlety, are important features for the differential diagnosis of benign and malignant pulmonary nodules. Second, the sample size was unbalanced, with only a small number of non-adenocarcinoma cases. The numbers of the different histological types of adenocarcinomas were also unbalanced. Third, the time required for diagnosis was not assessed and should be included in future studies. Fourth, other currently recognized nodule classification systems, such as LungRADS, were not analyzed, and this may have limited the clinical value of the report. Fifth, the device types and scanning techniques that were not in the model training set will have a certain impact on the model’s performance. The generalization of AI models can be improved by including as many types of scanners as possible in the training set. For AI products to be more useful in clinical practice, the training sample size will need to be expanded to optimize the model, and class imbalance issues need to be resolved to make the model more robust. Furthermore, since the best-known CADx schemes distinguish between benign and malignant nodules based on volume doubling time (35,36), embedding clinical information and volume change information in the algorithm in future investigations will be beneficial.
AI malignancy risk prediction for lung nodules and masses with different pathological types is particularly important and useful but complicated and challenging. The ICLR (Infervision, China) had very high accuracy in primary lung cancer malignancy prediction but poor accuracy in predicting metastases and benign lesions. Further efforts are warranted to augment the number of metastatic and benign lesions in the training dataset to improve the AI products’ performance.
We thank Buddy Zhou and David Yankelevitz for language editing.
Funding: Guangdong Ministry of Education Industry-University-Research Project (2011A090200057).
Conflicts of Interest: All authors have completed the International Journal of Medical Editors (ICMJE) uniform disclosure form (available at http://dx.doi.org/10.21037/qims-20-1314). The authors have no conflicts of interest to declare.
Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). This study was approved by the ethics committee of the Fifth Affiliated Hospital of Sun Yat-sen University. Informed consent was waived due to the retrospective nature of the study.
Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.
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