ORIGINAL ARTICLE

Cite this article as: Kudo Y, Shimada Y, Matsubayashi J, Kitamura Y, Makino Y, Maehara S et al. Artificial intelligence analysis of three-dimensional imaging data
derives factors associated with postoperative recurrence in patients with radiologically solid-predominant small-sized lung cancers. Eur J Cardiothorac Surg 2021;
doi:10.1093/ejcts/ezab541.

Yujin Kudo a,*, Yoshihisa Shimadaa, Jun Matsubayashib, Yoshiro Kitamura c, Yojiro Makinoa,
Sachio Maeharaa, Masaru Hagiwaraa, Jinho Parkd, Takafumi Yamadad, Susumu Takeuchi a,
Masatoshi Kakihanaa, Toshitaka Nagaob, Tatsuo Ohiraa, Jun Masumoto e and Norihiko IkedaaaDepartment of Surgery, Tokyo Medical University, Tokyo, Japan
Department of Anatomic Pathology, Tokyo Medical University, Tokyo, Japanc
Imaging Technology Center, FUJIFILM Corporation, Tokyo, Japand
Department of Radiology, Tokyo Medical University, Tokyo, JapaneMedical System Research & Development Center, FUJIFILM Corporation, Tokyo, Japanb* Corresponding author. Department of Surgery, Tokyo Medical University, 6-7-1 Nishishinjuku, Shinjuku-ku, Tokyo 160-0023, Japan. Tel: +81-3-3342-6111ext5070;
fax: +81-3-3349-0326, e-mail: yjnkudo@gmail.com.
Received 29 June 2021; received in revised form 9 September 2021; accepted 23 November 2021

Abstract
OBJECTIVES: Indications of limited resection, such as segmentectomy, have recently been reported for patients with solid-predominantlung cancers <_2 cm. This study aims to identify unfavourable prognostic factors using three-dimensional imaging analysis with artificial intelligence (AI) technology.

C The Author(s) 2021. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.
V

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Artificial intelligence analysis of three-dimensional imaging data
derives factors associated with postoperative recurrence in patientswith radiologically solid-predominant small-sized lung cancers

THORACIC

European Journal of Cardio-Thoracic Surgery 00 (2021) 1–10
https://doi.org/10.1093/ejcts/ezab541

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METHODS: A total of 157 patients who had clinical N0 non-small cell lung cancer with a radiological size <_2 cm, and a consolidation
tumour ratio > 0.5, who underwent anatomical lung resection between 2011 and 2017 were enrolled. To evaluate the three-dimensional
structure, the ground-glass nodule/Solid Automatic Identification AI software Beta Version (AI software; Fujifilm Corporation, Japan)
was used.

CONCLUSIONS: High AI-SV, high SUVmax and abnormal carcinoembryonic antigen level were unfavourable prognostic factors ofpatients with solid-predominant lung adenocarcinoma with a radiological size <_2 cm. Our results suggest that lobectomy should be preferred to segmentectomy for patients with these prognostic factors.
Keywords: Lung cancer • Artificial intelligence • Three-dimensional imaging • Ground-glass nodule • Maximum standardized uptake
value

ABBREVIATIONS
3D
3-Dimensional
AI
Artificial intelligence
AI-SV
Solid-part volume calculated by AI software
AI-WV Whole tumour volume calculated by AI software
AUC
Area under the curve
CEA
Carcinoembryonic antigen
CT
Computed tomography
CTR
Consolidation tumour ratio
FDG
Fluorodeoxyglucose
GGNs
Ground-glass nodules
HRCT
High-resolution computed tomography
JCOG
Japan Clinical Oncology Group
NSCLC Non-small-cell lung cancer
OS
Overall survival
PET
Positron emission tomography
RFS
Recurrence-free survival
SUVmax Maximum standardized uptake value
WJOG West Japan Oncology GroupINTRODUCTION
Recent developments in imaging modalities and the widespreaduse of low-dose helical computed tomography (CT) for the detection of lung cancer have contributed to the increase in the detection rate of small pulmonary lesions [1]. Ground-glass nodules(GGNs) are associated with a favourable prognosis and havebeen reported to indicate adenocarcinoma with a less invasive
component [2]. In the TNM classification (8th edition), the size of
GGNs is no longer considered to be a T factor [3]. In Japan, clinical trials using the consolidation tumour ratio (CTR) have been
conducted, and the Japan Clinical Oncology Group (JCOG) 0201
study showed that a CTR of <_0.25 in small-sized adenocarcinomas <_2 cm could be used as a radiological criterion for a non-invasive pathology [4]. In addition, the JCOG0804 study, which wasa nonrandomized confirmatory study of the effectiveness of limited surgical resection for patients with peripheral early lung cancer, validated the efficacy of partial resection for this population[5]. Furthermore, the results of the JCOG and the West Japan

Oncology Group (WJOG) (JCOG0802/WJOG4607L) study demonstrated that a higher survival benefit of segmentectomy was evident for patients with radiologically solid-predominant tumours,
which were 2 cm or less in diameter with a CTR of 0.5–1, compared with lobectomy [6]. However, lesions containing GGNs are
sometimes difficult to measure precisely, such as GGNs showing
a mosaic pattern with solid lesions or weak/high-density GGNs,
because it is difficult to distinguish GGNs from the normal tissue.
The measured values might hence be variable owing to the subjective nature of the measurement process.
In addition, positron emission tomography (PET) is essential forevaluating unfavourable prognostic factors as well as the lymph
node status of patients with small-sized lung cancers [7]. The maximum standardized uptake value (SUVmax) is reported to be one
such prognostic factor. A recently developed deep learning
method using artificial intelligence (AI) technology enables the accurate determination of lymph node status in early-stage lung cancer patients using CT images [8]. Deep learning is one of thedomains of machine learning, which in surgical practice provides
promising opportunities to improve surgical performance and patient outcomes [9]. Moreover, the combination of findings on PET/CT and high-resolution CT (HRCT) has been shown to be morepredictive of the malignancy of small-sized lung cancers, including
lymph node metastasis than PET/CT or HRCT alone, and has evenbeen shown to be useful for determining surgical procedures.
Although the use of this combination has recently been recommended, it was not included in the evaluation factors of the
JCOG0802/WJOG4607L trial. Furthermore, there are issues regarding the standardization of SUVs among different institutions andPET systems, as the results vary depending on the PET system used
and the reconstruction method.
In this study, we aimed to analyse the unfavourable prognostic
factors of patients with solid-predominant tumours <_2 cm. In recent years, AI technology has made substantial progress, andhence, we decided to utilize GGN/Solid Automatic Identification
AI software Beta Version (AI software; Fujifilm Corporation,
Tokyo, Japan), which works on the Synapse Vincent System(Fujifilm Corporation) [10]. The software enables the analysis ofdata in three-dimensional (3D), and the automatic measurementof the percentage and volume of GGNs and solid tumours, without any variation among different measurement processes.

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RESULTS: Maximum standardized uptake value (SUVmax) and solid-part volume measured by AI software (AI-SV) showed significant differences between the 139 patients with adenocarcinoma and the 18 patients with non-adenocarcinoma. Among the adenocarcinoma
patients, 42 patients (30.2%) were found to be pathological upstaging. Multivariable analysis demonstrated that high SUVmax, high carcinoembryonic antigen level and high AI-SV were significant prognostic factors for recurrence-free survival (RFS; P < 0.05). The 5-year RFS
was compared between patients with tumours showing high SUVmax and those showing low SUVmax (67.7% vs 95.4%, respectively,
P < 0.001). The 5-year RFS was 91.0% in patients with small AI-SV and 68.1% in those with high AI-SV (P = 0.001).

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Figure 1: Screenshot of ground-glass nodule/Solid Automatic Identification AI Software Beta Version in the Synapse Vincent system. Pink: solid component; green:
ground-glass nodule component.

MATERIALS AND METHODSRadiological evaluation

Ethical statement

HRCT images of the whole lung were acquired using the settings
previously described [12]. Briefly, HRCT images with a 1.25-mm
thickness were obtained of the entire lung. The whole tumour
size and solid tumour size were preoperatively measured on the
HRCT images by highly experienced radiologists (Jinho Park and
Takafumi Yamada). The solid tumour size was defined as the
maximum dimension of the solid component of the lung window, excluding GGNs. The CTR (%) was defined as the maximum
dimension of consolidation in the lung window setting divided
by the maximum dimension of the tumour in the lung window
setting.
Detailed radiological evaluation is shown in the Supplementary
Material.

The Institutional Review Board of our hospital approved theprotocol for data collection and analyses, and the need to obtainwritten informed consent from each patient was waived (study
approval no: SH3951).

Patient cohort
A retrospective analysis was performed on patients chosen using
the following inclusion criteria: (i) patients who underwent curative anatomical lung resection (lobectomy or segmentectomy)
for non-small-cell lung cancer (NSCLC) at Tokyo Medical
University Hospital between 2011 and 2017, (ii) patients who hadclinical N0 NSCLCs <_2 cm radiologically, with a CTR of 0.5–1, and(iii) patients who underwent 18F-fluorodeoxyglucose (FDG) PET/CT at the same institution for both evaluation of the primary tumour and for the evaluation of any metastatic lesions. The exclusion criteria were patients who received preoperative
chemotherapy and/or radiotherapy. The preoperative stage wasdetermined by HRCT, 18F-FDG PET/CT or endobronchialultrasound-guided transbronchial needle aspiration followed byhistological analysis. Clinical staging was performed based on theTNM classification (8th edition) [3, 11].
Detailed lymph node evaluation or patient follow-up is shownin the Supplementary Material.

AI imaging analysis
After the results of conventional enhanced CT were transmitted
to the Synapse Vincent system as digital imaging and communication in medicine data [13], AI software in the system automatically detected lung nodules in the bilateral lungs and AI software
reconstructed the 3D images of the lungs and nodules (Fig. 1). AI
software was subsequently able to distinguish between GGNs
and radiologically solid lesions in the 3D images, once the target
lung nodules were selected. The software determined the volumes of the GGNs, radiologically solid lesions and whole tumour
lesions, as well as the ratios of GGNs or the radiologically solid

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Y. Kudo et al. / European Journal of Cardio-Thoracic Surgery

lesions. Information about the maximum sizes of the radiologically solid lesions within the nodules was also obtained.

The GGN/Solid Automatic Identification AI Software Beta Version
is a developing software that works in the Synapse Vincent system. The software was not approved for clinical use at the time
the study was conducted. The software uses 2 steps to analyselung nodules using AI. First, the lung nodule detection algorithm
is applied to the entire lung field within the imaging range, and a
3D bounding box of the nodule is detected. Next, the local volume image in each detected bounding box is automatically segmented into 3 parts, i.e. the surrounding anatomical structures(background) and the solid-part and the GGN part of the nodule.
Then, the solid-part volume, the whole tumour volume, the ratioof the solid-part volume and the ratio of the GGN part volume
are automatically calculated. This segmentation algorithm isbased on 3D-Convolutional Neural Network using modified UNet architecture. The network consists of 17 convolutional layers.
The training dataset for the GGN/solid-part segmentation algorithm was the total number of 357 patients with 1 lung nodule ormore. Ground truth labels were prepared under the supervisionof expert radiologists. In the training phase, 80% of the data was
randomly chosen and used for actual training of the network.
The rest of the data was used for validation. Segmentation performance was quantitatively evaluated by the metrics of recall
and precision, using the data of 18 patients that were not used
for the training. The recall was defined as the number of correctlypredicted voxels by AI divided by the number of target groundtruth voxels. Precision was defined as the number of correctlypredicted voxels by AI divided by the sum of the number of target ground truth voxels and falsely predicted voxels by AI. The
mean recall values were 0.698 for the GGO part and 0.904 forthe solid part. The mean precision values were 0.676 for the GGOpart and 0.888 for the solid part.
Detailed protocol is shown in the Supplementary Material.

Pathological analyses
Surgical specimens were reviewed by 2 experienced pathologists(Jun Matsubayashi and Toshitaka Nagao). TNM classification was
performed according to the Union for International Cancer
Control and the American Joint Committee on Cancer staging
system (8th edition) [14]. Histopathological analyses were performed according to the World Health Organization criteria (4thedition) [15]. Detailed pathological evaluation is shown in the
Supplementary Material.

Statistical analysis
The primary end point in this study was to identify radiologicalparameters associated with survival using the AI software. One of
the secondary end points was to evaluate the radiological parameters of tumours categorized by histological characteristics, andthe other was to assess whether conventional preoperative factors were associated with survival in this cohort. Overall survival(OS) and recurrence-free survival (RFS) were estimated using the

RESULTS
Different clinical profiles between patientswith adenocarcinoma and those with
non-adenocarcinoma
We analysed 157 patients with resected NSCLCs, which wereradiologically demonstrated to be solid-predominant nodules<_2 cm. All nodules had CTR of 0.5–1.0. The characteristics of thepatients are shown in Table 1. The number of patients withadenocarcinomas and non-adenocarcinomas was 139 and 18,
respectively.
Figure 2A illustrates that the median SUVmax was 2.9 [interquartile range (IQR): 1.5–7.0] in adenocarcinoma and 12.2 (IQR:
7.4–15.4) in non-adenocarcinoma with a statistically significant
difference (P < 0.001). Figure 2B shows that the median solid-partvolume measured by AI software (AI-SV) was 724 mm3 (IQR:

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Protocols of the GGN/Solid Automatic
Identification AI Software Beta Version (AIsoftware) in the Synapse Vincent system

Kaplan–Meier method, and differences in survival rates were
determined by log-rank analysis. OS was defined as the time
elapsed from the date of pulmonary resection to the date ofdeath. RFS was defined as the time elapsed from the date of pulmonary resection to the date of the first recurrence or the last
follow-up showing no recurrence. The last follow-up observation
was censored if the patient was alive or lost to follow-up. As forrecurrence patterns, detailed definition is shown in the
Supplementary Material.
Receiver operating characteristic curve analysis was performed
to assess the discrimination of the pathological staging performed using radiological parameters computed by PET/CT
imaging and the AI software. The optimal cut-off points were
defined using the curves closest to the upper left-hand corner, toseparate the 2 categories using continuous variables for eachparameter.
Continuous variables between 2 categories were analysedusing the Mann–Whitney U-test when normality tests did notnormally distribute variables. Univariable analysis for survival was
performed among the different groups. In the multivariable analysis, the backward stepwise procedure was performed for preoperative variables such as radiological factors or
carcinoembryonic antigen (CEA), using the Cox proportional hazards model to control for bias due to confounding. An adjusted
significance level of 0.05 was used to exclude a variable from themodel. The likelihood ratio test was performed to analyse interactions in terms of variables selected in the final model. All variables, including radiological parameters, CEA and pathologicalparameters, were available, although missing data were basically
excluded from analysis.
Subgroup analysis was performed because histological differences might affect radiological profiles. Bonferroni P-value adjustment was used to control for multiple comparisons. A P-valueof <0.025 was considered to indicate a statistically significant difference between groups in each analysis. Furthermore, the analysis was exploratory, and there was no prior planning to adjust
for multiple comparisons; the P-values are interpreted as descriptive rather than confirmatory. All statistical calculations were performed using SPSS statistical software (version 27.0; SPSS, Inc.,
Chicago, IL, USA). Graphs were generated using GraphPad Prism(version 9.1.0.; GraphPad Software, San Diego, CA, USA) andSPSS statistical software.

Y. Kudo et al. / European Journal of Cardio-Thoracic Surgery

Patient characteristics and radiological factors (n = 157)

VariableMedian age, years (range)
Smoking habit
CEA
Tumour location
Radiological whole tumour size, cm
Radiological solid-part size, cm
CTR
SUVmaxRadiological whole tumour volume, mm3 (AI software)Radiological solid-part volume, mm3 (AI software)Radiological solid-part volume ratio (AI software)Radiological solid-part size, cm (AI software)
Surgical procedure
Adjuvant chemotherapy

Men
Women
Ever smoker/unknown
Never smoker<_5.0 ng/ml>5 ng/ml
Right lungLeft lungMedian (range)
Median (range)
Median (range)
Median (range)
Median (range)
Median (range)
Median (range)
Median (range)
Lobectomy
Segmentectomy
Yes

Number (%)
Adenocarcinoma (n = 139)

Non-adenocarcinoma (n = 18)

65 (46.8)
74 (53.2)
67.0 (38–86)
78 (57.1)
61 (43.9)
121 (87.1)
18 (12.9)
86 (61.9)
53 (38.1)
1.5 (0.7–2.0)
1.3 (0.4–2.0)
1.0 (0.5–1.0)
2.9 (0.6–65.1)
1,214 (253–6,575)
724 (0–4,523)
67.2 (0–99.7)
1.3 (0–2.3)
131 (94.2)
8 (5.8)12 (8.6)

15 (83.3)
3 (16.7)
74.5 (63–86)
18 (100)0 (0)14 (77.8)
4 (22.2)
9 (50.0)9 (50.0)
1.55 (1.1–2.0)
1.55 (1.1–2.0)
1.0 (1.0–1.0)
12.2 (3.3–25.1)
1,425 (515–5,188)
1,242 (375–4,858)
88.2 (72.9–98.6)
1.5 (1.0–2.1)
18 (100)0 (0)1 (5.6)

CEA: carcinoembryonic antigen; CTR: consolidation tumour ratio; SUVmax: maximum standardized uptake value.

Figure 2: Distribution of maximum standardized uptake value (A), solid-part volume calculated by AI software (B) and ratio of solid-part volume calculated by AI software (C) in the Vincent system of patients with adenocarcinoma and non-adenocarcinoma. (D) Correspondence between ratio of solid-part volume calculated by AI
software and consolidation tumour ratio.

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Sex

Category

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Table 1:5

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Table 2:

Pathological characteristics of the tumours (n = 157)

Variable

Blood vessel invasion
Lymphatic permeation
Visceral pleural invasion
Pulmonary metastasis (pm1)
N status

Pathological stage

EGFR mutation status

ALK fusion geneMedian (range)
Median (range)
Sq
Combined LCNEC
Adsq
Present
Present
Present
Present
N0
N1
N2
IA1–IA2
IA3
IB
IIA/IIB/IIIA
MutantWild type
Unknown
Positive

Number (%)
Adenocarcinoma (n = 139)

Non-adenocarcinoma (n = 18)

1.5 (0.7–5.0)
1.0 (0.0–3.0)
42 (30.2)
47 (33.8)
29 (20.9)
4 (2.9)124 (89.2)
6 (4.3)9 (6.5)95 (69.3)
4 (2.9)22 (15.8)
16 (11.5)
63 (45.3)
55 (39.6)
21 (15.1)
2 (1.4)

1.8 (1.2–3.2)
1.8 (1.2–3.2)
16 (88.9)
1 (5.6)1 (5.6)6 (33.3)
7 (38.9)
3 (16.7)
0 (0)14 (77.8)
3 (16.7)
1 (5.6)
11 (61.1)0 (0)3 (16.7)
4 (22.2)0 (0)10 (55.6)
8 (44.4)0 (0)Adsq: adenosquamous cell carcinoma; ALK: anaplastic lymphoma kinase; EGFR: epidermal growth factor receptor; LCNEC: large cell neuroendocrine carcinoma;
Sq: squamous cell carcinoma.

355–1301) in adenocarcinoma and 1242 mm3 (IQR: 802–1962) in
non-adenocarcinoma, indicating a statistically significant difference (P = 0.022). Figure 2C demonstrates that the median ratio of
AI-SV was 67.2% (IQR: 43.1–86.7) in adenocarcinoma and 88.2%(IQR: 79.8–91.8) in non-adenocarcinoma with a statistically significant difference (P < 0.001). In addition, the association between CTR and the ratio of AI-SV is shown in Fig. 2D, whichindicated that the proportion of AI-SV were distributed fromeach CTR to various values. Supplementary Material, Fig. S1A and
B illustrates 2 examples of part-solid GGNs with the same CTR of
1.0, to more clearly demonstrate the usefulness of AI software.
These results indicated the radiologically different profiles oftumours on PET/CT and 3D-CT upon analysis using AI software.
Therefore, we separately investigated the association between
the pathological findings and radiological findings according to
the histological type of the tumour (adenocarcinoma and nonadenocarcinoma).

Differences in pathological findings between
adenocarcinoma and non-adenocarcinoma
Next, we reviewed the pathological features of adenocarcinoma
and non-adenocarcinoma, as shown in Table 2. Nonadenocarcinoma included 16 patients with squamous cell carcinoma, and 4 patients (22.2%) had lymph node metastasis.
In the 139 patients with adenocarcinoma, the median pathological whole tumour size and the median pathological invasive
component size were 1.5 and 1.0 cm, respectively. Pulmonary
metastasis was found in 4 patients (2.9%), and lymph node metastasis was found in 15 patients (10.8%). Pathological stage IA3,
IB and II–IIII were observed in 4 patients (2.9%), 22 patients(15.8%) and 16 patients (11.5%), respectively. These results demonstrated that adenocarcinomas that were radiologically measured to be <_2 cm are pathologically upstaged by 30.2%. We theninvestigated the preoperative prognostic factors, including 3D-CT
findings upon AI software, in patients with adenocarcinoma.

Identifying factors associated with high malignancy
potential in lung adenocarcinoma
Receiver operating characteristic curve analysis was performed toassess the accuracy of the pathological staging using the radiological parameters computed by PET/CT imaging and by AI software. The results of the calculations of the area under the curve(AUC) for tumours of pathological stages IA3 or higher showedthat the optimal cut-off value of the SUVmax was 3.45 (AUC:
0.795; 95% CI: 0.722–0.868; P < 0.001; Supplementary Material,
Fig. S2A); that of AI-SV was 1074 mm3 (AUC: 0.785; 95% CI:
0.710–0.860; P < 0.001; Supplementary Material, Fig. S2B); that of
the AI-SV ratio was 74.5% (AUC: 0.842; 95% CI: 0.774–0.904;
P < 0.001; Supplementary Material, Fig. S2C); that of whole tumour volume calculated by AI software (AI-WV) was 1436 mm3(AUC: 0.673; 95% CI: 0.578–0.768; P < 0.001; Supplementary
Material, Fig. S2D); and that of solid-part size of the tumour calculated by AI software was 14.0 mm (AUC: 0.731; 95% CI: 0.650–
0.812; P < 0.001; Supplementary Material, Fig. S2E). We classified
the patients with adenocarcinoma using these parameters to investigate prognostic factors.

Survival analysis of patients with adenocarcinoma
and non-adenocarcinoma
The median follow-up period was 51.2 months (range: 8.1–93.2).
The OS and RFS curves between adenocarcinoma and nonadenocarcinoma are shown in Supplementary Material, Fig. S3A
and B. The 5-year OS was 92.2% in adenocarcinoma patients and
89.0% in patients with non-adenocarcinoma. The 5-year RFS was

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Pathological whole tumour size, cm
Pathological invasive tumour size, cm
Histology

Category

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Table 3: Multivariable analysis of model A for recurrencefree survival in lung adenocarcinoma patients (n = 139)

Table 4: Multivariable analysis of model B for recurrencefree survival in lung adenocarcinoma patients (n = 139)

Variable

Variable

>5.0
>3.45

Hazard ratio

95% CI

P-value

3.44
8.56

1.39–8.47
2.53–28.9

0.007
0.001

CEA
AI-SV

Multivariable analysis>5.0>1074 mm3

Hazard ratio

95% CI

P-value

3.05
2.81

1.20–7.76
1.15–6.87

0.019
0.023

CEA, carcinoembryonic antigen; CI: confidence interval; SUVmax, maximum standardized uptake value.

AI-SV: solid-part volume analysed by AI software; CEA: carcinoembryonicantigen; CI: confidence interval.

82.9% in adenocarcinoma patients and 83.3% in patients with
non-adenocarcinoma.
In patients with adenocarcinoma, univariable analysis showedthat SUVmax, CEA, AI-WV, AI-SV and solid-part size of the tumour
calculated by AI software were statistically significant factors associated with RFS, whereas CTR was not a statistically significant factor(Supplementary Material, Table S1). Multivariable analysis in model
A showed that a high SUVmax and a high CEA were independentsignificant factors associated with RFS (P = 0.001 and P = 0.007, respectively; Table 3). The variables in model A consisted of preoperative factors analysed by univariable analysis, including SUVmax.
Although SUV has been widely used as a semi-quantitative metricof uptake in FDG PET/CT, it is substantially affected by various factors, including PET/CT scanner specifications and reconstruction
parameters [16]. Therefore, SUVmax is not an appropriate variable
to utilize as a measurable standardized value among various institutions. To control for interinstitutional variability, we also performed
multivariable analysis in model B using the radiological parameters
excluding SUVmax. The results demonstrated that a high AI-SV anda high CEA were significant independent factors associated with RFS(P = 0.023 and P = 0.019, respectively; Table 4).
Next, survival analysis of patients with lung adenocarcinoma
was performed using each preoperative prognostic factor in
Fig. 3. The 5-year RFS was 91.0% in the group with AI-SV <_1074
mm3 and 68.1% in the group with AI-SV >1074 mm3 (Fig. 3A).
The 5-year RFS was 86.7% in the group with normal CEA leveland 54.6% in that with high level (Fig. 3B). The 5-year RFS was
95.4% in the group with SUVmax <_3.45 and 67.7% in the groupwith SUVmax >3.45 (Fig. 3C). There were statistically significant
differences in the RFS among the groups (P = 0.001, P = 0.001 and
P < 0.001, respectively, Fig. 3A–C). The association between thepattern of initial recurrence and each radiological prognostic factor is shown in Supplementary Material, Table S2. The ratios of
local recurrence, including both distant and local recurrence,
were 19%, 14% and 10% in patients with high SUVmax, high AISV and high CEA, respectively.
Finally, the result of competing risk analysis with mortality isillustrated in Fig. 4. The cumulative incidence of mortality at
5 years was 5.6% for lung cancer-specific deaths and 3.4% for nonlung cancer-specific deaths in the cohort of lung adenocarcinoma.

prognosis, (ii) high SUVmax and abnormal CEA level were also
unfavourable prognostic factors, and (iii) SUVmax values werehigh in almost all non-adenocarcinomas, and 20% of thepatients had lymph node metastasis.
Recently, the JCOG0802/WJOG4607L study demonstrated thatsegmentectomy resulted in more favourable survival rates
compared with lobectomy in patients with peripheral solidpredominant lung cancers <_2 cm [5-year OS: 94.3% (segmentectomy) vs 91.1% (lobectomy), P = 0.0082] [6]. Segmentectomy isgenerally reported to be more favourable than lobectomy owing
to the preservation of lung function [17]. However, the trial
showed only a difference of 3.5% in postoperative 1-year pulmonary function between patients who underwent segmentectomy and those who underwent lobectomy, indicating that oneof the secondary end points was unmet [6].
AI software can objectively evaluate GGN lesions and solid
lesions in 3D images. Peripheral solid-predominant lung cancers<_2 cm in the JCOG0802/WJOG4607L study had the crucial inclusion criteria of ‘0.5 < CTR <_ 1’. As the CTR is calculated from the
maximum tumour diameter and the diameter of the solid component in 2D images, the CTR has limitations as a measurementfor 3D tumours. Furthermore, lesions containing GGNs, such as
GGNs showing a mosaic pattern with solid lesions or weak/highdensity GGNs, are sometimes difficult to measure precisely because of the difficulty in distinguishing GGNs from normal tissue.
The measured values might hence be variable owing to the subjective nature of the measurement process. Many clinicians havegenerally experienced that GGN lesions are challenging to measure accurately, and subjective factors of the physicians may affectthe measurement of lesions containing GGNs.
Recent studies demonstrated that radiologically pure-solid
tumours in small lung cancers are an unfavourable prognostic
factor [18]. None of the patients showed an AI-SV ratio of 100%in our cohort, because AI software calculated the AI-SV ratio ofradiologically pure-solid tumours as 95–99%. However, multivariable analysis did not identify AI-SV ratio as a statistically significant prognostic factor. In contrast, AI-SV was a poor
prognostic factor based on our multivariable analysis. We assume
that the volume of the solid component more directly reflects
malignant potential associated with prognoses, such as the predominant subtype of adenocarcinoma and lymphovascular invasion, than the ratio of solid volume to whole tumour volume. In
TNM classification (8th edition), ‘size of solid-part’ in part-solid
GGNs represents a pathological variable for differential diagnoses, such as adenocarcinoma in situ, minimally invasive adenocarcinoma and lepidic adenocarcinoma, whereas the ratio of thesolid-part size to whole tumour size is not mentioned [3].
Furthermore, to understand the difference between solid-part

DISCUSSION
In this study, we analysed the preoperative prognostic factors forsolid-predominant lung cancers <_2 cm using AI software as well
as by conventional methods. Our results demonstrated that (i)the objective parameter that was calculated by AI software, i.e.
solid tumour volume, was able to predict an unfavourable

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CEA
SUVmaxMultivariable analysis

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Y. Kudo et al. / European Journal of Cardio-Thoracic Surgery

Figure 4: The cumulative incidence of mortality between lung cancer-specificdeaths and non-lung cancer-specific deaths in the cohort of lungadenocarcinoma.

volume and ratio of solid-part volume, 2 different-sized sphericaltumours can be imagined, in which one has an overall diameterof 2 cm with a solid-part of 1 cm in diameter and the other has
an overall diameter of 1 cm and a solid-part of 0.5 cm in diameter. The ratio of solid-part volume to whole tumour volume isthe same for both tumours. However, the solid-part volume of
the former tumour is 8 times larger than that of the latter. Some
reports demonstrated that the solid-part volume of the tumour is
a prognostic factor of small-sized lung adenocarcinoma [19]. As

our study has shown, AI software can minimize human errors inmeasurement and provide objective data associated with survival, including AI-SV, which will lead to decisions of surgical procedures, such as segmentectomy or lobectomy.
The essential points of segmentectomy were resection of boththe affected and adjacent segments together to obtain an adequate surgical margin, and intraoperative analysis of the segmental, hilar and mediastinal lymph nodes to confirm N0 disease [20].
Tsutani et al. [21] defined N0 criteria as having a solid tumour size
<0.8 cm and SUVmax <1.5, and they suggested that patients who
did not meet the N0 criteria should undergo segmentectomy orlobectomy with systematic hilar and mediastinal lymph node dissection rather than wedge resection, because of the possibility oflymph node metastasis. Analysis of frozen sections of intraoperative dissected lymph nodes is recommended when segmentectomy is performed on patients who do not meet the N0 criteria. If
lymph node metastasis is intraoperatively identified, the procedure
should be converted to lobectomy. In practice, however, it might
be difficult to examine all intralobar or lobar lymph nodes when
performing segmentectomy, incredibly complex segmentectomy.
Therefore, an accurate preoperative diagnosis is crucial in determining the indication for segmentectomy.

Limitations
There are some limitations to this study. First, this was a retrospective review of patients from a single institute. The small patientgroups, particularly for patients with non-adenocarcinoma, might
have introduced bias. Furthermore, interobserver variability in calculating the radiological parameters cannot be ignored. The border

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Figure 3: Recurrence-free survival curves based on solid-part volume calculated by AI software (A), carcinoembryonic antigen (B) and maximum standardized uptake
value (C).

Y. Kudo et al. / European Journal of Cardio-Thoracic Surgery

Author contributions
Yujin Kudo: Conceptualization; Data curation; Formal analysis; Investigation;
Writing—original draft; Writing—review & editing. Yoshihisa Shimada:Conceptualization; Writing—review & editing. Jun Matsubayashi: Data curation; Investigation. Yoshiro Kitamura: Software; Writing—review & editing.
Yojiro Makino: Data curation. Sachio Maehara: Data curation. Masaru
Hagiwara: Data curation. Jinho Park: Data curation; Investigation;
Supervision. Takafumi Yamada: Data curation; Investigation. Susumu
Takeuchi: Data curation. Masatoshi Kakihana: Data curation. Toshitaka
Nagao: Data curation; Investigation; Supervision. Tatsuo Ohira: Supervision.
Jun Masumoto: Software; Supervision; Writing—review & editing. Norihiko
Ikeda: Conceptualization; Supervision; Writing—review & editing.

Reviewer information
CONCLUSION
In conclusion, AI software can provide surgeons with radiologically quantified values in 3D of small-sized lung nodules. High AISV, high SUVmax and abnormal CEA level were found to be unfavourable prognostic factors in patients with adenocarcinomas
showing radiologically solid-predominant lung cancers <_2 cm.
Indications for segmentectomy should be reappraised in patientswith these factors. Prospective multi-institutional studies are warranted to validate the association between these radiological features and outcomes. In the future, more precise preoperative
assessment of malignancy is expected to become possible byintegrating AI software analysis with PET/CT.

SUPPLEMENTARY MATERIAL
Supplementary material is available at EJCTS online.

ACKNOWLEDGEMENTS
The authors are indebted to the medical editors of the Department
of International Medical Communications for editing and reviewing
the English manuscript.

Funding
Norihiko Ikeda reports research grant to the Department of Surgery,Tokyo Medical University from FUJIFILM Corporation.

Conflict of interest: Susumu Takeuchi reports receiving personal
lecture fees from AstraZeneca, Taiho, Ono, Nippon Kayaku, Kyowa
Kirin, Eli Lilly and Company Japan, Bristol Myers Squibb, Novartis
Japan, MSD K.K. and Chugai Pharmaceutical Company Limited. The
other authors declare no competing interests.

Data Availability Statement
The analysed data in this article will be shared on reasonable request to the corresponding author, except for the training andEuropean Journal of Cardio-Thoracic Surgery thanks J Matthew Reinersman
and the other, anonymous reviewer(s) for their contribution to the peer review process of this article.

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Downloaded from https://academic.oup.com/ejcts/advance-article/doi/10.1093/ejcts/ezab541/6481613 by Tokyo Medical University user on 13 February 2022of the GGN can be very obscure, and hence, estimation of tumour
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