Pulmonary function tests for identifying pathological invasive stage I lung adenocarcinoma: A multiple-center retrospective cohort study

doi:10.21203/rs.3.rs-4843066/v1

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Pulmonary function tests for identifying pathological invasive stage I lung adenocarcinoma: A multiple-center retrospective cohort study

https://doi.org/10.21203/rs.3.rs-4843066/v1

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Background

Preoperative biopsy can hardly be used to diagnose lung cancer invasion; therefore, supplementary methods to estimate pathological tumor invasiveness are needed to identify candidates for limited resection. We aim to ascertain the risk factors and create and verify a model for predicting lung cancer invasion likelihood.

Methods

A nomogram was trained and validated on retrospectively collected data of patients with primary lung cancer whose pulmonary function was examined within 3 months before surgery. Least absolute shrinkage and selection operator logistic regression were used for important factor selection. The nomogram was established by combining preoperative pulmonary function tests (PFTs) and clinical factors. The area under the receiver operating characteristic curve and decision curve analysis (DCA) were used to evaluate the model’s predictive performance and clinical utility, respectively.

Results

Lung function impairment was detected in 508 patients (38.72%, 508/1312). The prediction model, which included age (odds ratio [OR] = 1.02), tumor size (OR = 1.31), lung function (OR = 3.10), basophils (OR = 0.51), and direct bilirubin levels (OR = 1.15), showed good performance in both sets. The areas under the curve for predicting lung cancer invasion were 0.820 (95% confidence interval [CI]: 0.781–0.858), 0.758 (95% CI: 0.659–0.858), and 0.838 (95% CI: 0.797–0.879) in the training, internal validation, and external validation sets, respectively, indicating good performance. In the multivariable analysis, patients with restrictive ventilation impairment (OR 2.86 [95% CI 1.43–5.69]) and diffusion capacity impairment (OR 4.23 [95% CI 1.00-17.84]) had high tumor invasion risks.

Conclusions

Lung function impairment could potentially serve as a biomarker for stage I lung adenocarcinoma invasion.

Lung adenocarcinoma

Invasion

Lung Function Tests

Risk Factors

Prediction Model

Lung cancer is the most frequently diagnosed malignancy and one of the leading causes of cancer-related death worldwide [1, 2]. Non-small cell lung cancer accounts for 85–90% of lung cancers [3]. Lung adenocarcinoma (LUAD) is a subtype of non-small cell lung cancer that accounts for 50% of all lung cancer diagnoses [4, 5]. LUAD mainly consists of adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAD) with or without histologic invasive components according to the 2015 World Health Organization classification [5]. AIS and MIA have a nearly 100% recurrence-free survival after complete tumor segmentectomy (SE) [6, 7]. Additionally, this surgical approach maximally reserves pulmonary function. In contrast, patients with IAD more often undergo lobectomy (LE) and may require lymph node dissection owing to the higher probability of regional and distant metastasis; the 5-year survival rate is only 75% [6, 7]. Therefore, distinguishing AIS/MIA from IAD is vital.

Lung cancer development is influenced by multiple factors, including sociodemographic, environmental, clinical, and genetic factors [8]. There may be a hereditary predisposition or shared physiological processes between pulmonary function decline and the development of lung cancer [9, 10]. Therefore, pulmonary function tests (PFTs) are potential prognosticators of lung cancer [11]. Lung cancer can be predicted by severe declines in lung function [12]. Furthermore, chronic obstructive pulmonary disease, emphysema, and asthma are associated with a higher rate of lung cancer [13–15]. Airflow restriction and a reduced forced expiratory volume in one second (FEV1) are also linked to an increased risk of developing lung cancer [12, 16]. It has been discovered that a lower FEV1 is an independent risk factor for lung cancer. Additionally, preoperative PFTs are important for predicting perioperative morbidity and mortality [9].

As biopsy is insufficient for preoperative pathological differentiation of AIS/MIA and IAD, supplementary methods to estimate pathologic tumor invasiveness are urgently needed to identify candidates for limited resection. However, whether preoperative pulmonary function impairment can predict pathologic tumor invasion in LUAD, particularly for distinguishing AIS/MIA from IAD, is unclear. We conducted this study in patients with early-stage LUAD to assess the relationship between preoperative pulmonary function impairment and pathological tumor invasion (AIS/MIA vs. IAD).

Participants

We retrospectively collected two independent patient cohorts. The primary cohort from Wuhan Union Hospital, which was split into a training set and a validation set, was used for model selection and hyperparameter optimization. This cohort was based on real-world data from 911 patients with primary lung cancer treated at Wuhan Union Hospital between January 2018 and December 2022. The eligibility criteria were as follows: 1) operable clinical stage I lung adenocarcinoma, 2) PFTs were performed before surgery within 3 months, 3) nodule size ≤ 3 cm in diameter on computed tomography (CT), 4) no therapy history for lung cancer, and 5) a postoperative pathological examination was performed. The exclusion criteria were as follows: 1) tumor size > 3 cm, 2) multiple lesions or distant metastases, 3) history of additional malignant tumors, and 4) age < 18 or > 70 years. A total of 911 participants with complete data were included in the training and the internal validation sets (Fig. 1A). We also collected data for an external validation set, which was only used to evaluate the final model (Fig. 1B). The data for this cohort were obtained from Renmin Hospital of Wuhan University and comprised 401 patients with primary lung cancer with stage I tumors between January 2022 and March 2023. The inclusion and exclusion criteria were identical to those of the primary cohort.

The basic clinical information of the participants, including demographic characteristics, smoking status, laboratory parameters, pulmonary function, lung cancer diagnosis (histological type), tumor size, and cancer stage, was obtained from the electronic medical records system.

The study was approved by the Wuhan Union Hospital (No. S0802) and the Renmin Hospital of Wuhan University (No. WDRY2023-K004) Institutional Ethics Committees. Since the study was retrospective, patient consent was not required. This trial has been registered on the Clinical Trials website (no. NCT05830812).

Spirometry

PFTs were used to evaluate and monitor health status before surgery. Spirometry was performed using the same brand of spirometer under the supervision of well-trained staff according to the American Thoracic Society/European Respiratory Society guidelines [17]. The measured spirometry parameters included forced vital capacity (FVC), FEV1, total lung capacity (TLC), diffusion capacity for carbon monoxide, residual volume, and percentage of the predicted values. Age, sex, height, and ethnicity were considered to evaluate normal predictive values [18].

The spirometry data were divided into six groups: 1) Normal: FEV1/FVC ≥ 0.7 and FVC ≥ 80% of the normal predicted value; 2) Obstructive: FEV1/FVC < 0.7 [19]; 3) Restrictive ventilatory impairment: PFTs with reduced TLC (< 80% predicted) but normal or improved FEV1/FVC (> 0.7) [20]; 4) Diffusion capacity impairment: diffusion capacity for carbon monoxide < 80% predicted; 5) Preserved ratio impaired spirometry: a predicted FEV1 of less than 80% would mitigate the potential impact of airway reversibility in certain individuals [21]; and 6) Mixed ventilation impairment: FEV1/FVC < 0.7 and TLC < 80% predicted [22].

Histologic evaluation

Based on the fourth edition of the World Health Organization's categorization of lung tumors, tumors were histopathologically assessed by more than two pathologists [23]. AIS was defined as a localized, small (≤ 3 cm), neoplastic cell-only growth (pure lepidic growth) adenocarcinoma that exhibited lepidic growth and did not invade the pleura, stroma, or vessels. MIA was characterized as a solitary, ≤ 3 cm adenocarcinoma exhibiting a predominately lepidic pattern and a greatest-dimension invasion of ≤ 5 mm [23]. IAD was defined in the following manner: 1) histologic subtype different from a lepidic pattern, such as papillary, acinar, micropapillary, or solid; 2) tumor cells invading myofibroblastic stroma; 3) invasion of lymphatic, vascular, pleural, and air-space structures; and 4) spread across air spaces [23]. Because AIS and MIA have similar prognoses, this study did not assess them separately; AIS and MIA were defined as noninvasive, and IAD as invasive. Representative histological AIS and IAD tumors are shown in Fig. 2A-D.

Statistical analysis

R software (version 4.1.0) was used for all statistical studies. Using the Wilcoxon rank-sum test or Student's t-test, continuous variables were compared, and the results are presented as the median (standard deviation). Categorical variables were analyzed using Fisher’s exact test and are presented as n (%). The least absolute shrinkage and selection operator regression were employed to assess all potential factors contributing to the invasion of lung cancer. Univariate and multivariate logistic regression were used to build a predictive nomogram for lung cancer invasion. The area under the receiver operating characteristic curve, calibration plots, and decision curve analysis were used to evaluate the performance of the nomogram.

Baseline characteristics of study participants

Table 1 and Supplemental Table 1 show the detailed baseline characteristics of the subjects. The rates of lung cancer invasion were 82.7% (620/750), 83.2% (134/161), and 78.55% (315/401) in the in the training, internal validation, and external validation sets, respectively. Participants with lung cancer invasion were more likely to be older and have larger tumor sizes, fewer basophils (BAS), higher direct bilirubin levels, and higher glucose levels. There were significant differences in tumor size, lung function impairment, and direct bilirubin levels between the non-invasion and invasion groups in the training and validation sets.

Table 1

Baseline characteristics of all patients in the training and internal validation sets.
Variables	Training set (n = 750)			Internal validation set (n = 161)
Variables	Preinvasive (130)	Invasive (620)	P value	Preinvasive (27)	Invasive (134)	P value
Age, years	51.5 ± 12.26	56.9 ± 11.45	< 0.001	57.89 ± 10.65	58.54 ± 10.61	0.773
Sex, n (%)
Male	32 (24.6)	226 (36.5)		5 (18.5)	51 (38.1)
Female	98(75.4)	394 (63.5)	0.013	22 (81.5)	83 (61.9)	0.085
BMI, kg/m²	18.83 ± 1.71	18.74 ± 1.89	0.626	23.06 ± 2.79	23.71 ± 3.30	0.340
Smoking history, n (%)
NO	108 (83.1)	517 (83.4)		26 (96.3)	119 (88.8)
Yes	22 (16.9)	103 (16.6)	1.000	1 (3.7)	15 (11.2)	0.404
Location, n (%)
Right	77 (59.2)	360 (58.1)		21 (77.8)	77 (57.5)
Left	53 (40.8)	260 (41.9)	0.883	6 (22.2)	57 (42.5)	0.079
CT size, mm	10.02 (3.54)	14.91 (5.45)	< 0.001	12.52 (5.88)	16.18 (5.18)	0.001
Lung function, n (%)
Normal	98 (75.4)	391 (63.1)		16 (59.3)	36 (26.9)
Impaired	32 (24.6)	229 (36.9)	< 0.001	11 (40.7)	98 (73.1)	0.016
Hematologic indicators
WBCs, 10⁹/L	5.34 ± 1.30	5.40 ± 1.58	0.713	5.51 ± 1.11	5.48 ± 1.82	0.916
RBCs, 10⁹/L	4.18 ± 0.47	4.89 ± 17.87	0.653	4.27 ± 0.61	4.21 ± 0.49	0.553
HGB, g/L	125.8 ± 14.87	126.8 ± 13.92	0.445	126.9 ± 12.91	128.4 ± 15.13	0.621
HCT, %	37.43 ± 4.11	37.69 ± 3.87	0.491	38.31 ± 3.67	38.53 ± 4.30	0.806
MCV, fl	89.7 ± 4.98	90.62 ± 5.42	0.085	90.32 ± 6.88	91.76 ± 5.37	0.228
MCH, pg	30.16 ± 2.17	30.51 ± 2.16	0.098	29.97 ± 2.71	30.61 ± 2.17	0.182
MCHC, g/L	335.92 ± 10.61	336.53 ± 10.78	0.555	331.41 ± 8.88	333.36 ± 9.61	0.332
PLT, 10⁹/L	213.11 ± 57.91	206.78 ± 56.86	0.250	200.9 ± 44.38	204.2 ± 47.25	0.741
NE, 10⁹/L	3.00 ± 1.07	3.07 ± 1.26	0.562	3.08 ± 0.89	3.21 ± 1.65	0.699
LYM, 10⁹/L	1.75 ± 0.50	1.75 ± 0.54	0.984	1.90 ± 0.57	1.69 ± 0.48	0.054
MON, 10⁹/L	0.40 ± 0.14	0.41 ± 0.15	0.702	0.37 ± 0.13	0.39 ± 0.14	0.574
EOS, 10⁹/L	0.15 ± 0.11	0.15 ± 0.14	0.934	0.14 ± 0.12	0.16 ± 0.17	0.509
BAS, 10⁹/L	0.15 ± 1.00	0.03 ± 0.02	0.002	0.06 ± 0.02	0.02 ± 0.01	0.079
Liver function indicators
AST, U/L	21.22 ± 7.00	23.28 ± 15.49	0.138	20.85 ± 5.94	21.38 ± 8.82	0.766
ALT, U/L	20.92 ± 17.60	21.88 ± 13.23	0.482	20.15 ± 11.23	20.47 ± 16.14	0.921
ALP, U/L	64.39 ± 46.73	66.03 ± 20.52	0.529	69.04 ± 22.11	69.98 ± 22.55	0.843
LDH, U/L	183.39 ± 28.20	188.73 ± 31.13	0.071	180.5 ± 29.85	178.3 ± 28.29	0.724
TBIL, umol/L	12.48 ± 4.31	13.09 ± 5.57	0.233	10.76 ± 3.55	12.86 ± 4.60	0.026
DBIL, umol/L	4.45 ± 1.69	4.85 ± 2.02	0.036	3.89 ± 1.31	3.45 ± 1.52	0.026
TB, g/L	63.33 ± 4.88	63.76 ± 4.77	0.348	66.32 ± 6.38	65.52 ± 5.15	0.481
ALB, g/L	39.94 ± 3.33	40.06 ± 3.03	0.692	42.14 ± 3.08	41.24 ± 2.77	0.136
GLO, g/L	25.69 ± 26.84	23.74 ± 3.70	0.083	24.19 ± 4.40	24.28 ± 3.64	0.905
Renal function indicators
BUN, mmol/L	5.06 ± 1.46	5.41 ± 2.28	0.094	5.24 ± 1.35	5.22 ± 1.12	0.929
SCR, µmol/L	60.03 ± 15.35	64.53 ± 16.42	0.004	68.7 ± 15.66	67.02 ± 14.35	0.568
UA, umol/L	298.69 ± 77.25	304.20 ± 78.51	0.466	314.0 ± 78.70	304.5 ± 79.89	0.573
TBA, µmol/L	4.53 ± 3.65	5.37 ± 6.37	0.145	4.39 ± 3.80	4.89 ± 3.35	0.488
CysC, mg/L	0.72 ± 0.15	0.76 ± 0.18	0.005	0.89 ± 0.23	0.89 ± 0.17	0.937
CK, U/L	85.04 ± 46.65	93.41 ± 78.52	0.241	76.5 ± 25.58	91.77 ± 50.36	0.130
eGFR, mL/min	102.16 ± 15.99	96.65 ± 13.93	< 0.001	96.43 ± 14.87	97.39 ± 14.99	0.761
Electrolyte
Mg, mmol/L	0.90 ± 0.08	1.04 ± 3.42	0.643	0.92 ± 0.10	0.89 ± 0.07	0.052
P, mmol/L	1.03 ± 0.13	1.01 ± 0.15	0.216	1.03 ± 0.21	1.02 ± 0.16	0.652
Ca, mmol/L	2.22 ± 0.10	2.22 ± 0.10	0.643	2.25 ± 0.10	2.25 ± 0.10	0.858
Cl, mmol/L	102.37 ± 2.14	102.50 ± 2.16	0.528	102.3 ± 1.98	103.06 ± 2.48	0.183
TCO2, mmol/L	25.59 ± 2.65	26.07 ± 2.70	0.063	25.28 ± 1.98	25.15 ± 1.96	0.761
Blood glucose and lipids
Glu, mmol/L	4.92 ± 1.00	5.10 ± 1.96	0.318	4.95 ± 0.58	5.23 ± 0.97	0.160
TC, mmol/L	4.61 ± 0.87	4.59 ± 0.90	0.788	4.66 ± 0.81	4.46 ± 0.94	0.314
TG, mmol/L	1.26 ± 0.77	1.35 ± 0.90	0.311	1.76 ± 2.07	1.47 ± 1.33	0.352
HDLC, mmol/L	1.37 ± 0.33	1.29 ± 0.34	0.026	1.31 ± 0.39	1.31 ± 0.35	0.909
LDLC, mmol/L	2.66 ± 0.76	2.64 ± 0.78	0.780	2.80 ± 0.75	2.64 ± 0.73	0.302
Coagulation function indicators
TT, seconds	18.28 ± 1.05	18.80 ± 11.01	0.595	17.49 ± 1.41	17.42 ± 1.06	0.799
FIB, g/l	2.86 ± 0.60	2.88 ± 0.65	0.736	2.85 ± 0.61	2.97 ± 0.74	0.410
APTT, seconds	36.07 ± 2.87	35.44 ± 3.57	0.057	36.28 ± 3.74	36.10 ± 3.10	0.796
INR	1.10 ± 0.09	1.09 ± 0.09	0.453	0.98 ± 0.07	0.98 ± 0.07	0.995
PT, seconds	13.16 ± 0.82	13.12 ± 0.82	0.614	12.79 ± 0.64	12.80 ± 0.61	0.946
BMI, body mass index; WBC, white blood cell count; RBC, red blood cell count; HGB, hemoglobin; HCT, hematocrit; MCV, mean corpuscular volume; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; PLT, platelets; NE, neutrophils; LYM, lymphocytes; MON, monocytes; EOS, Eosinophils; BAS, Basophils; AST, aspartate aminotransferase; ALT, alanine aminotransferase; ALP, alkaline phosphatase; LDH, lactate dehydrogenase; TBIL, total bilirubin; DBIL, direct bilirubin; TB, total protein; ALB, albumin; GLO, globin; BUN, blood urea nitrogen; SCR, serum creatinine; UA, urine acid; TBA, total bile acid; CysC, CystatinC; CK, creatine kinase; eGFR, estimated glomerular filtration rate; Mg, magnesium; P, phosphorus; Ca, calcium; Cl, chlorine; TCO2, total carbon dioxide; Glu, glucose; TC, total cholesterol; TG, triglyceride; HDLC, high density lipoprotein cholesterol; LDLC, low density lipoprotein cholesterin; TT, thrombin time; FIB, fibrinogen; APTT, activated partial thromboplastin time; INR, international normalized ratio; PT, prothrombin time.

Overall, 38.72% (508/1312) of patients had lung function impairment. The proportions of patients with lung function impairment in the training, internal validation, and external validation sets were 34.8% (261/750), 67.7% (109/161), and 34.41% (138/401), respectively. There were no significant differences in the mean FVC (110.72% vs. 109.75%, P = 0.545), mean FEV1 (100.09% vs. 98.07%, P = 0.216), mean FEV1/FVC (88.99% vs. 89.21%, P = 0.826), or mean TLC (97.86% vs. 96.73%, P = 0.265) between the non-invasion and invasion groups in the training set. There were no statistically significant variations in the lung function metrics between the two groups in the internal validation and external validation sets. (Table 2 and Supplemental Table 2).

Table 2

Spirometry parameters of all patients in training and internal validation set.
	Training set (n = 750)			Validation set (n = 161)
	Preinvasive (130)	Invasive (620)	P value	Preinvasive (27)	Invasive (134)	P value
FVC (ml)	2.33 ± 0.88	2.27 ± 0.77	0.375	2.69 ± 1.14	2.77 ± 1.00	0.703
FVC (% of predicted)	109.75 ± 15.58	110.72 ± 16.85	0.545	105.03 ± 23.43	106.25 ± 16.54	0.747
FEV1	2.56 ± 0.63	2.55 ± 0.66	0.906	2.42 ± 0.75	2.39 ± 0.68	0.816
FEV1 (% of predicted)	98.07 ± 15.69	100.09 ± 17.20	0.216	93.50 ± 25.13	95.27 ± 17.60	0.661
TLC (ml)	5.14 ± 1.00	5.13 ± 1.02	0.970	4.67 ± 0.82	4.82 ± 0.81	0.572
TLC (% of predicted)	97.86 ± 9.18	96.73 ± 10.80	0.265	66.47 ± 27.49	70.02 ± 26.04	0.522
DLco (mol/min/kPa)	1.46 ± 0.23	1.46 ± 0.23	0.943	1.46 ± 0.23	1.42 ± 0.27	0.650
DLco (% of predicted)	91.82 ± 14.81	96.40 ± 15.71	0.013	93.9 ± 15.3	93.6 ± 17.1	0.952
FEV1, forced expiratory volume in the 1st second of expiration; FVC, forced vital capacity; TLC, total lung capacity; DLco, diffusion capacity for carbon monoxide.

Nomogram development and verification for lung cancer invasion

The least absolute shrinkage and selection operator regression model for identifying predictors had fifty-three variables (Table 1). The number of variables associated with complete response prediction was reduced to eight (Fig. 3). The variables with nonzero coefficients in the univariate analyses were age (P < 0.001), smoking (P = 0.931), tumor size (P < 0.001), lung function (P < 0.001), BAS (P = 0.014), albumin (P = 0.693), direct bilirubin (DBIL) (P = 0.036), and glucose level (P = 0.042) (Table 3). The multivariable logistic regression analysis indicated that age (odds ratio [OR], 1.019; 95% confidence interval [CI], 1.001–1.037; P = 0.044), tumor size (OR, 1.314; 95% CI, 1.228–1.407; P < 0.001), lung function (OR, 3.104; 95% CI, 1.989–4.845; P < 0.001), BAS (OR, 0.514; 95% CI, 0.104–0.873; P = 0.037), and DBIL (OR, 1.152; 95% CI, 1.018–1.304; P = 0.025) were independent risk factors for lung cancer invasion. We selected these five independent risk variables to create a prediction model and a nomogram to estimate the likelihood of lung cancer invasion based on the outcomes of multivariable analyses (Fig. 4). The probability of lung cancer invasion is determined by summing the individual scores for each risk factor and calculating the probabilities associated with the total score. For example, a 60-year-old (2 points) patient with a tumor > 2 cm (7 points), abnormal lung function (10 points), DBIL ≤ 4.4 (0 points), and BAS > 0.01 (0 points) would have a score of 19 points, indicating a probability of lung cancer invasion slightly over 85%.

Table 3

Logistic regression analysis for the factors of invasion selected by LASSO regression in training set.
	Univariate		Multivatiate
	OR (95% CI)	P	OR (95% CI)	P
Age	1.038 (1.022–1.055)	< 0.001	1.019 (1.001–1.037)	0.044
Smoking	0.978 (0.590–1.620)	0.931
Size	1.318 (1.238–1.404)	< 0.001	1.314 (1.228–1.407)	< 0.001
Lung function	2.163 (1.475–3.174)	< 0.001	3.104 (1.989–4.845)	< 0.001
BAS	0.468 (0.138–0.723)	0.014	0.514 (0.104–0.873)	0.037
ALB	1.012 (0.952–1.077)	0.693
DBIL	1.123 (1.007–1.252)	0.036	1.152 (1.018–1.304)	0.025
Glu	1.174 (1.004–1.507)	0.042	1.057 (0.948–1.179)	0.315
OR, odds ratio; 95%CI, 95% confidence index; BAS, Basophils; ALB, Albumin; DBIL, Direct Bilirubin; Glu, Glucose.

Nomogram performance for lung cancer invasion

Patients were classified as high-risk (> 14.0) or low-risk (≤ 14.0) according to the median total points of the nomogram (Fig. 5A-B). Based on lung function impairment, Fig. 5C presents the subgroup analysis of the risk of lung cancer invasion. The nomogram had good predictive power in the training, internal validation, and external validation sets, with areas under the curve of 0.820 (95% CI, 0.781–0.858), 0.758 (95% CI, 0.659–0.858) and 0.838 (95% CI, 0.797–0.879), respectively, for predicting lung cancer invasion (Fig. 6A-C). The calibration curves suggested a good agreement between the predicted and actual lung cancer invasion in both sets (Fig. 6D-F). Decision curve analysis revealed that the nomogram was clinically beneficial for predicting the risk of lung cancer invasion (Fig. 6G-I). The decision curve analysis suggested that using the nomogram for lung cancer invasion adds more benefit than the treat-all-patients scheme or the treat-none scheme in both the training and validation cohorts.

Relationships between lung function and lung cancer invasion

Further, we classified lung function impairment into five categories: obstructive impairment, restrictive ventilatory impairment, diffusion capacity impairment, preserved ratio impaired spirometry, and mixed ventilation impairment (Table 4 and Supplementary Table 3). Patients with obstructive ventilation impairment, restrictive ventilation impairment, or diffusion capacity impairment had higher risks of LUAD invasion. In the multivariable model, the association remained significant after adjusting for age, sex, and body mass index (model 1). The adjusted ORs for obstructive ventilation impairment, restrictive ventilation impairment, and diffusion capacity impairment were 1.88 (95% CI, 1.12–3.15), 3.28 (95% CI, 1.22–10.76), and 2.78 (95% CI, 1.41–5.48), respectively. When adjusted for the factors in model 1 plus smoking status, tumor location, and tumor size, obstructive ventilation impairment (OR, 1.55; 95% CI, 1.08–2.63), restrictive ventilation impairment (OR, 2.80; 95% CI, 1.07–8.24), and diffusion capacity impairment (OR, 2.74; 95% CI, 1.31–5.70) remained significantly associated with tumor invasion. After further adjustment for basophils, albumin, direct bilirubin, and glucose levels (model 3), only diffusion capacity impairment remained significantly associated with invasion (OR, 2.86; 95% CI, 1.43–5.69).

Table 4

Crude and adjusted odds ratios of different pulmonary impairment for invasion in the training set.
	Crude OR (95%CI)	Model 1		Model 2		Model 3
	Crude OR (95%CI)	Adjusted OR (95%CI)	P	Adjusted OR (95%CI)	p	Adjusted OR (95%CI)	p
Obstructive ventilation impairment	2.01 (1.21–3.34)	1.88 (1.12–3.15)	0.017	1.55 (1.08–2.63)	0.038	1.51 (0.89–2.58)	0.129
Restrictive ventilation impairment	3.47 (1.06–11.33)	3.28 (1.22–10.76)	0.027	2.80 (1.07–8.24)	0.032	4.23 (1.00-17.84)	0.050
Mixed ventilation impairment	1.05 (0.23–4.85)	0.91 (0.19–4.28)	0.900	0.89 (0.16–1.39)	0.896	0.74 (0.15–3.60)	0.708
PRISM	1.13 (0.69–1.85)	1.15 (0.70–1.89)	0.586	1.26 (0.74–1.39)	0.397	1.26 (0.73–2.18)	0.411
Diffusion capacity impairment	2.70 (1.38–5.32)	2.78 (1.41–5.48)	0.003	2.74 (1.31–5.70)	0.007	2.86 (1.43–5.69)	0.003
OR, odds ratio; 95%CI, 95% confidence index; PRISM, preserved ratio impaired spirometry.
Model 1 adjusted for age, gender, and body mass index. Model 2 adjusted for model 1 plus smoking status, location, and size. Model 3 adjusted for model 2 plus basophilic, albumin, direct bilirubin, and glucose.

Restrictive ventilation impairment is a common comorbidity of lung cancer, and their interaction poses significant clinical challenges. Patients with restrictive ventilation impairment are three to six times more likely to develop lung cancer than those with normal lung function. The prognosis of lung cancer positively correlates with the severity of chronic obstructive pulmonary disease; therefore, attention needs to be paid to the progression and decline of lung function. In this retrospective study, we found a significant association between lung function impairment and early-stage lung adenocarcinoma invasion: 38.72% of patients with stage I lung adenocarcinoma have different types of lung dysfunction. This study demonstrated that obstructive ventilation, restrictive ventilation, mixed ventilation, preserved ratio impaired spirometry, and diffusion capacity impairment were associated with a higher risk of lung cancer invasion and highlighted the effects of PFTs in predicting lung cancer invasion. Our prediction model for lung cancer invasion included age, tumor size, lung function, basophil count, and direct bilirubin level and showed good discrimination and calibration in both the training and validation sets. To the best of our knowledge, this is the first study to discuss the connection between various forms of lung function impairment and invasion in LUAD. These good predictive performances increase the clinical capacity to identify AIS/MIA from IAD.

Increasing research has elucidated the substantial correlation between the development of lung cancer and the decline of lung function [24]. A large study reported that FEV1 < 80% in the presence of airway obstruction was a risk factor independent of lung cancer development [25]. The performance of a lung cancer prediction model for individuals who had never smoked was improved with the addition of lung function as a covariable [26]. A cohort study conducted in South Korea revealed that even slight and moderate impairments in lung function are associated with a higher chance of developing lung cancer and a significant attributable risk [12]. The shared mechanisms for lung function impairment and lung cancer development include significant carcinogenic exposure, chronic inflammation-induced remodeling and thickening of the bronchiolar wall, production of genotoxic reactive oxygen species, and inflammatory gene variation in the inflammation-cancer transition [27]. Several supplementary pathological characteristics, including secondary histological patterns [28], nuclear grade [29], mitotic grade [30], presence of dissemination across air gaps [31], and necrosis [32], have been demonstrated to possess predictive significance. As the evolution of airway function and pathology may be related, it may be feasible to evaluate airway function using PFTs to predict lung cancer invasion, even when it is difficult to evaluate lung cancer invasion by microcosmic means.

Numerous studies have explored the clinical and pathologic features of AIS and MIA [33, 34], with a focus on 5-year recurrence-free survival after resection [35, 36]. Due to the increasing prevalence of screening using low-dose CT, early-stage tumors should be detected more frequently. The histological features of tumors are the gold standard for determining tumor type and invasion. However, the power of histological characteristics of tumors for prognosis prediction is limited [37]. Additionally, the identification of histological features is dependent on pathologists, whose assessment varies according to their experience and specialization. Therefore, the reproducibility of histologic pattern assessments can be challenging [38]. Supplementing the preliminary evaluation and prediction of lung cancer invasion with a nomogram model before surgery will help achieve better postoperative management and treatment for LUAD. Patients may benefit from the changing landscape of emerging management and optional treatment options. Our study provides a landmark for performing preoperative PFTs to predict LUAD invasion, and our nomogram had an area under the curve of 0.82 in the internal training set.

Since LE can reduce morbidity and improve patient outcomes when compared to pneumonectomy, it has become the standard surgical approach for lung cancer. SE has become more popular for AIS/MIA as it has the advantage of preserving more lung parenchyma; however, SE is generally only indicated for patients with small tumors and no lymph node invasion [39]. Although several studies have shown contradictory findings about the relationship between the extent of lung tissue removed and the decline of lung function, some have reported that SE offers preservation of PF compared with open LE [40]. A large retrospective observational study that included 1284 patients demonstrated that the decline in pulmonary function was significantly more pronounced following LE compared to SE [41]. Harada et al. reported that patients with non-small cell lung cancer who underwent SE had significantly better functional preservation than those who underwent LE, and the extent of removed lung parenchyma was strongly related to the decline of FVC and FEV1 at follow-up [42]. Preoperative pulmonary function may be associated with invasive lung cancer, and surgical methods may further increase loss of lung function. Once the patient is on the operating table, the surgical method has already been decided based on the intraoperative pathological examination. Therefore, adequate evaluation of lung cancer invasion before surgery will help with treatment decisions, allowing for the optimal surgical type and thereby improving patient outcomes.

An important finding of our study was establishing a quick and easy prediction model that provides a new supplement for the preliminary evaluation of pulmonary adenocarcinoma invasion, assisting in preoperative and postoperative management for LUAD. The present study demonstrates the role of PFTs, a noninvasive, low-cost, and rapid examination, in identifying individuals at high risk of lung cancer invasion. Radiomics technology can accurately detect early-stage lung adenocarcinomas. This method can semiautomatically and objectively discriminate the less-invasive lung adenocarcinomas suitable for sublobar resection by applying voxel-based histogram analysis to 3D-CT images [43]. The highly accurate method can assist physicians in decision-making and treatment planning. Chen et al. [44] found that tumor size and solid component size are the two most important CT factors for identifying pathological tumor invasion. Our study also demonstrated that tumor size measured by CT can independently predict lung adenocarcinoma invasion. With the increasing prevalence of screening using low-dose CT, an increasing number of early-stage tumors are being detected more frequently. Combined CT and PFT screening in this high-risk population can offer better therapy and patient management guidance.

In conclusion, this study proposed a new prediction model for IAD. The scoring methodology and practicality of the prediction model enable its rapid and simple integration into regular clinical practice since they eliminate the need for the doctor to acquire new technological skills. The model provided a new supplement for predicting pulmonary adenocarcinoma invasion and was reproducible in the training and validation sets. In clinical practice, this prediction model will provide doctors with a standard tool to utilize, and it will also help researchers assess prognostic and/or predictive indicators in early-stage adenocarcinoma. A multidimensional approach for the assessment of a higher individual risk of lung cancer invasion may improve future prevention strategies, early detection approaches, and clinical management of this lethal disease.

More than a third of patients with lung cancer have lung function impairment, and lung function impairment may be a potential biomarker of lung cancer invasion. Combined CT and PFT screening in this high-risk population can offer better guidance for patient management. Our prediction model can serve as a multidimensional tool to comprehensively predict lung cancer invasion.

Acknowledgements

We are grateful to Yugang Hu for helping us in the data analyzing. We would like to thank Editage for English language editing.

Author contributions

Yu Liu, Xueyun Tan, Guanzhou Ma, Dong Zhao, lead authors, had full access to all of the data in the study and contributed to the data collection and interpretation, and writing of manuscript. All authors contributed to data collection, interpretation and writing of manuscript. Sufei Wang and Yang Jin, corresponding authors, contributed to the study design, data analysis and interpretation, and the writing of the manuscript. All authors have read and approved the final manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (No. 82330003; No. 82172034).

Availability of data and materials

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

Consent for publication

Not applicable.

Competing interests

The authors declared that they have no conficts of interest.

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No competing interests reported.

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Pulmonary function tests for identifying pathological invasive stage I lung adenocarcinoma: A multiple-center retrospective cohort study

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Abstract

Background

Methods

Results

Conclusions

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Introduction

Study Design and Methods

Participants

Spirometry

Histologic evaluation

Statistical analysis

Results

Baseline characteristics of study participants

Nomogram development and verification for lung cancer invasion

Nomogram performance for lung cancer invasion

Relationships between lung function and lung cancer invasion

Discussion

Conclusions

Declarations

References

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