|Year : 2020 | Volume
| Issue : 7 | Page : 1569-1574
Correlation between computed tomography imaging and pathological stages and subtypes in early lung adenocarcinoma
Xingchen Shang1, Benchuang Hu2, Feng Gao2, Wangang Ren2
1 Department of Breast and Thyroid Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
2 Department of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
|Date of Submission||31-May-2020|
|Date of Decision||04-Aug-2020|
|Date of Acceptance||07-Sep-2020|
|Date of Web Publication||9-Feb-2021|
Department of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 9677 Jing 10 Road, Ji'nan 250000, Shandong Province
Source of Support: None, Conflict of Interest: None
Background: Detection of early-stage lung cancers has increased due to computed tomography (CT). The pathological stages and subtypes of early lung cancer determine the treatment strategy. We aimed to investigate the correlation between CT characteristics and pathological status in early lung adenocarcinoma (ADC).
Subjects and Methods: Between June 2018 and December 2019, 415 consecutive patients who underwent surgery for lung ADC with pathological atypical adenomatous hyperplasia (AAH) and ADC in situ (AIS), T1a (mi) N0M0, and T1a–cN0M0 were analyzed. The relationship between CT imaging and pathological status was investigated using Chi-squared or Kruskal–Wallis test and binary logistic regression.
Results: When cases of AAH, AIS, and T1a (mi) N0M0 were used as the control group, the lesion size, solid component ratio (SCR), and spiculation were significantly and independently associated with T1a-cN0M0 (P < 0.01). SCR >50% (P < 0.01) and spiculation (P < 0.05) were significantly and independently associated with T1aN0M0. In cases of pathological T1a-cN0M0, SCR >50% was significantly different between adherent wall growth ADC and mucinous ADC (P < 0.01).
Conclusions: Some CT characteristics are related to the pathological stage and subtypes of early lung ADC. Larger diameter, spiculation, and SCR >50% are associated with invasive ADC. SCR >50% is positively correlated with mucinous ADC and negatively with adherent growth ADC.
Keywords: Computed tomography imaging, lung cancer, pathological stage, pathological subtype
|How to cite this article:|
Shang X, Hu B, Gao F, Ren W. Correlation between computed tomography imaging and pathological stages and subtypes in early lung adenocarcinoma. J Can Res Ther 2020;16:1569-74
|How to cite this URL:|
Shang X, Hu B, Gao F, Ren W. Correlation between computed tomography imaging and pathological stages and subtypes in early lung adenocarcinoma. J Can Res Ther [serial online] 2020 [cited 2021 Jun 15];16:1569-74. Available from: https://www.cancerjournal.net/text.asp?2020/16/7/1569/308772
| > Introduction|| |
Lung cancer has emerged as a leading cause of cancer-associated death worldwide over the recent 10 years. Non-small cell lung carcinoma (NSCLC) accounts for 80% of lung cancers and can be mainly subclassified into adenocarcinoma (ADC), squamous cell carcinoma, and large cell carcinoma. In recent years, there have been significant changes in the disease spectrum of lung cancer, and the incidence of ADC has increased significantly., The pathological classification of lung cancer is being constantly updated. In 2015, the World Health Organization established a new classification for lung ADC., Lung ADC is classified as preinvasive lesion, minimally invasive ADC (MIA), and invasive ADC (IAC). Preinvasive lesions include atypical adenomatous hyperplasia (AAH) and ADC in situ (AIS). The subtypes of IAC include lepidic predominant ADC (LPA), acinar, papillary, micropapillary (MIP), invasive mucinous ADC, and solid predominant with mucin ADCs.
In recent years, large number of early-stage lung cancers is being detected due to the advances in screening techniques. Timely diagnosis and accurate treatment strategies in the early stage of lung cancer can significantly improve the survival of patients., Computed tomography (CT) is characterized by rapid, clear, and volume-based imaging, which is of unquestionable value in the detection and diagnosis of early lung cancer. The pathological stages and subtypes of early lung cancer aid in determining the surgical strategy. It is unclear if CT characteristics can predict the pathological subtype and stage in early lung cancer, which can play an important role in clinical decision-making.
The aim of this study was to investigate the correlation between CT imaging characteristics and pathological status in early lung ADC.
| > Subjects and Methods|| |
Between June 2018 and December 2019, 559 patients with lung ADC underwent surgical resection in the thoracic department of Shandong Provincial Hospital.
We reviewed the hospital records of all patients who underwent surgery for ADC at the Department of Thoracic Surgery, Shandong Provincial Hospital, between June 2018 and December 2019. The inclusion criteria were the following: (1) pathologically confirmed primary lung ADC; (2) pathological AAH, AIS, T1a (mi) N0M0, or T1a-cN0M0; (3) R0 resection; and (4) at least 1-mm thin CT imaging performed preoperatively. The exclusion criteria were the following: (1) T1a-cN0M0 without pathologic subtype data; (2) missing image data; and (3) previous chemoradiotherapy, targeted therapy, or other conservative treatment. The study was approved by the Medical Ethics Commission of Shandong Provincial Hospital (No. 2019-214), and all patients signed informed consent.
CT scans were performed in deep inspiration at total lung capacity; 1-mm CT slices were used to evaluate the lung nodules in the lung window. Image analysis included nodule size, spiculation, pleural indentation, cavitation, solid component ratio (SCR), and location of the lung segment. The largest long-axis diameter was measured using multiplanar reformation in the lung window. Ground glass shadow was defined as slight increase in density in the CT image with the internal bronchovascular texture still visible. Consolidation component was defined as an area of increased opacification that completely obscured the underlying vascular structures. SCR was calculated by dividing the maximum consolidation diameter by the maximum tumor dimension in the lung window. SCR grouping was based on 50%. CT data were analyzed by consensus of two radiologists with more than 5 years of experience in CT, in a random order and without access to the patients' clinical information.
Surgically resected specimens were fixed in formalin, embedded in paraffin, sectioned with a microtome, and stained with hematoxylin and eosin. Histologic subtypes and stages of lung ADCs were classified according to the eighth edition of the TNM classification. The histological patterns include LPA, acinar, papillary, MIP, solid, infiltrating mucinous ADC, and intestinal ADC. Cases of comprehensive histologic subtyping were not included in this study. The pathological sections were reviewed by two pathologists. Disagreements were resolved by consensus.
Age, sex, smoking history, lesion size, SCR, spiculation, pleural indentation, cavitation, and pathologic subtype and stage were analyzed. Univariate analysis was used in both continuous and categorical variables. Continuous variables were reported as median and interquartile range for nonnormal distribution. Intragroup comparisons were performed using Chi-squared test or a nonparametric test, such as Kruskal–Wallis test. Intergroup comparisons were performed using Fisher's exact test if any expected cell count was <5.
Multivariable analysis was performed using binary logistic regression. All significant variables in univariate analysis were inserted in this model. Stepwise regression analysis was performed. Statistical analyses were performed using SPSS 17.0 (SPSS Inc., Chicago, IL, USA). P < 0.05 indicated statistical significance.
| > Results|| |
Overall, 415 consecutive patients (161 men, 254 women; mean age of 57.2 ± 9.2 years, range, 31–82 years) with ADC were eligible for inclusion in this study. The baseline characteristics of the patients are summarized in [Table 1]. In these cases, lymph node metastasis was not detected in patients with microinvasive ADC; therefore, we abbreviated T1a(mi) N0M0 as MIA.
|Table 1: Clinical and pathologic characteristics of the early lung adenocarcinoma|
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Correlation between pathological stages, imaging features, and clinical data
- All cases were separated into two groups based on the pathological stage. Group 1 included cases of pathologically confirmed AAH, AIS, and MIA. Group 2 included IAC cases of T1a-cN0M0. Age, smoke history, lesion size, SCR, spiculation, and pleural indentation were significantly different between Group 1 and Group 2. IAC was significantly associated with a history of smoking, advanced age, CT characters of larger diameter, spiculation, pleural indentation, and SCR >50%. Comparisons of sex and cavitation did not reveal significant differences between Group 1 and Group 2. The results are summarized in [Table 2]
- We excluded the data of T1b-cN0M0, while other data were divided into two groups. Group 1 included cases of pathologically confirmed AAH, AIS, and MIA. The T1aN0M0 cases were grouped into Group 2. SCR, spiculation, and pleural indentation were significantly different between Group 1 and Group 2. The rates of SCR >50% (24%), spiculation (44%), and pleural indentation (30%) were higher in Group 2. Comparisons of sex, age, smoke history, lesion size, and cavitation did not reveal significant differences between Group 1 and Group 2. The results are summarized in [Table 3].
|Table 2: Correlation between pathological stages, imaging features and clinical data (atypical adenomatous hyperplasia/adenocarcinoma in situ/ minimally invasive adenocarcinoma vs. invasive adenocarcinoma)|
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|Table 3: Correlation between pathological stages, imaging features and clinical data (atypical adenomatous hyperplasia/adenocarcinoma in situ/minimally invasive adenocarcinoma vs. T1aN0M0)|
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Correlation between pathological subtypes, imaging features, and clinical data in invasive adenocarcinoma
On excluding the comprehensive histologic subtyping, the following five histological subtypes across 160 cases were available for statistical analysis: LPA, acinar, papillary, solid, and mucinous ADC. Comparisons of the baseline characteristics and CT characteristics demonstrated that only SCR was significantly different between the histological subtypes. The results are summarized in [Table 4].
|Table 4: Correlation between pathological subtypes, imaging features and clinical data of invasive adenocarcinoma|
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Predictors of pathological stage
Multivariable analysis was performed using binary logistic regression analysis to identify independent predictors for all causes of the pathological stage.
- Group 1 included cases of pathologically confirmed AAH, AIS, and MIA. Group 2 included IAC cases of T1a-cN0M0 Multiple variables including lesion size, SCR, and spiculation were significantly and independently associated with IAC, as summarized in [Table 5]. The probability of IAC increased by 4.07 times (95% confidence interval [CI], 2.59–6.41) with 1-cm increase in lesion size. Lesions with SCR >50% were 17.86 times (95% CI, 6.25–52.63) more likely to be IAC than lesions with SCR ≤50%. The odds ratio for IAC was 2.84 (95% CI, 1.398–2.376) for the spiculated ones compared with nodules without spiculation.
- Group 1 included cases of pathological AAH, AIS, and MIA. Group 2 included cases of T1aN0M0
Statistical analysis revealed that SCR and spiculation were significantly and independently associated with T1aN0M0, as summarized in [Table 6]. Lesions with SCR >50% were 9.9 times (95% CI, 2.83–34.48) more likely to be T1aN0M0 than lesions with SCR ≤50%. Lesions with spiculation were 2.15 times (95% CI, 1.02–4.55) more likely to be T1aN0M0 than lesions without spiculation.
|Table 5: Predictors of pathological T1a-cN0M0 compared with cases of atypical adenomatous hyperplasia/adenocarcinoma in situ/minimally invasive adenocarcinoma|
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|Table 6: Predictors of pathological T1aN0M0 compared with cases of atypical adenomatous hyperplasia/adenocarcinoma in situ/minimally invasive adenocarcinoma|
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Predictors of pathological subtypes
Univariate analysis revealed that only SCR was significantly different between the histological subtypes. We used binary logistic regression analysis to identify SCR for each histological subtype. Adjustments for age, sex, and smoking history were also performed in a separate model to assess their effects on the association between SCR and histological subtype. SCR was actually accounted for more than 50% in all five solid ADC cases. The results of other histological subtypes are summarized in [Table 7]. There were significant statistical differences between LPA and mucinous ADC. Lesions with SCR >50% were unadjusted 0.16 times (95% CI, 0.06–0.44) more likely to be LPA than lesions with SCR ≤50%. Lesions with SCR >50% were unadjusted 4.95 times (95% CI, 1.48–16.67) more likely to be mucinous ADC than lesions with SCR ≤50%.
|Table 7: Predictor (solid component ratio >50%) of pathological subtypes|
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| > Discussion|| |
With advances in screening techniques, especially CT, lung cancer can now be detected in its early stages. CT can effectively detect early lung cancer and verify the pathological changes in the location and scope of involvement., The National Lung Cancer Screening Trial in the United States demonstrated that low-dose CT screening can reduce the mortality rate of lung cancer in the high-risk population by 20%, and that it is the most effective lung cancer screening tool currently., With more number of cases of early lung cancer being identified, the surgical treatment of lung cancer has also become diversified. The stage and pathological subtype of lung cancer affects the surgical protocol. In general, patients with AIS and MIA experience lower risk of recurrence than those with IAC. The MIP and solid (SOL) predominant subtypes have poorer prognoses than other subtypes., Lobectomy is still considered the standard-of-care for IAC subtypes. However, some studies have demonstrated that sublobectomy can achieve the therapeutic effects of lobectomy in patients with preinvasive lung cancers, multiple primary lung cancers, and stage IA NSCLC ≤2 cm.,,
The identification of the pathological status is of great significance in the selection of the surgical methods. However, the current methods have some shortcomings. AIS and MIA cannot be diagnosed in small biopsies or cytology specimens., Furthermore, it is difficult to identify the histological subtypes using cytology specimens. Intraoperative frozen sections are reliable for identifying the invasion status of lung ADC (accuracy is approximately 95%). However, frozen sections have low sensitivity in differentiating the histological subtypes of MIP and SOL patterns (37% and 69%, respectively). In some cases, surgeons need to first perform wedge resection or segmentectomy to obtain specimens for frozen pathological examination, and then some patients need further lobectomy, which increases the operation time, trauma, and resource consumption.
Some CT characteristics, such as size, SRC, spiculation, pleural indentation, vacuolation, and lobulation may predict lung cancer and reflect the pathological status. Tumor size is one of the strongest predictors of tumor progression and prognosis in lung cancer as indicated by the TNM staging system. Greater proportion of SCR is well-known to be strongly associated with invasive lung ADC., Tsutani et al. reported that the solid area diameter is more effective in predicting high-grade malignancy and prognosis when compared with whole nodule diameter., Spiculation is the proliferation of the fibrous connective tissue caused by invasion of or irritation due to lung cancer. This study explored which CT characteristics were different in early lung ADC. In this study, we listed T1a (mis) N0M0 in the same group as AAH and AIS because T1a (mi) is defined as single ADC ≤3 cm with clear boundaries and mainly adherent growth and maximum diameter of infiltrating stroma ≤5 mm. If MIA is completely excised, the overall 5-year survival rate is 100%.
First, we explored the relationship between the pathological stages and imaging features in early lung cancer. Cases of AAH, AIS, and MIA were used as the control group. Multivariable analysis revealed that lesion size, SCR, and spiculation were significantly and independently associated with T1a-cN0M0. This is consistent with some previous reports. Cases of T1aN0M0 were the earliest lesions in the IAC group. We further compared the controls with cases of T1aN0M0. Our findings revealed significant differences in SCR and spiculation between the control and T1aN0M0 groups. Lesions with SCR >50% and spiculation were more likely to be T1aN0M0 cases. Second, we explored the relationship between the pathological subtypes and imaging features in early lung cancer. Only SCR was significantly different between the histological subtypes on univariate analysis. The adherent wall growth ADC has greater proportion of ground-glass opacity, while mucinous ADC has greater proportion of solid component in T1a-cN0M0 stage.
There were some limitations to our study. First, it is limited by a selection bias because we included surgically proven cases only. Our study might not represent the true relationship between CT characteristics and pathological status in early lung ADC. Second, the number of patients with early lung ADC in our study was small. There were only 5 cases of solid ADC. A larger sample data are needed to investigate the correlation between CT characteristics and pathological status of lung cancer. Meanwhile, these data should be updated with the changes in the disease spectrum in lung cancer.
| > Conclusions|| |
Some CT characteristics are related to the pathological stage and subtypes in early lung ADC. Larger diameter, spiculation, and SCR >50% are associated with IAC. SCR >50% is positively correlated with mucinous ADC and negatively with adherent growth ADC.
Financial support and sponsorship
This work was supported by the National Natural Science Foundation of Shandong province (grant number ZR2019BH072).
Conflicts of interest
There are no conflicts of interest.
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[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7]