Lung cancer is a disease that seriously affects human health. Approximately 85% of patients are non–small cell lung cancer (NSCLC), of which adenocarcinoma (AC) and squamous cell carcinoma (SC) are the most common subtypes (1, 2). Histological classification of lung cancer has been demonstrated as an independent prognostic indicator. Because there are significant differences in biological behavior, treatment strategies and prognostic evaluation between different pathological subtypes of lung cancer (3, 4), a reasonable choice of treatment strategies can reduce mortality, prolong the survival time, and improve the quality of life.

Chest CT is the preferred imaging examination for the diagnosis of lung cancer. Traditional CT can evaluate the benign and malignant lesions according to their morphological features, intensity, and lymph node metastasis. However, it is difficult to evaluate the pathological subtypes of lung cancer because of the lack of quantitative indicators. If morphological criteria did not help to distinguish benign from malignant lung lesions, then it is strongly dependent on invasive pathologic examination or follow-up studies (5). As a non-invasive and effective pathological classification method of lung cancer, energy spectrum CT has become a hot spot in clinical research. The basic structure of the new generation of spectral CT is similar to that of ordinary CT, but it has two spatially equivalent detectors: the upper layer only absorbs low-energy photons, whereas the lower layer absorbs high-energy photons. On the premise of a perfect match in time and space within the projected data domain, high-energy and low-energy data can be parsed to obtain both traditional CT images and spectral images (6, 7).

Previous studies on energy spectrum CT in SC and AC are insufficient and controversial. Fehrenbach et al. (8) considered that only normalized iodine density (NID) in the arterial phase (AP) was significant for the differential diagnosis of NSCLC. Wang et al. (9) showed that the slope of the spectral curve (λ) of 40 to 70 keV and iodine density (ID) in AP had diagnostic value in differentiating SC from AC, but the venous phase (VP) parameters were not included in the study. In contrast, Li et al. (10) showed that VP-ID can differentiate SC from AC by reflecting tumor microvessel density. Jia et al. (11) showed that AP-λ (40–100), AP-Zeff, AP-ID, and VP-ID were significant for their identification, but these data of SC were greater than that of AC. Problems, such as single-phase scanning, single spectral parameters, and the exclusion of morphological parameters, have caused a lot of controversies.

This study improves the above issues. The purpose of this study was to investigate the diagnostic value of spectral parameters of DLCT in determining efficacy to distinguish AC and SC, and their combined diagnostic efficacy was also analyzed.

Our results showed that the spectral parameters of the VP have a higher diagnostic value in differentiating SC from AC than those of the AP. Morphological parameters also have important diagnostic value, in which distribution, air bronchus, and vacuole sign are associated with AC. The joint diagnosis efficacy is higher than that of single index, and the joint diagnosis of spectral parameters and morphological parameters had the best efficacy. Therefore, the combined diagnosis of energy spectral parameters and morphological parameters is more effective and instructive for the pathological classification of lung cancer.

CT is widely used in the diagnosis of lung diseases. In recent years, spectral CT has become a hot spot in the study of lung cancer. However, in diagnostic studies of lung cancer (8–11), most researchers were keen to study the optimal diagnostic performance of quantitative parameters, ignoring the morphological features. Therefore, we have supplemented the study of the morphological features of tumors. Our results suggest that the morphological features of tumors can be used for histological inference of AC and SC, although some of the signs are not significant. Several previous studies have compared the clinical and imaging features of lung cancer of different pathological types. Chu et al. (14) showed that the radiologic and microscopic findings correlate well with each other and are closely associated with tumour prognosis. Understanding their morphological features is helpful for early identification and diagnosis. Wang et al. (15) have shown that AC and SC have different clinical and imaging characteristics and that imaging features are useful for differential diagnosis. Koenigkam et al. (16) found that SC more likely to occur in male patients, more frequently found in the central distribution, and less lobulation, burr, and air bronchogram. In addition, previous studies did not analyze the contrast enhancement between AC and SC, but we did a detailed analysis. We found the differences between them and verified the diagnostic value of morphological features.

Conventional CT is used to differentiate subtypes of tumors by their size, shape, and attenuation value. Although it has some advantages, the accuracy of diagnosis is still limited (11, 17). The spectral parameters provide quantitative analysis for histological diagnosis of NSCLC, and its accuracy is higher than that of conventional CT, making up for the deficiency of conventional CT. There are several reasons: i. From the perspective of histology, AC is a malignant epithelial neoplasm of glandular differentiation, in which glandular tubular and glandular luminal–like structures are common, and it contains more microvessels with abundant blood supply (18). Whereas, SC is defined as keratinized malignant epithelial tumor, which mostly grows in a stacked pattern, and its internal structure is dense, with frequent tumor nests, keratinized beads, and intercellular bridges, and the internal part is mostly accompanied by liquefactive necrosis (18, 19). ii. The degree of tumor enhancement depends only on the amount of contrast medium in the tumor vessels. The central type of lung cancer is mainly SC, mainly supplied by bronchial artery, whereas the peripheral type of lung cancer is mainly AC with dual blood supply of bronchial artery and pulmonary artery (20, 21). Because the blood supply from the bronchial artery is later than that from the pulmonary artery, the VP better reflects the blood supply of the tumor.

ID reflects intravascular blood flow distribution and vascular status by quantitative analysis of iodine content, but it is influenced by many factors, including cardiac output and blood volume of the patient, concentration and flow rate of the contrast medium, and dose and rate of injection (20). NID was defined as the ratio of tumor ID to that of the aorta or subclavian arteries at the same level, and most researchers (8, 21–23) state that NID can reduce the effect of individual circulatory variability on tumor iodine content and thus more accurately reflect the blood supply of the lesion. However, in this comparison of AC and SC, we found that ID is more effective than NID in both the AP and the VP, which is consistent with the results of the study by Li et al. (10). This suggests that NID depends on the extent of lesion and aortic enhancement, and changes in aortic ID may cause NID to deviate.

Energy spectrum curve is the curve of material decay varying with x-ray energy, which reflects the decay characteristics of material. Theoretically, each substance has its own specific spectrum curve, and the slope of the spectrum curve decreases with the expansion of its range (24). The K edge of iodine is 33.2 keV, so the lower the keV, the higher the CT value (25). From IQon, monochrome images of the 40- to 200-keV energy range can be obtained. However, not all images can be used to observe and diagnose lesions, because image quality varies at different energy levels. Low–kilo–electron volt images can improve tissue enhancement, increase tissue contrast, and make the display of small lesions clearer. Equivalent kilo–electron volt images can reduce image noise and improve image quality. This study shows that VP-λ is conducive to the differentiation of lung SC and AC. Jia et al. (9) reported that the spectral parameters of SC in the AP and the VP were larger than those of AC, and this is different from our experimental results.

Zeff is the atomic number of an element that has the same decay coefficient as a compound or mixture and can be used to identify the tissue composition of a substance, especially in substances with similar CT values. It is a quantitative indicator of different substances (11, 26). Moreover, Zeff can indirectly provide information about the accumulation of contrast media (27). Previous studies have shown that Zeff can identify substances (28, 29). In the differential diagnosis between AC and SC, Zeff in VP was greater than that in AP. This is related to the blood supply of the tumors described above, their histological characteristics, the growth pattern of the tumors, and the changes of their surrounding microenvironment.

In this study, we provide not only quantitative parameters for tumor enhancement but also qualitative parameters for morphological features. The diagnostic ability of combining morphological features with quantitative parameters, especially DLCT, has been significantly improved, which confirms the additional diagnostic value of quantitative analysis of spectral parameters. Although there are more and more studies on quantitative data, CT morphology of lung cancer remains an important indicator of cancer diagnosis. On the one hand, they are closely related to the growth characteristics of tumors and easy to collect. On the other hand, in the era of precision medicine and big data, a single spectral parameter may also be considered unreliable.

The limitations of this study are as follows: (1) because of the small sample size, this study failed to stratify the degree of differentiation of tumors; (2) unenhanced spectral parameters were not included in the study; (3) not all pathological sections were matched with imaging ROI; and (4) some patients’ unenhanced images were replaced by virtual unenhanced images, which may affect our results.

In summary, the combination of morphological features and DLCT spectral parameters improved the diagnostic efficiency in distinguishing SC from AC. This may help clinicians develop initial treatment strategy and prognostic predictions. Although relatively accurate pathological diagnosis can be obtained by invasive methods, complications or patient intolerance still exist.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving human participants were reviewed and approved by Nanxishan Hospital of Guangxi Zhuang Autonomous Region Institution Review Committee [NO.2020(KY-E-30)]. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

First author: RM. Co-author: ZM; ZG. Other authors: XQ; GH; XY; HJ; PY; MD; XDZ. Guarantor and correspondent: XQZ. All authors contributed to the article and approved the submitted version.

The authors thank the dedicated this project participants, their loved ones, and the devoted staff and trainees who contributed to recruitment, screening, and enrollment of the cohort.

Author XZ is employed by Philips China Investment Co.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.