Feasibility and efficiency of ultrahigh-frequency ultrasound for intraoperatively localizing early lung cancer presenting as ground-glass opacity in lung tissue: a prospective study

Introduction

Lung cancer is a leading cause of mortality worldwide and the International Agency for Research on Cancer projected 2.3 million new lung cancer cases and 1.8 million associated deaths worldwide for 2020 (1). With the widespread use of low-dose chest computed tomography (CT) scanning, lung cancer is being increasingly detected at an early stage with the appearance of ground-glass opacities (GGOs) (2,3). The rate of discovery for GGOs in patients with lung cancer is approximately 20% (4); however, data from a screening study among Chinese hospital staff indicated that 95.5% of patients with screening-detected lung cancer present with GGOs (5). Surgery is the main treatment for suspected malignant GGOs (6,7), and early resection of GGOs with malignant signs can improve the survival rate of patients (2). Thus, the accurate localization of targeted early lung cancers manifesting as GGOs is a critical step in lung surgery.

Currently used clinical preoperative and intraoperative localization technologies, such as CT-guided percutaneous and intraoperative electromagnetic navigation bronchoscopy, primarily involve preresection localization. However, several problems can be encountered in this surgical process. When preoperative localization fails, such as in cases of negative fluorescence or inaccurate positioning, there is no substitute method for intraoperative, real-time, assisted localization. Moreover, despite the strengths of preoperative localization techniques, localization of GGOs in excised lung tissue remains difficult due to the similarity in texture between GGOs and normal lung parenchyma and to the lack of distinguishable features in GGOs specimens, especially those ≤1 cm. For example, even with precise wedge resection or segmentectomy guided by preoperative localization,there remains a possibility of GGOs being within the excised pulmonary tissue being missed, thereby necessitating lobectomy. The prevailing approach for GGO localization in excised lung tissue is examination by traditional palpation, which distinguishes between normal lung tissue and lesions based on variations in textures (8,9). Therefore, there is a need to develop a method that can provide accurate intraoperative detection and localizatiion of early lung cancers presenting as GGOs in excised lung tissue.

In 1999, Santambrogio et al. (10) were the first to apply intraoperative ultrasound to examine pulmonary nodules, and since then, several studies have validated the safety and efficacy of intraoperative ultrasound for detecting pulmonary nodules (11-14). However, the primary focus of this research has been on solid or large nodules (maximum diameter >2 cm) with a 5- to 12-MHz ultrasound transducer, with less attention given to the detection rate of small GGOs (≤1 cm). Compared with 5- and 7.5-MHz ultrasound, 12-MHz ultrasound has demonstrated a superior ability to detect pulmonary nodules (15). Ultrahigh-frequency ultrasound (UHFUS) involves higher frequency (≥20 MHz) ultrasound waves (16) and higher resolution, with levels as fine as 30 µm, which may have the potential to satisfy the requirements of intraoperative GGO imaging. This study aimed to assess the feasibility and efficiency of UHFUS for intraoperatively localizing early lung cancers presenting as GGOs in excised lung tissue and to summarize the relevant features. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-448/rc).

Methods

Study design

This single-center, prospective, observational clinical trial (http://www.clinicaltrials.gov, No. NCT05994898) was designed to evaluate the value of intraoperative UHFUS for detecting GGOs in excised lung tissue (Figure 1). This study was approved by the Institutional Review Board of Peking Union Medical College Hospital (PUMCH) (approval No. K23C2063). This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Figure 1 Flowchart and criteria for patient enrolment and summary of major findings in the study. GGO, ground glass opacity; UHFUS, ultrahigh-frequency ultrasound; VATS, video-assisted thoracoscopic surgery.

Participants

Consecutive patients with suspected malignant GGOs who underwent video-assisted thoracoscopic surgery (VATS) were enrolled at PUMCH between June 4, 2023 and March 31, 2024. Patients with suspected lung cancer detected by chest CT scan and with at least one GGO lesion (including multiple pulmonary nodules) present in the designated surgical area, were included. A standard surgical protocol devised according to the National Comprehensive Cancer Network guidelines (version 2, 2023) was followed, and the tumor stage was reported according to the American Joint Committee on Cancer eighth edition tumor-node-metastasis (TNM) staging system (17,18). The exclusion criteria were as follows: patients declined enrollment, emphysema, and compromisedblinding (e.g., cases in which the pleural surface of nodules was burned by an intraoperative electric hook). Full preoperative assessments were completed, and informed consent was obtained from all participants (HFUSGGO1 version 3.0). We assumed that the expected sensitivity of ultrasound and palpation for GGO localization would be similar to that reported in previous studies and the pre-experiment (13,19); ultmatedly, a sample size of 50 nodules was determined as being sufficient to provide statistical power.

Procedures and palpation

Patient characteristics were recorded, including age, gender, smoking history, medical history, forced expiratory volume in 1 s (FEV1), and ratio of FEV1 to forced vital capacity (FVC). Features of all GGOs in the planned surgical area were preoperatively evaluated, including the number, maximum diameter, location, consolidation-to-tumor ratio (CTR), and distance from the pleura. Based on the CTR on CT, nodules were classified as pure GGOs (CTR =0) and mixed GGOs (0< CTR <100). Mixed GGOs included a subgroup in which CRT >0 but CTR ≤50 and a subgroup in which CTR >50 but <100 (9). Surgical options—including wedge resection, segmentectomy, and lobectomy—were comprehensively considered by the surgeon based on the patient’s condition and nodule features.

All participants received general anesthesia with single lung ventilation via double-lumen endotracheal intubation. Each patient was placed in the lateral decubitus position. Surgical incisions were made according to the standard VATS procedure. After VATS port placement, the lung was collapsed and thoracoscopyperformed. GGOs and peripheral lung tissue were excised according to the schedule. The excised lung tissue was first examined by two experienced surgeons (X.G.: surgeon in-training, with 6 years of surgical experience; S.L.: 20 years of surgical experience) to identify visual surface features. Pleural surface features were defined as alterations in the color and morphology of the pleural surface overlying the tumor area, such as gray-white or black discoloration and pleural surface depression. GGOs were then detected and localized by palpation. The number of localized nodules and procedure time were recorded.

UHFUS examination

After palpation examination, UHFUS was performed with the Paragon XHD UHFUS imaging system (Wisonic Medical Technology Co., Ltd., Shenzhen, China) with a bandwidth of 22–38 MHz. The excised lung tissue was fixed in the space between the trans-acoustic membrane and operation console. The trans-acoustic membrane is the unit constituting the locking mechanism of the Automated Breast Volumetric Scanner (Siemens Healthineers, Erlangen, Germany). Two experienced radiologists (Y.G.: radiologist in training, with 6 years of experience in ultrasound; Q.Z.: 23 years of experience in ultrasound) performed ultrasound of the whole resected lung and recorded all suspicious nodules (Figure S1 and Video S1), including tiny GGOs in multiple pulmonary nodules, and new GGOs not observed on preoperative CT scans. The procedure time of the ultrasound scans were recorded. A blinding procedure was applied during the UHFUS examination: surgeons documented pleural surface findings but did not inform the ultrasound examiners, who localized the nodules solely based on imaging features rather than on surface appearance.

UHFUS image analysis

The UHFUS images were documented during the examinations. The same two radiologists who had performed ultrasound independently analyzed the images postoperatively and recorded the following features of each nodule (19-23): shape, boundary, and posterior echoic patterns.

Localization and pathologic outcomes

Following surgical resection, each specimen was sequentially examined by palpation and UHFUS to identify and localize the target nodule. The identification and localization of nodules were jointly decided upon by both radiologists intraoperatively, and the imaging features of each nodule were independently analyzed postoperatively. Detection failure was defined as a palpation or UHFUS time exceeding 5 minutes. If both palpation and UHFUS failed to localize nodules, the choice of treatment was at the discretion of the respective surgeon. All lesions were pathologically confirmed postoperatively. The histopathological slides were assessed by designated pathological experts (Junliang Lu and Yinbo Xiao), the histological diagnosis was based on the 2021 World Health Organization Classification of Thoracic Tumors (24), and tumors were staged according to the eighth edition of the TNM classification (25).

Statistical analysis

Data are expressed as the mean ± standard deviation or as the median and interquartile range (IQR), as appropriate. The Wilcoxon signed-rank test was used to compare the UHFUS and palpation times. Detection rates were evaluated with the McNemar test or Fisher exact test (if there were zero values). Scatter plot and Bland-Altman analyses were used to estimate the correlation and agreement between measurements of tumor size by CT, UHFUS, and pathology (26). The consistency of UHFUS features and maximum diameter, assessed by two radiologists, was examined via kappa testing. All P-values were based on a two-sided hypothesis, and a P value <0.05 was considered statistically significant. All statistical calculations were performed with MedCalc version 19.2.1 (MedCalc Software Ltd., Ostend, Belgium) and SPSS version 25.0 (IBM Corporation, Armonk, NY, USA) software. Y.G. and X.G. performed the statistical analyse. The data generated or analyzed during the study are available from the corresponding author upon reasonable request.

Results

Participant information and GGO characteristics

The 36 enrolled patients included 9 males and 27 females (55±10 years), with 6 and 18 patients had smoking and medical histories, respectively. The mean FEV1 of patients was 2.6±0.6 L, and the mean FEV1/FVC ratio was 78.8±5.3%. VATS was successfully completed in all patients. A total of 58 GGOs were assessed for eligibility (maximum diameter: median 7.0, IQR 5.0–10.0 mm; distance from pleura: median 2.0, IQR 1.0–6.3 mm) on CT (Table S1). The 58 GGOs included 37 (63.8%) pure and 21 (36.2%) mixed GGOs, with 44 GGOs (79.3%) being in the lateral pulmonary field. Of the 58 GGOs, 43 (74.1%), 10 (17.2%), and 5 (8.6%) underwent wedge resection, segmentectomy, and lobectomy, respectively. Among the 58 nodules, 14 (24.1%) exhibited pleural surface change, while 44 (75.9%) did not. All nodules with pleural changes demonstrated regional color alterations—12 appeared grayish-white and 2 appeared black—and 6 of them also presented with pleural indentation. These 14 nodules were all located within 5 mm of the pleural surface, including 8 that were directly attached to the pleura (0 mm). On final diagnosis, 27 (46.6%) adenocarcinoma, 6 (10.3%) adenocarcinoma in situ, 11 (19.0%) atypical adenomatous hyperplasic, and 14 (24.1%) benign nodules were analyzed (Table 1).

Localization rate and time

The GGO localization rates were 86.2% for UHFUS and 83.3% for palpation (Table 2). 50 small GGOs were identified; for which the localization rate of UHFUS (33/35, 94.3%) was significantly higher (P=0.02) than that of palpation (28/35, 80.0%) (Figure 2A). In contrast, for GGOs 10 mm, UHFUS had a lower localization rate than did palpation (25.0% vs. 87.5%). Unexpectedly, 15 micro-GGOs (≤5 mm) were detected and localized by UHFUS. To further verify the localization ability of UHFUS, small GGOs were classified into two groups based on their maximum diameter on CT: ≤5 mm (micro-GGOs) and >5–10 mm. All 15 (100%) micro-GGOs were localized by UHFUS, while only 12 (80.0%) were localized by palpation. Of the 35 GGOs in the >5 to 10 mm group, 33 (94.3%) were localized by UHFUS, and 28 (80.0%) were localized by palpation. In all GGO groups, the localization time of UHFUS (median 5, IQR 5–8 s) was significantly shorter (P=0.003) than that of palpation (median 6, IQR 5–15 s), including for small GGOs (UHFUS: median 5 s, IQR 5–8 s; palpation: median 5, IQR 5–15 s; P=0.003), micro-GGOs (UHFUS: median 5 s, IQR 5–10 s; palpation: median 11.5, IQR 5–30 s; P=0.04), and >5 to 10 mm GGOs (UHFUS: median 5, IQR 5–8 s; palpation: median 6, IQR 5–15 s; P=0.03) (Figure 2B).

Figure 2 Localization rate and localization time of UHFUS and palpation in each GGO subgroups. (A) Localization rate of UHFUS and palpation in different groups (yellow for UHFUS and green for palpation). (B) Localization time of UHFUS and palpation in different groups (yellow for UHFUS and green for palpation). *, P≤0.05; **, P≤0.01. φ, localization time expressed in logarithmic form. GGO, ground glass opacity; UHFUS, ultrahigh-frequency ultrasound.

Subgroup analysis

A total of 50 small GGOs were included in the analysis (Table 2) and categorized as pure or mixed GGOs according to the CTR. The successful localization rate was significantly higher (P=0.02) for UHFUS (33/35, 94.3%) than for palpation (25/35, 71.4%); meanwhile, the localization time for pure GGOs was significantlty shorter (P=0.02) for UHFUS (median 5, IQR 5–10 s) compared to palpation (median 12.5, IQR 5−20 s). All 15 mixed GGOs were successfully localized by both UHFUS and palpation.

Concerning the localization of nodules, a statistically significant difference was evident between nodules located in different pulmonary fields. In the lateral field, the successful localization rate was higher (P=0.04) for UHFUS (41/42, 97.6%) than for palpation (34/42, 81.0%), and UHFUS had a significantly shorter (P=0.004) localization time (median 5, IQR 5–7 s) than did palpation (median 5, IQR 5–15 s). Meanwhile, for wedge resection, more nodules were localized by UHFUS (32/39, 97.4%) than by palpation (32/39, 82.1%; P=0.07), and the localization time was significantly shorter (P=0.001) for UHFUS (median 5, IQR 5–6 s) than palpation (median 5, IQR 5–15 s) (Figure 2).

According to pleural surface features, all 14 nodules exhibiting pleural surface changes were successfully localized by palpation, whereas UHFUS had 1 failed localization. Among 44 nodules without pleural surface changes (Table S2), there was no statistically significant difference (P=0.42) in localization rates between UHFUS (37/44, 84.1%) and palpation (33/44, 75.0%). However, within this subgroup, UHFUS achieved a significantly higher (P=0.02) localization rate for small GGOs (36/38, 94.7%) than did palpation (28/38, 73.7%). Similar differences were observed in the subgroups of pure GGOs (P=0.02) and lateral locations (P=0.04).

UHFUS characteristics of GGOs

The characteristics of GGOs obtained from UHFUS images are presented in Figure 3 and Figure S2. The kappa values calculated for the consistency of posterior acoustic patterns, borders, and shapes between the two radiologists were 0.95, 0.78, and 0.85, respectively. The final features of each nodule, as presented in Table 3, were jointly determined by both radiologists. Lung tissue excised by wedge resection was homogeneously hyperechoic with many B lines on ultrasound images; meanwhile, 81.8% (27/33) of small nodules ranging from 5 to 10 mm in diameter (83.3%) displayed posterior acoustic shadowing, while for micro-GGOs, this feature was not as common (attenuated: 40%; absent: 46.7%; enhanced: 13.3%). The margin of small nodules tended to be indistinct (24/33, 72.7%), while most micronodules displayed distinct margins (11/15, 73.3%). Moreover, the majority of small nodules had an irregular shapes (27/33, 81.8%), while 60.0% (9/15) of micronodules had a regular shape. Concerning nodule types based on different CTRs, pure GGOs were likely to manifest as indistinct nodules (21/33, 63.6%) with acoustic shadowing (26/33, 78.8%); however, there was no statistically significant difference in UHFUS features between pure and mixed GGOs.

Figure 3 Comparison of chest CT, UHFUS, macroscopic images and HE staining (10× magnification) in different types of GGOs. Solid red arrows on CT imaged indicate the tumor. Dashed white arrows on UHFUS images indicate the border of the tumor. The white line on macroscopic images marks the entire tumor margin. (A) Chest CT shows a 4-mm pure GGO in the left upper lobe, and UHFUS shows a regular hypoechoic nodule with well-defined margins. The macroscopic image shows a gray-white lesion with unclear boundaries. HE staining shows a significantly widened alveolar septum. Lymphocyte infiltration and fibrous tissue proliferation are visible in interstitial and intra-alveolar locations, with scattered tissue cells present in the alveolar lumen. (B) Chest CT shows a 5-mm GGO in the right upper lobe with minor central solid components and localized pleural traction sign being visible. UHFUS shows an irregular hypoechoic nodule with indistinct margins. The macroscopic image shows a gray-white lesion with spicules of black anthracotic pigmentation and unclear boundaries. HE staining of lung epithelial cells shows atypical hyperplasia (red arrow) along the alveolar wall with lymphocytic aggregation and infiltration being visible in the alveolar interstitium, along with a small amount of carbon deposition. (C) Chest CT shows an 8-mm pure GGO in the superior S6 segment of the right lower lobe. UHFUS shows an irregular hypoechoic nodule with indistinct margin and posterior shadowing. The macroscopic image shows a gray-white lesion with a uniform texture and unclear boundaries. HE staining indicates lung adenocarcinoma cells, mainly growing along the alveolar wall, with a few papillary structures. Scattered foam cells are visible in the alveolar cavity, and fibrous tissue proliferation is presented in the alveolar interstitium, along with lymphocyte aggregation. (D) Chest CT shows a 6.4-mm GGO with unclear boundaries in the right lower lobe, and a bronchus in the nodule. UHFUS shows an irregularly shaped hypoechoic nodule with indistinct border and posterior shadowing. The macroscopic image shows a gray-white pliable lesion with central black anthracotic pigmentation and lobulated margins. HE staining shows lung adenocarcinoma cells. These cells have mainly grown along the alveolar wall, with a few acinar cells, and fibrous tissue proliferation is visible in the alveolar interstitium, along with lymphocyte aggregation and focal carbon deposition. CT, computed tomography; GGO, ground glass opacity; HE, hematoxylin and eosin; UHFUS, ultrahigh-frequency ultrasound.

Detailed case reports

Of the 50 small nodules, 9 (18%) were exclusively identified by UHFUS, all of which were pure GGOs. Six were involved in the preoperative resection plan, while the remaining three nodules were newly found by UHFUS. These six GGOs could not be detected by intraoperative palpation as four were unclearly identified by palpation, one had a localization time exceeding 5 minutes, and one was mislocalized. Meanwhile, UHFUS could localize these six GGOs accurately and rapidly (localization times from 6 to 20 s); one of the three newly found lesions was diagnosed as early-stage lung cancer with minimal invasive adenocarcinoma on pathology. Due to the discovery by UHFUS, the tumor in this patient was more accurately staged, with a TNM stage of pT1aN0M0 (IA1). Only one GGO was found by palpation but missed by UHFUS; in this case, UHFUS misidentified clustered bronchia as a targeted nodule next to the real lesion.

Correlation analysis of lesion sizes

The kappa value for assessing the consistency of GGO diameter measurements by UHFUS between the two radiologists was 0.82. The final UHFUS diameter measurement used for comparison with CT and pathological measurements was the average of the two radiologists’ measurements. The scatter plots revealed a strong correlation between tumor size and CT and UHFUS measurements (Figure 4). A heat map indicated that the best application interval was 1–10 mm, with an 8 to 10 mm diameter being measured most accurately (Figure 4A). However, Bland-Altman plots indicated a statistically significant bias in the measurements between CT and ultrasound (P<0.001) (Figure 4B). No statistically significant bias was evident in the measurements of size between CT and pathology or between UHFUS and pathology (Figure 4C,4D).

Figure 4 The consistency test of GGOs sizes measured by UHFUS, CT and pathology. (A) Scatterplot of the linearity and difference between the measurements of UHFUS and CT. The blue line represents linear regression fit with coefficient of determination indicating a significant linear fit (r=0.85; P<0.001). The color of the heat map shows the correlation between two measurements, with red indicating a high correlation and blue a low correlation. The brown dotted line represents the 95% limits of agreement. (B-D) Bland-Altman plots of CT compared with UHFUS (B), CT compared with pathology (C), and, as a reference, UHFUS compared with pathology (D). The blue line indicates the mean bias detected by comparison between the two methods. The brown dotted line indicates the upper and lower limits of agreement at ±1.96 standard deviations of the measurement difference. (B) The mean bias of measurements between CT and UHFUS is 0.53 mm (95% CI: 0.09–0.96; P=0.02). (C) The mean bias of measurements between CT and pathology is 0.65 mm (95% CI: –0.11 to –1.41; P=0.09). (D) The mean bias of measurements between CT and pathology is 0.14 mm (95% CI: –0.54 to 0.82; P=0.6). CI, confidence interval; CT, computed tomography; GGO, ground glass opacity; UHFUS, ultrahigh-frequency ultrasound.

Discussion

This study introduced a UHFUS technique for the intraoperative detection and localization of early lung cancers presenting as GGOs. Our findings demonstrated the feasibility of UHFUS for examining collapsed resected lung tissue. For small GGOs, UHFUS, as compared with traditional palpation, achieved a significantly higher localization rate (96.0% vs. 80.0%; P=0.02) and shorter localization time (UHFUS: median 5, IQR 5–8 s; palpation: median 5, IQR 5–15 s; P=0.003). Subgroup analyses confirmed its superiority for pure, peripheral, and wedge-resected GGOs. Moreover, UHFUS incidentally detected small nodules misinterpreted as tiny bronchi or vessels on preoperative CT, and its size measurements closely matched those of pathological findings.

Previous reports have indicated that pulmonary ultrasound is technically challenging due to strong reflection of ultrasound waves by intrapulmonary air (27,28). In our study, sufficient intraoperative lung collapse reduced the GGO-pleura distance to <1 cm, allowing clear sonographic visualization. UHFUS, operating at frequencies >20 MHz and providing resolution <50 µm, overcame the limitations of conventional ultrasound in detecting small or subpleural GGOs. Although its penetration depth is limited (<0.7 cm) (29), the use of a sterile, air-free coupling pad and thick coupling gel facilitated effective acoustic transmission and yielded satisfactory imaging quality.

Pleural surface features served as critical landmarks for nodule localization. Only one lesion with pleural change was not identified by UHFUS due to the misinterpretation of a cavity as poor lung collapse and an indentation as a compression artifact, leading to a prolonged localization time (>5 min). Careful visual assessment of pleural features prior to UHFUS examination may therefore enhance detection accuracy in the real clinical practice.

The lower localization rate of UHFUS for GGOs >1 cm may be explained by both anatomical and technical factors. Larger nodules are easily detected by palpation, reducing the added value of UHFUS. In addition, after lung collapse, the surrounding parenchyma typically appears liver-like, and GGOs can be recognized by reverberation artifacts from trapped air. However, when collapse is incomplete, broad regions of lung tissue may exhibit similar artifacts, and the restricted field of view makes it difficult to compare the lesion with adjacent parenchyma, thereby reducing detection capability. Compared with techniques reported in previous research (10,19,20,22,30-32), the UHFUS in our study showed marked advantages for small or pure GGOs, suggesting it is particularly useful for patients with multiple primary lung cancers, in whom simultaneous localization of several microlesions can be clinically valuable. Since even nodules as small as 4–6 mm may harbor malignancy (3), accurate intraoperative localization remains critical for staging and surgical planning.

During surgery, excised lung tissue can be damaged by interstitial exudation, with most of the gas being squeezed out. The air inside the alveoli may be replaced by fluid, blood, or tissue. These alterations reduce the mismatch in acoustical properties between the intercostal tissues and the lungs and open channels for the transmission of ultrasound energy (33,34). Thus, the ultrasound images of excised lung tissue usually contain multiple B lines. Pure GGOs are commonly pure/lepidic-predominant growing malignant nodules or benign lesions with the preservation of intra-alveolar airspace; meanwhile, mixed GGOs typically have lepidic growth and an acinar portion (35). These highly uneven histopathological structures result in the posterior acoustic shadowing of GGOs in ultrasound images, and some nodules with a relatively homogeneous internal structure may display an enhanced posterior acoustic pattern. Compared with conventional ultrasound, UHFUS can fully reflect the microscale differences among tissues by providing detailed characteristics and displaying the cancerous alveolar cavity.

In our study, when discrepancies occurred between CT- and UHFUS-measured nodule diameters, comparative analysis revealed that UHFUS correlated more closely with pathological measurements. For nodules <6 mm, CT tended to overestimate size due to trapped air and imaging artifacts; for larger lesions, CT slightly underestimated dimensions because it measured the maximum diameter on a horizontal plane, whereas UHFUS captured the maximal three-dimensional extent.

This study involved several limitations that should be addressed. First, we employed a single-center design, and multicenter validation is required to confirm the generalizability of our findings. Second, although UHFUS enhances imaging resolution, its penetration depth is limited, and accurate detection becomes challenging for nodules larger than approximately 10 mm. Third, as the study was performed using resected lung specimens, the findings primarily serve as a proof of concept for future intrathoracic applications. With the development of high-frequency thoracoscopic ultrasound probes, UHFUS may be further validated and clinically implemented for the pre-resection localization of pulmonary nodules.

Conclusions

This prospective observational study suggests that UHFUS is an innovative, real-time, noninvasive, and radiation-free technique with strong potential for the intraoperative localization of GGOs within lung tissue. It may improve the efficiency of lung surgery by identifying small lesions and enabling the detection of previously unrecognized nodules.

Acknowledgments

The authors thank the study participants. Without their participation and commitment, this study would not be possible. The authors thank Yinbo Xiao, MD, and Junliang Lu, MD, from the Department of Pathology, Peking Union Medical College Hospital, for their assistance with analyzing HE stained pathological sections of pulmonary nodules. We thank International Science Editing (http://www.internationalscienceediting.com), AME Language Editing Service (https://editing.amegroups.cn), and Editage (http://www.editage.cn) for English language editing. The abstract (Control #: 2024-RA-1124-WCLC) was presented as a poster at 2024 World Conference of Lung Cancer (WCLC; September 8, 2024; San Diego).

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-448/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-448/dss

Funding: This work was supported by the National High Level Hospital Clinical Research Funding (Nos. 2025-PUMCH-A-177 and 2025-PUMCH-A-015), the CAMS Innovation Fund for Medical Sciences (Nos. 2023-I2M-C&T-B-019, 2024-I2M-C&T-B-035 and 2024-I2M-C&T-B-025) and Beijing Natural Science Foundation (No. L258029).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-448/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was approved by the Institutional Review Board of Peking Union Medical College Hospital (PUMCH) (approval No. K23C2063). This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Informed consent was obtained from all participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021;71:209-49. [Crossref] [PubMed]
2. Zhang Y, Fu F, Chen H. Management of Ground-Glass Opacities in the Lung Cancer Spectrum. Ann Thorac Surg 2020;110:1796-804. [Crossref] [PubMed]
3. Church TR, Black WC, Aberle DR, Berg CD, Clingan KL, Duan F, Fagerstrom RM, Gareen IF, Gierada DS, Jones GC, Mahon I, Marcus PM, Sicks JD, Jain A, Baum S. Results of initial low-dose computed tomographic screening for lung cancer. N Engl J Med 2013;368:1980-91. [Crossref] [PubMed]
4. Henschke CI, Yankelevitz DF, Mirtcheva R, McGuinness G, McCauley D, Miettinen OS. CT screening for lung cancer: frequency and significance of part-solid and nonsolid nodules. AJR Am J Roentgenol 2002;178:1053-7. [Crossref] [PubMed]
5. Zhang Y, Jheon S, Li H, Zhang H, Xie Y, Qian B, Lin K, Wang S, Fu C, Hu H, Zheng Y, Li Y, Chen H. Results of low-dose computed tomography as a regular health examination among Chinese hospital employees. J Thorac Cardiovasc Surg 2020;160:824-831.e4. [Crossref] [PubMed]
6. Shimada Y, Saji H, Otani K, Maehara S, Maeda J, Yoshida K, Kato Y, Hagiwara M, Kakihana M, Kajiwara N, Ohira T, Akata S, Ikeda N. Survival of a surgical series of lung cancer patients with synchronous multiple ground-glass opacities, and the management of their residual lesions. Lung Cancer 2015;88:174-80. [Crossref] [PubMed]
7. Saji H, Okada M, Tsuboi M, Nakajima R, Suzuki K, Aokage K, et al. Segmentectomy versus lobectomy in small-sized peripheral non-small-cell lung cancer (JCOG0802/WJOG4607L): a multicentre, open-label, phase 3, randomised, controlled, non-inferiority trial. Lancet 2022;399:1607-17. [Crossref] [PubMed]
8. Hou YL, Wang YD, Guo HQ, Zhang Y, Guo Y, Han H. Ultrasound location of pulmonary nodules in video-assisted thoracoscopic surgery for precise sublobectomy. Thorac Cancer 2020;11:1354-60. [Crossref] [PubMed]
9. Matsunaga T, Suzuki K, Takamochi K, Oh S. What is the radiological definition of part-solid tumour in lung cancer?†. Eur J Cardiothorac Surg 2017;51:242-7. [Crossref] [PubMed]
10. Santambrogio R, Montorsi M, Bianchi P, Mantovani A, Ghelma F, Mezzetti M. Intraoperative ultrasound during thoracoscopic procedures for solitary pulmonary nodules. Ann Thorac Surg 1999;68:218-22. [Crossref] [PubMed]
11. Wada H, Anayama T, Hirohashi K, Nakajima T, Kato T, Waddell TK, Keshavjee S, Yoshino I, Yasufuku K. Thoracoscopic ultrasonography for localization of subcentimetre lung nodules. Eur J Cardiothorac Surg 2016;49:690-7. [Crossref] [PubMed]
12. Zhou Z, Wang Z, Zheng Z, Cao J, Zhang C, He Z, Lv W, Hu J. An "alternative finger" in robotic-assisted thoracic surgery: intraoperative ultrasound localization of pulmonary nodules. Med Ultrason 2017;19:374-9. [Crossref] [PubMed]
13. Sortini D, Feo CV, Carcoforo P, Carrella G, Pozza E, Liboni A, Sortini A. Thoracoscopic localization techniques for patients with solitary pulmonary nodule and history of malignancy. Ann Thorac Surg 2005;79:258-62; discussion 262. [Crossref] [PubMed]
14. Yamamoto M, Takeo M, Meguro F, Ishikawa T. Sonographic evaluation for peripheral pulmonary nodules during video-assisted thoracoscopic surgery. Surg Endosc 2003;17:825-7. [Crossref] [PubMed]
15. Ujiie H, Kato T, Hu HP, Hasan S, Patel P, Wada H, Lee D, Fujino K, Hwang DM, Cypel M, de Perrot M, Pierre A, Darling G, Waddell TK, Keshavjee S, Yasufuku K. Evaluation of a New Ultrasound Thoracoscope for Localization of Lung Nodules in Ex Vivo Human Lungs. Ann Thorac Surg 2017;103:926-34. [Crossref] [PubMed]
16. Rohrbach DJ, Muffoletto D, Huihui J, Saager R, Keymel K, Paquette A, Morgan J, Zeitouni N, Sunar U. Preoperative mapping of nonmelanoma skin cancer using spatial frequency domain and ultrasound imaging. Acad Radiol 2014;21:263-70. [Crossref] [PubMed]
17. Amin MB, Greene FL, Edge SB, Compton CC, Gershenwald JE, Brookland RK, Meyer L, Gress DM, Byrd DR, Winchester DP. The Eighth Edition AJCC Cancer Staging Manual: Continuing to build a bridge from a population-based to a more "personalized" approach to cancer staging. CA Cancer J Clin 2017;67:93-9.
18. Ettinger DS, Wood DE, Aisner DL, Akerley W, Bauman JR, Bharat A, et al. NCCN Guidelines® Insights: Non-Small Cell Lung Cancer, Version 2.2023. J Natl Compr Canc Netw 2023;21:340-50. [Crossref] [PubMed]
19. Kondo R, Yoshida K, Hamanaka K, Hashizume M, Ushiyama T, Hyogotani A, Kurai M, Kawakami S, Fukushima M, Amano J. Intraoperative ultrasonographic localization of pulmonary ground-glass opacities. J Thorac Cardiovasc Surg 2009;138:837-42. [Crossref] [PubMed]
20. Piolanti M, Coppola F, Papa S, Pilotti V, Mattioli S, Gavelli G. Ultrasonographic localization of occult pulmonary nodules during video-assisted thoracic surgery. Eur Radiol 2003;13:2358-64. [Crossref] [PubMed]
21. Matsumoto S, Hirata T, Ogawa E, Fukuse T, Ueda H, Koyama T, Nakamura T, Wada H. Ultrasonographic evaluation of small nodules in the peripheral lung during video-assisted thoracic surgery (VATS). Eur J Cardiothorac Surg 2004;26:469-73. [Crossref] [PubMed]
22. Taurchini M, Quarato CMI, Frongillo EM, Ferretti GM, Cipriani C, Bizzarri M, Foschino Barbaro MP, Lacedonia D, Simeone A, Graziano P, Dimitri L, Maiello E, Cavaliere L, De Cosmo S, Sperandeo M. Intraoperative Lung Ultrasound (ILU) for the Assessment of Pulmonary Nodules. Diagnostics (Basel) 2021;11:1691. [Crossref] [PubMed]
23. Khereba M, Ferraro P, Duranceau A, Martin J, Goudie E, Thiffault V, Liberman M. Thoracoscopic localization of intraparenchymal pulmonary nodules using direct intracavitary thoracoscopic ultrasonography prevents conversion of VATS procedures to thoracotomy in selected patients. J Thorac Cardiovasc Surg 2012;144:1160-5. [Crossref] [PubMed]
24. Nicholson AG, Tsao MS, Beasley MB, Borczuk AC, Brambilla E, Cooper WA, Dacic S, Jain D, Kerr KM, Lantuejoul S, Noguchi M, Papotti M, Rekhtman N, Scagliotti G, van Schil P, Sholl L, Yatabe Y, Yoshida A, Travis WD. The 2021 WHO Classification of Lung Tumors: Impact of Advances Since 2015. J Thorac Oncol 2022;17:362-87. [Crossref] [PubMed]
25. Detterbeck FC, Boffa DJ, Kim AW, Tanoue LT. The Eighth Edition Lung Cancer Stage Classification. Chest 2017;151:193-203.
26. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986;1:307-10.
27. Mongodi S, De Luca D, Colombo A, Stella A, Santangelo E, Corradi F, Gargani L, Rovida S, Volpicelli G, Bouhemad B, Mojoli F. Quantitative Lung Ultrasound: Technical Aspects and Clinical Applications. Anesthesiology 2021;134:949-65. [Crossref] [PubMed]
28. Yang T, Karakus O, Anantrasirichai N, Achim A. Current Advances in Computational Lung Ultrasound Imaging: A Review. IEEE Trans Ultrason Ferroelectr Freq Control 2023;70:2-15. [Crossref] [PubMed]
29. Bhatta AK, Keyal U, Liu Y. Application of high frequency ultrasound in dermatology. Discov Med 2018;26:237-42.
30. Roshankhah R, Blackwell J, Ali MH, Masuodi B, Egan T, Muller M. Detecting pulmonary nodules by using ultrasound multiple scattering. J Acoust Soc Am 2021;150:4095. [Crossref] [PubMed]
31. Hornblower VD, Yu E, Fenster A, Battista JJ, Malthaner RA. 3D thoracoscopic ultrasound volume measurement validation in an ex vivo and in vivo porcine model of lung tumours. Phys Med Biol 2007;52:91-106. [Crossref] [PubMed]
32. Uramoto H, Nakajima Y, Ohtaki K, Kinoshita H. Intraoperative ultrasound elastography has little diagnostic benefit for deeper tumours of the lung. Eur J Cardiothorac Surg 2016;49:1538-9. [Crossref] [PubMed]
33. Demi L, Wolfram F, Klersy C, De Silvestri A, Ferretti VV, Muller M, et al. New International Guidelines and Consensus on the Use of Lung Ultrasound. J Ultrasound Med 2023;42:309-44. [Crossref] [PubMed]
34. Marini TJ, Rubens DJ, Zhao YT, Weis J, O'Connor TP, Novak WH, Kaproth-Joslin KA. Lung Ultrasound: The Essentials. Radiol Cardiothorac Imaging 2021;3:e200564. [Crossref] [PubMed]
35. Gao JW, Rizzo S, Ma LH, Qiu XY, Warth A, Seki N, Hasegawa M, Zou JW, Li Q, Femia M, Lv TF, Song Y. Pulmonary ground-glass opacity: computed tomography features, histopathology and molecular pathology. Transl Lung Cancer Res 2017;6:68-75. [Crossref] [PubMed]