Differentiation of persistent pulmonary subsolid nodules with a solid component smaller than 6 mm: to be invasive adenocarcinoma or not to be?

Photon-counting detector CT provides superior subsolid nodule characterization compared to same-day energy-integrating detector CT

PURPOSE

To investigate the image quality and the performance of photon-counting detector (PCD) CT compared to conventional energy-integrating detector (EID) CT in identifying subsolid nodule (SSN) characteristics.

MATERIALS AND METHODS

Participants with SSNs who underwent same-day EID CT and PCD CT between October 2023 and April 2024 were prospectively included. The 1.0 mm EID CT images and, subsequently, 1.0 mm, 0.4 mm, and 0.2 mm PCD CT images were reviewed to assess image noise and subjective image quality on a 5-point Likert scale. SSN characteristics, including lobulation, spiculation, pleural retraction, air cavities, intra-nodular vessel signs, internal vascular changes, and heterogeneous solid components, were evaluated. Additionally, a step-by-step observation and comparison method was used to determine the presence of any additional characteristics.

RESULTS

Forty-eight participants (mean age: 56 ± 11 years; 16 males) with 89 SSNs were included. PCD CT significantly reduced radiation dose when using matched scans (1.79 ± 0.39 vs 2.17 ± 0.57 mSv, p < 0.001). Compared to 1.0 mm EID CT, 1.0 mm PCD CT images exhibited significantly lower objective image noise and higher subjective image quality (all p < 0.001). Compared to EID CT, PCD CT demonstrated enhanced visualization of subtle characteristics, except for lobulation, with a 0.4 mm section thickness offering a favorable balance between ultra-high resolution and perceived image quality for radiologists.

CONCLUSION

PCD CT facilitated radiation dose reduction and outperformed conventional EID CT in terms of image quality and visualization of SSN characteristics.

KEY POINTS

Question PCD CT, featuring ultra-high-resolution mode acquisition and a thinner reconstruction, has not been fully explored for characterizing SSNs. Findings Compared to EID CT, PCD CT was associated with lower objective image noise, higher subjective image quality, and superior SSN characterization. Clinical relevance PCD CT effectively reduced the radiation dose delivered to the patients and enabled more precise SSN characterization.

A radiomics nomogram for invasiveness prediction in lung adenocarcinoma manifesting as part-solid nodules with solid components smaller than 6 mm

Objective

To investigate whether radiomics can help radiologists and thoracic surgeons accurately predict invasive adenocarcinoma (IAC) manifesting as part-solid nodules (PSNs) with solid components <6 mm and provide a basis for rational clinical decision-making.

Materials and Methods

In total, 1,210 patients (mean age ± standard deviation: 54.28 ± 11.38 years, 374 men and 836 women) from our hospital and another hospital with 1,248 PSNs pathologically diagnosed with adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or IAC were enrolled in this study. Among them, 1,050 cases from our hospital were randomly divided into a derivation set (n = 735) and an internal validation set (n = 315), 198 cases from another hospital were used for external validation. Each labeled nodule was segmented, and 105 radiomics features were extracted. Least absolute shrinkage and selection operator (LASSO) was used to calculate Rad-score and build the radiomics model. Multivariable logistic regression was conducted to identify the clinicoradiological predictors and establish the clinical-radiographic model. The combined model and predictive nomogram were developed based on identified clinicoradiological independent predictors and Rad-score using multivariable logistic regression analysis. The predictive performances of the three models were compared via receiver operating characteristic (ROC) curve analysis. Decision curve analysis (DCA) was performed on both the internal and external validation sets to evaluate the clinical utility of the nomogram.

Results

The radiomics model showed superior predictive performance than the clinical-radiographic model in both internal and external validation sets (Az values, 0.884 vs. 0.810, p = 0.001; 0.924 vs. 0.855, p < 0.001, respectively). The combined model showed comparable predictive performance to the radiomics model (Az values, 0.887 vs. 0.884, p = 0.398; 0.917 vs. 0.924, p = 0.271, respectively). The clinical application value of the nomogram developed based on the Rad-score, maximum diameter, and lesion shape was confirmed, and DCA demonstrated that application of the Rad-score would be beneficial for radiologists predicting invasive lesions.

Conclusions

Radiomics has the potential as an independent diagnostic tool to predict the invasiveness of PSNs with solid components <6 mm.