RPS 804 - Current topics in lung cancer imaging

Author Block: Y. Xin¹, S. Z. Erick¹, M. A. Heuvelmans¹, G. De Bock¹, R. Vliegenthart¹, B. Edwin², M. Van Tuinen¹, F. Mohamed Hoesein²; ¹Groningen/NL, ²Utrecht/NL
Purpose: CT-based body composition measures improve prediction of mortality in oncology, but reference values are lacking. We evaluated age- and smoking-related variation in body composition in a lung cancer screening cohort.
Methods or Background: We used artificial intelligence-based automated analysis of body composition measures on baseline low-dose chest CT from male participants in the NELSON lung cancer screening trial. Skeletal muscle area (SMA) and subcutaneous adipose tissue area (SAT) were quantified at T5, T8, and T10 levels. The mean values of these levels were used to derive a single SMA and SAT metric per participant; the fat-to-muscle ratio (FMR) was calculated. Age in 5-year groups and smoking status were analyzed in relation to body composition measures. Smoking pack-years was included as covariate in regression analyses.
Results or Findings: We included 4,435 men with mean age 59.4 (SD=5.6) years and mean smoking pack-years 42.2 (SD=29.7). Current smokers (55.0%%) had significantly lower SAT (372 vs. 441 cm², p<0.001), SMA (501 vs. 507 cm², p<0.001) and FMR (0.74 vs. 0.87, p<0.001), compared with former smokers(45.0%). Across 5-year age groups, SMA declined from 515 cm² in the 50–54 years group to 472 cm² in the ≥70 years group (p<0.001), while SAT increased from 376 to 443 cm² (p<0.001) and FMR from 0.70 to 0.90 (p<0.001); associations remained significant after adjusting for smoking status and pack-years.
Conclusion: In men undergoing lung cancer screening, higher age was associated with muscle loss and fat gain, while current smoking was associated with lower muscle and fat compared to former smoking. Reference values of chest CT-based body composition measures may help risk stratification in screening.
Limitations: Our further analyses should confirm the relationship of body composition markers to outcomes in lung cancer screening setting.
Funding for this study: Funding was provided by the Dutch Cancer Society and Siemens Healthineers.
The first author is supported by funding from the Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China.
Has your study been approved by an ethics committee? Yes
Ethics committee - additional information: Health Council, WBO Committee. Population Screening Act: CT screening on lung cancer. The Hague: Health Council of the Netherlands, 2000.

Author Block: G. Chassagnon, A. Lupo, M. ROYER, M. Alifano, N. Roche, M-P. Revel; Paris/FR
Purpose: Preoperative risk stratification before lung cancer surgery mainly relies on clinical indicators. This study aimed to determine whether chest computed tomography (CT)-derived parameters could provide additional prognostic value in identifying patients at increased risk of peri-operative mortality.
Methods or Background: We retrospectively analyzed data from 720 patients who underwent lung cancer resection at our tertiary center in 2018-2019. Clinical, functional and imaging data were analyzed, including for the latter, coronary artery calcium score, pulmonary artery and aorta diameters, presence of emphysema, interstitial lung abnormalities, or airspace enlargement with fibrosis (AEF). Multivariable logistic and Cox regression models were applied to identify independent predictors of 30-day and 90-day mortality, as well as overall survival.
Results or Findings: AEF on CT (Odds Ratio (OR) 10.87, p < 0.001) was a significant predictor of 30-day mortality, while AEF (OR 7.04, p<0.001), as well as a higher ECOG status (OR 1.98, p=0.029) and a lower FEV₁ (OR 0.97, p=0.023) were associated with increased 90-day mortality. AEF was present in 8.2% of patients and was observed in 45.5% of patients who died within 30 days postoperatively. AEF was a nearly significant predictor of long-term survival in the multivariate Cox regression (HR = 1.57, 95% CI: 0.99–2.47, p = 0.046).
Conclusion: CT can provide valuable prognostic information in patients undergoing lung cancer surgery. AEF on CT is a major independent prognostic marker, especially for perioperative mortality. Our results support recognizing AEF as a distinct radiological entity that should be systematically assessed.
Limitations: Firstly, AEF was defined radiologically rather than histologically. Histological confirmation of AEF was unavailable because the affected lung areas were not systematically sampled and analysed.. Secondly, it was a single-centre retrospective study, which may limit the generalizability of the findings.
Funding for this study: This study received no funding



This study received non funding
Has your study been approved by an ethics committee? Yes
Ethics committee - additional information: The study protocol was approved by the local Institutional Review Board (CLEP Decision N°: AAA-2025-10005), which waived the need for patient consent.

Author Block: M. F. G. Konrad¹, E. Nischwitz¹, J. Chorostowska-Wynimko², J. Vogel-Claussen³, J. Moes-Sosnowska², M. Adamek⁴, A. Kerpel-Fronius⁵, H. Prosch⁶, H-U. Kauczor¹; ¹Heidelberg/DE, ²Warsaw/PL, ³Hannover/DE, ⁴Gdańsk/PL, ⁵Budapest/HU, ⁶Vienna/AT
Purpose: Assessing the current status of applied CT image acquisition protocols in lung cancer screening (LCS) worldwide, focusing on technical factors linked to radiation exposure.
Methods or Background: The survey was expanded from an internal SOLACE version to include broader dissemination, collecting data from personnel responsible for the definition of CT protocols at LCS centres worldwide. Data were collected through a baseline survey between June 2024 and September 2025 to represent the most current status.
Results or Findings: Survey responses were received from 71 screening centres from 29 countries (19 Europe, 10 other continents). Institutional factors influencing CT protocols encompass the responsibility of establishment and modification of protocols by personnel (radiologists, radiographers, medical physicists, manufacturer personnel). In 33% of the institutions the protocol was established by a multiprofessional team. CT protocols were mostly modifiable (85%). Technical questions were partially answered. In 88% of the centres (43 of 49) automatic exposure control was implemented. Reconstructed slice thickness ranged 0.625-1.5 mm; 1.0 mm dominating with 64%. Increment ranged 0.5-1.25 mm, where 0.625 mm, 0.7 mm, and 1.0 mm were distributed more evenly with 18%, 25%, and 30%, respectively. Screening-specific software was used at 87% of sites; mainly for nodule detection, volumetry, and calculation of volume doubling time (32, 31, and 25 centres, respectively). Reconstruction algorithm types included filtered-back projection (4), iterative reconstruction with statistical modeling (30), and iterative reconstruction with deep learning support (7).
Conclusion: LCS imaging often reaches the technical limits of currently operated devices. Multiprofessional establishment of CT protocols is an area for improvement. Variations in reconstruction algorithm types warrant further research regarding their influence on volumetry calculations. Protocol optimization is essential to balance radiation exposure reduction and diagnostic quality.
Limitations: The survey likely reflects centres with research interests.
Funding for this study: This project is co-funded under the EU4Health Programme 2021–2027 under grant agreement no. 101101187
Has your study been approved by an ethics committee? Not applicable
Ethics committee - additional information:

Author Block: F. Palmeri¹, A. Del Gaudio², M. Zerunian², F. Di Gregorio², A. Ferraris¹, D. Caruso², M. Francone², A. Laghi³; ¹Torino/IT, ²Rome/IT, ³Pieve Emanuele/IT
Purpose: To assess the accuracy of RECIST 1.1 in evaluating treatment response after neoadjuvant chemotherapy, with or without immunotherapy, in patients with stage IIIA–IIIB non-small cell lung cancer (NSCLC) undergoing surgical resection, and to highlight its limitations in reflecting true pathological outcomes.
Methods or Background: In a prospective pilot study, 33 patients with stage IIIA–IIIB NSCLC received neoadjuvant chemotherapy prior to surgery; 11 also received pembrolizumab. All patients underwent baseline and post-treatment contrast-enhanced CT scans, evaluated using RECIST 1.1 or iRECIST by two radiologists in consensus. Surgical specimens were analyzed histopathologically to determine complete or partial response. Radiological and pathological responses were then compared.
Results or Findings: Of the 33 patients, 12 achieved complete pathological response (pCR). None of these patients were classified as complete responders by RECIST 1.1 on post-treatment imaging. The remaining 21 patients showed partial pathological response, with residual viable tumor ranging from 10% to 80%. Only 9 of these (43%) met RECIST criteria for partial response, while the others were categorized as stable disease despite substantial histological regression. This discordance was more pronounced in patients treated with combined chemo-immunotherapy, suggesting that lesion size alone may fail to capture treatment-induced changes such as immune-related remodeling or necrosis.
Conclusion: RECIST 1.1 appears insufficiently sensitive to capture complete or substantial tumor response after neoadjuvant therapy in stage IIIA–IIIB NSCLC. The discrepancy is most evident with immunotherapy, which may induce changes not reflected by lesion size. As a result, radiological assessments often underestimate true pathological response. In locally advanced NSCLC, alternative or complementary imaging biomarkers are needed to better predict pathological outcomes and guide clinical decisions, particularly when chemotherapy is combined with immunotherapy.
Limitations: The limitations are the small sample size and single-center design
Funding for this study: No funding was received for this study.
Has your study been approved by an ethics committee? Yes
Ethics committee - additional information: Written informed consent was acquired for all patients and Istitutional Review Board approval was obtained.

Author Block: F. Palmeri¹, V. Solimene², A. Del Gaudio², M. Zerunian², F. Di Gregorio², A. Ferraris¹, D. Caruso², M. Francone², A. Laghi³; ¹Torino/IT, ²Roma/IT, ³Pieve Emanuele/IT
Purpose: To develop and validate machine learning (ML) models based on radiomic features extracted from CT imaging to differentiate between high (PD-L1 ≥50%) and low (PD-L1 <50%) expression in non-small cell lung cancer (NSCLC). The aim was to explore whether imaging biomarkers could provide a noninvasive alternative to histological profiling for guiding immunotherapy decisions.
Methods or Background: This retrospective study included 210 patients with histologically confirmed NSCLC and pre-treatment contrast-enhanced chest CT. Among them, 46 (21.9%) showed high PD-L1 expression, while 164 (78.1%) showed low expression. Tumors were manually segmented, and radiomic features were extracted following Image Biomarker Standardization Initiative guidelines. Five ML models were built for binary classification (“PD-L1 ≥50%” vs. “PD-L1 <50%”), using Random Forest, Support Vector Machine, K-Nearest Neighbors, Multi-Layer Perceptron, and Logistic Regression classifiers. Histological PD-L1 served as the reference standard. Statistical significance was set at P < .05.
Results or Findings: The Multi-Layer Perceptron model achieved the best performance, with a ROC-AUC of 0.61 (95% CI: 0.57–0.66, P< .005) and an accuracy of 64% (95% CI: 60–68%). Sensitivity reached 70% (95% CI: 65–75%) and PPV 82% (95% CI: 80–84%), while specificity and NPV were lower at 44% (95% CI: 36–52%) and 29% (95% CI: 24–35%), respectively. The F1 score was 75% (95% CI: 71–78%).
Conclusion: Radiomic analysis of pre-treatment CT images using a Multi-Layer Perceptron classifier showed potential in distinguishing PD-L1 expression levels in NSCLC, with good sensitivity, PPV, and overall F1 score. However, the model's limited specificity and NPV suggest it may be more effective in identifying patients likely to express high PD-L1, rather than excluding them. Radiomic-based ML models could aid noninvasive prediction of PD-L1, supporting immunotherapy selection when biopsy is inconclusive or risky.
Limitations: The limitations are the retrospective single-center design.
Funding for this study: No funding was received for this study.
Has your study been approved by an ethics committee? Yes
Ethics committee - additional information: Written informed consent was acquired for all patients and Istitutional Review Board approval was obtained.