SUVmax to tumor perimeter distance: a robust radiomics prognostic biomarker in resectable non-small cell lung cancer patients

Quality of lymph node dissection and early recurrence in robotic versus thoracoscopic lobectomy for stage N1-2 non-small cell lung cancer: Eleven-year real-world data from a high-volume center

BACKGROUND

The efficacy of lymph node dissection (LND) and oncological outcomes of robot-assisted (RL) versus video-assisted thoracoscopic lobectomy (VL) for non-small cell lung cancer (NSCLC) with nodal involvement remains controversial. This study aims to compare LND quality and early recurrence (ER) rate between RL and VL for stage N1-2 NSCLC patients based on eleven-year real-world data from a high-volume center.

METHODS

Pathologic stage IIB-IIIB (T1-3N1-2) NSCLC patients undergoing RL or VL in Shanghai Chest Hospital from 2010 to 2021 were retrospectively reviewed from a prospectively maintained database. Propensity-score matching (PSM, 1:4 RL versus VL) was performed to mitigate baseline differences. LND quality was evaluated by adequate (≥16) LND and nodal upstaging rates. ER was defined as recurrence occurring within 24 months post-surgery.

RESULTS

Out of 1578 cases reviewed, PSM yielded 200 RL and 800 VL cases. Without compromising perioperative outcomes, RL assessed more N1 and N2 LNs and N1 stations, and led to higher incidences of adequate LND (58.5 % vs. 42.0 %, p < 0.001) and nodal upstaging (p = 0.026), compared to VL. Notably, RL improved perioperative outcomes for patients undergoing adequate LND than VL. Finally, RL notably reduced ER rate (22.0 % vs. 29.6 %, p = 0.032), especially LN ER rate (15.0 % vs. 21.5 %, p = 0.041), and prolonged disease-free survival (DFS; hazard ratio = 0.837, p = 0.040) compared with VL. Further subgroup analysis of ER and DFS within the cN1-2-stage cohort verified this survival benefit.

CONCLUSIONS

RL surpasses VL in enhancing LND quality, reducing ER rates, and improving perioperative outcomes when adequate LND is performed for stage N1-2 NSCLC patients.

Promising Candidate Prognostic Biomarkers in [18F]FDG PET Images: Evaluation in Independent Cohorts of Non-Small Cell Lung Cancer Patients

The normalized distances from the hot spot of radiotracer uptake (SUVmax) to the tumor centroid (NHOC) and to the tumor perimeter (NHOP) have recently been suggested as novel PET features reflecting tumor aggressiveness. These biomarkers characterizing the shift of SUVmax toward the lesion edge during tumor progression have been shown to be prognostic factors in breast and non-small cell lung cancer (NSCLC) patients. We assessed the impact of imaging parameters on NHOC and NHOP, their complementarity to conventional PET features, and their prognostic value for advanced-NSCLC patients. Methods: This retrospective study investigated baseline [18F]FDG PET scans: cohort 1 included 99 NSCLC patients with no treatment-related inclusion criteria (robustness study); cohort 2 included 244 NSCLC patients (survival analysis) treated with targeted therapy (93), immunotherapy (63), or immunochemotherapy (88). Although 98% of patients had metastases, radiomic features including SUVs were extracted from the primary tumor only. NHOCs and NHOPs were computed using 2 approaches: the normalized distance from the localization of SUVmax or SUVpeak to the tumor centroid or perimeter. Bland-Altman analyses were performed to investigate the impact of both spatial resolution (comparing PET images with and without gaussian postfiltering) and image sampling (comparing 2 voxel sizes) on feature values. The correlation of NHOCs and NHOPs with other features was studied using Spearman correlation coefficients (r). The ability of NHOCs and NHOPs to predict overall survival (OS) was estimated using the Kaplan-Meier method. Results: In cohort 1, NHOC and NHOP features were more robust to image filtering and to resampling than were SUVs. The correlations were weak between NHOCs and NHOPs (r ≤ 0.45) and between NHOCs or NHOPs and any other radiomic features (r ≤ 0.60). In cohort 2, the patients with short OS demonstrated higher NHOCs and lower NHOPs than those with long OS. NHOCs significantly distinguished 2 survival profiles in patients treated with immunotherapy (log-rank test, P < 0.01), whereas NHOPs stratified patients regarding OS in the targeted therapy (P = 0.02) and immunotherapy (P < 0.01) subcohorts. Conclusion: Our findings suggest that even in advanced NSCLC patients, NHOC and NHOP features pertaining to the primary tumor have prognostic potential. Moreover, these features appeared to be robust with respect to imaging protocol parameters and complementary to other radiomic features and are now available in LIFEx software to be independently tested by others.

Analytical performance validation of aPROMISE platform for prostate tumor burden, index and dominant tumor assessment with 18F-DCFPyL PET/CT. A pilot study

To validate the performance of automated Prostate Cancer Molecular Imaging Standardized Evaluation (aPROMISE) in quantifying total prostate disease burden with 18F-DCFPyL PET/CT and to evaluate the interobserver and histopathologic concordance in the establishment of dominant and index tumor. Patients with a recent diagnosis of intermediate/high-risk prostate cancer underwent 18F-DCFPyL-PET/CT for staging purpose. In positive-18F-DCFPyL-PET/CT scans, automated prostate tumor segmentation was performed using aPROMISE software and compared to an in-house semiautomatic-manual guided segmentation procedure. SUV and volume related variables were obtained with two softwares. A blinded evaluation of dominant tumor (DT) and index tumor (IT) location was assessed by both groups of observers. In histopathological analysis, Gleason, International Society of Urological Pathology (ISUP) group, DT and IT location were obtained. We compared all the obtained variables by both software packages using intraclass correlation coefficient (ICC) and Cohen's kappa coefficient (k) for the concordance analysis. Fifty-four patients with a positive 18F-DCFPyL PET/CT were evaluated. The ICC for the SUVmax, SUVpeak, SUVmean, tumor volume (TV) and total lesion activity (TLA) was: 1, 0.833, 0.615, 0.494 and 0.950, respectively (p < 0.001 in all cases). For DT and IT detection, a high agreement was observed between both softwares (k = 0.733; p < 0.001 and k = 0.812; p < 0.001, respectively) although the concordances with histopathology were moderate (p < 0001). The analytical validation of aPROMISE showed a good performance for the SUVmax, TLA, DT and IT definition in comparison to our in-house method, although the concordance was moderate with histopathology for DT and IT.

Prognostic value of consolidation-to-tumor ratio on computed tomography in NSCLC: a meta-analysis

BACKGROUND

Although several studies have confirmed the prognostic value of the consolidation to tumor ratio (CTR) in non-small cell lung cancer (NSCLC), there still remains controversial about it.

METHODS

We systematically searched the PubMed, Embase, and Web of Science databases from inception to April, 2022 for eligible studies that reported the correlation between CTR and prognosis in NSCLC. Hazard ratios (HRs) with 95% confidence intervals (95% CIs) were extracted and pooled to assess the overall effects. Heterogeneity was estimated by I² statistics. Subgroup analysis based on the cut-off value of CTR, country, source of HR and histology type was conducted to detect the sources of heterogeneity. Statistical analyses were performed using STATA version 12.0.

RESULTS

A total of 29 studies published between 2001 and 2022 with 10,347 patients were enrolled. The pooled results demonstrated that elevated CTR was associated with poorer overall survival (HR = 1.88, 95% CI 1.42-2.50, P < 0.01) and disease-free survival (DFS)/recurrence-free survival (RFS)/progression-free survival (PFS) (HR = 1.42, 95% CI 1.27-1.59, P < 0.01) in NSCLC. According to subgroup analysis by the cut-off value of CTR and histology type, both lung adenocarcinoma and NSCLC patients who had a higher CTR showed worse survival. Subgroup analysis stratified by country revealed that CTR was a prognostic factor for OS and DFS/RFS/PFS in Chinese, Japanese, and Turkish patients.

CONCLUSIONS

In NSCLC patients with high CTR, the prognosis was worse than that with low CTR, indicating that CTR may be a prognostic factor.

Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency

The interest in machine learning (ML) has grown tremendously in recent years, partly due to the performance leap that occurred with new techniques of deep learning, convolutional neural networks for images, increased computational power, and wider availability of large datasets. Most fields of medicine follow that popular trend and, notably, radiation oncology is one of those that are at the forefront, with already a long tradition in using digital images and fully computerized workflows. ML models are driven by data, and in contrast with many statistical or physical models, they can be very large and complex, with countless generic parameters. This inevitably raises two questions, namely, the tight dependence between the models and the datasets that feed them, and the interpretability of the models, which scales with its complexity. Any problems in the data used to train the model will be later reflected in their performance. This, together with the low interpretability of ML models, makes their implementation into the clinical workflow particularly difficult. Building tools for risk assessment and quality assurance of ML models must involve then two main points: interpretability and data-model dependency. After a joint introduction of both radiation oncology and ML, this paper reviews the main risks and current solutions when applying the latter to workflows in the former. Risks associated with data and models, as well as their interaction, are detailed. Next, the core concepts of interpretability, explainability, and data-model dependency are formally defined and illustrated with examples. Afterwards, a broad discussion goes through key applications of ML in workflows of radiation oncology as well as vendors' perspectives for the clinical implementation of ML.