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

Novel PET imaging biomarkers as predictors of postoperative recurrence in lung adenocarcinoma

BACKGROUND

The exploration of biomarkers is of crucial importance for the prognosis of cancer patients. The objective of this study was to ascertain the predictive value of positron emission tomography (PET) image-derived biomarkers, specifically the normalized distances from the hot spot of radiotracer uptake to the tumor centroid (NHOC) and the tumor perimeter (NHOP), in forecasting the recurrence risk and disease-free survival (DFS) in patients with operable stage IA-IIIA lung adenocarcinoma (LUAD).

METHODS

A retrospective analysis was conducted on 164 patients with surgically treated pathologically confirmed stage IA-IIIA LUAD, all of whom had prior 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (18F-FDG PET/CT) scans. In addition to conventional PET/CT parameters, we assessed the normalized distances from the maximum SUV to both the tumor centroid (NHOCmax) and the tumor perimeter (NHOPmax) as observed in the PET/CT images.

RESULTS

A total of 164 patients were included, with a median age of 65 years. NHOPmax exhibited the highest AUC of 0.682 (95% CI: 0.578-0.785), with a sensitivity of 78.8%. Correlation analysis showed that NHOPmax had low correlations with other metabolic parameters such as SUVmax, TLG, and MTV. In both univariate and multivariate analyses, NHOPmax was significantly associated with postoperative outcomes (P < 0.001, odds ratio 0.033). Survival analysis indicated that NHOPmax was an independent predictor of DFS (HR = 0.399, P < 0.05), with higher NHOPmax (> 0.43) associated with significantly better survival (P < 0.0001).

CONCLUSION

NHOPmax quantified from 18F-FDG PET/CT scans, could be a promising predictor of postoperative recurrence in patients with resectable LUAD.

Clinicopathologic and metabolic variables from 18F-FDG PET/CT in the prediction of recurrence pattern in stage I-III non-small cell lung cancer after curative surgery

AIM

This study aimed to analyze the clinicopathologic and metabolic parameters derived from staging 18F-FDG PET/CT that can predict recurrence patterns in non-small-cell lung cancer (NSCLC) after curative surgery.

MATERIAL AND METHODS

Retrospective study included stage I-III NSCLC patients with a baseline 18F-FDG PET/CT scan. Relapse patterns were analyzed based on location, lesion, and organ-specific recurrence. Clinicopathologic variables were recorded. Three distinct categories of variables were obtained: standardized uptake value (SUV)-based metrics, heterogeneity parameters, and morphological features. The relation of relapse patterns with clinicopathologic and metabolic parameters was analyzed using the uni-multivariate logistic regression.

RESULTS

Out of 173 patients, 104 experienced recurrences, with 66% presenting distant involvement and 56.7% exhibiting polymetastatic disease at initial recurrence. Patient age, pathologic lymphovascular invasion, and normalized SUVmax perimeter distance (nSPD) were considered as risk factors for early recurrence. Adenocarcinoma histology was identified as an independent variable for distant recurrence. Patient age, number of metastatic mediastinal lymph nodes at staging (nN), sphericity, normalized SUVpeak to centroid distance (nSCD), entropy, low gray-level run emphasis, and high gray-level run emphasis were independent variables for polymetastatic disease. Certain variables were correlated with organ-specific recurrence. Bone recurrence was related to nN and SUVmean. Brain recurrence was related to adenocarcinoma histology. Lung recurrence was associated with coefficient of variation and nSPD.

CONCLUSION

The metabolic profile of lung primary tumors obtained from 18F-FDG PET/CT seems to be predictive of recurrence patterns that are closely linked to the overall survival of NSCLC patients. These findings could help in the development of personalized follow-up strategies based on an individual's recurrence pattern.

Prognostic significance of normalized distance from maximum standardized uptake value to tumor centroid on [18F]FDG PET/CT in head and neck squamous cell carcinoma

OBJECTIVE

The maximum [18F]FDG uptake of a cancer lesion has been found to relocate from the center to the periphery during progression. This behavior suggests that the normalized distances from the hotspot of radiotracer uptake to the tumor centroid (NHOC) and to the tumor perimeter (NHOP) could serve as novel geometric PET parameters indicative of tumor aggressiveness. This study aimed to explore the prognostic relevance of NHOC and NHOP in [18F]FDG PET/CT for predicting the response to concurrent chemoradiotherapy (CCRT) and progression-free survival (PFS) in patients with head and neck squamous cell carcinoma (HNSCC).

MATERIALS AND METHODS

We retrospectively reviewed 116 HNSCC patients who received CCRT and were assessed with pre-treatment (PET1) and three months post-treatment PET/CT (PET2). Along with conventional PET parameters, NHOC and NHOP for primary tumors on PET1 and the percent changes in NHOC and NHOP between PET1 and PET2 were measured.

RESULTS

Of all the PET1 parameters assessed, NHOC was the most effective in predicting the CCRT response, achieving an area under the receiver operating characteristic curve of 0.645. In multivariate logistic regression and survival analysis, NHOC identified as an independent predictor for both complete metabolic response (P = .028) and PFS (P = .006). In a subgroup of 46 patients exhibiting residual primary tumors on PET2, both the percent changes in NHOC (P = .048) and NHOP (P = .041) were significantly associated with PFS.

CONCLUSIONS

NHOC and the percent changes in NHOC and NHOP following CCRT may serve as effective [18F]FDG PET/CT parameters for predicting clinical outcomes in HNSCC patients.

Clinical value of SUVpeak-to-tumor centroid distance on FDG PET/CT for predicting neoadjuvant chemotherapy response in patients with breast cancer

BACKGROUND

Since it has been found that the maximum metabolic activity of a cancer lesion shifts toward the lesion edge during cancer progression, normalized distances from the hot spot of radiotracer uptake to tumor centroid (NHOC) and tumor perimeter (NHOP) have been suggested as novel F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) parameters that can reflect cancer aggressiveness. This study aimed to investigate whether NHOC and NHOP parameters could predict pathological response to neoadjuvant chemotherapy (NAC) and progression-free survival (PFS) in breast cancer patients.

METHODS

This study retrospectively enrolled 135 female patients with breast cancer who underwent pretreatment FDG PET/CT and received NAC and subsequent surgical resection. From PET/CT images, normalized distances of maximum SUV and peak SUV-to-tumor centroid (NHOCmax and NHOCpeak) and -to-tumor perimeter (NHOPmax and NHOPpeak) were measured, in addition to conventional PET/CT parameters.

RESULTS

Of 135 patients, 32 (23.7%) achieved pathological complete response (pCR), and 34 (25.2%) had events during follow-up. In the receiver operating characteristic (ROC) curve analysis, NHOCmax showed the highest area under the ROC curve value (0.710) for predicting pCR, followed by NHOCpeak (0.694). In the multivariate logistic regression analysis, NHOCmax, NHOCpeak, and NHOPmax were independent predictors for pCR (p < 0.05). In the multivariate survival analysis, NHOCpeak (p = 0.026) was an independent predictor for PFS along with metabolic tumor volume, with patients having higher NHOCpeak showing worse PFS.

CONCLUSION

NHOCpeak on pretreatment FDG PET/CT could be a potential imaging parameter for predicting NAC response and survival in patients with breast cancer.

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.