Heterogeneity and irregularity of pretreatment 18F-fluorodeoxyglucose positron emission tomography improved prognostic stratification of p16-negative high-risk squamous cell carcinoma of the oropharynx

Association between maximum standarised uptake value (SUV) and local control in patients with oropharyngeal carcinoma treated with radiotherapy

OBJECTIVE

To analyse the prognostic ability of the maximum standardised uptake value (SUVmax) on local disease control in patients with oropharyngeal carcinoma treated with radiotherapy.

MATERIAL AND METHODS

Retrospective study of 105 patients with oropharyngeal carcinomas treated with radiotherapy, including chemo- and bio-radiotherapy, and who had a PET-CT scan prior to the start of treatment.

RESULT

Patients with a SUVmax value higher than 17.2 at the primary tumour site had a significantly higher risk of local recurrence. The 5-year local recurrence-free survival for patients with SUVmax less than or equal to 17.2 (n = 71) was 86.5% (95% CI 78.2-94.7 %), and for patients with SUVmax greater than 17.2 (n = 34) it was 55.8% (95% CI 36.0-75.6 %) (P = 0.0001). This difference in local control was maintained regardless of patients' HPV status. Specific survival was similarly lower for patients with a SUV greater than 17.2. The 5-year specific survival for patients with SUVmax greater than 17.2 was 39.5% (95% CI: 20.6-58.3 %), significantly shorter than that of patients with SUVmax equal to or less than 17.2, which was 77.3% (95% CI: 66.9-87.6 %) (P = 0.0001).

CONCLUSIONS

Patients with oropharyngeal carcinomas treated with radiotherapy with a SUVmax greater than 17.2 at the level of the primary tumour site had a significantly higher risk of local recurrence.

Asphericity derived from [18F]FDG PET as a new prognostic parameter in cervical cancer patients

The objective of this study was to assess the prognostic value of asphericity (ASP) and standardized uptake ratio (SUR) in cervical cancer patients. Retrospective analysis was performed on a group of 508 (aged 55 ± 12 years) previously untreated cervical cancer patients. All patients underwent a pretreatment [¹⁸F]FDG PET/CT study to assess the severity of the disease. The metabolic tumor volume (MTV) of the cervical cancer was delineated with an adaptive threshold method. For the resulting ROIs the maximum standardized uptake value (SUV_max) was measured. In addition, ASP and SUR were determined as previously described. Univariate Cox regression and Kaplan-Meier analysis with respect to event free survival (EFS), overall survival (OS), freedom from distant metastasis (FFDM) and locoregional control (LRC) was performed. Additionally, a multivariate Cox regression including clinically relevant parameters was performed. In the survival analysis, MTV and ASP were shown to be prognostic factors for all investigated endpoints. Tumor metabolism quantified with the SUV_max was not prognostic for any of the endpoints (p > 0.2). The SUR did not reach statistical significance either (p = 0.1, 0.25, 0.066, 0.053, respectively). In the multivariate analysis, the ASP remained a significant factor for EFS and LRC, while MTV was a significant factor for FFDM, indicating their independent prognostic value for the respective endpoints. The alternative parameter ASP has the potential to improve the prognostic value of [¹⁸F]FDG PET/CT for event-free survival and locoregional control in radically treated cervical cancer patients.

Prognostic and predictive values of baseline and mid-treatment FDG-PET in oropharyngeal carcinoma treated with primary definitive (chemo)radiation and impact of HPV status: review of current literature and emerging roles

BACKGROUND AND PURPOSE

This study provides a review of the literature assessing whether semiquantitative PET parameters acquired at baseline and/or during definitive (chemo)radiotherapy ("prePET" and "iPET") can predict survival outcomes in patients with oropharyngeal squamous cell carcinoma (OPC), and the impact of human papilloma virus (HPV) status.

MATERIAL AND METHODS

A literature search was carried out using PubMed and Embase between 2001 to 2021 in accordance with PRISMA.

RESULTS

The analysis included 22 FDG-PET/CT studies^1-22, 19 pre-PET and 3 both pre-PET and iPET^14,18,20,. The analysis involved 2646 patients, of which 1483 are HPV-positive (17 studies: 10 mixed and 7 HPV-positive only), 589 are HPV-negative, and 574 have unknown HPV status. Eighteen studies found significant correlations of survival outcomes with pre-PET parameters, most commonly primary or "Total" (combined primary and nodal) metabolic tumour volume and/or total lesional glycolysis. Two studies could not establish significant correlations and both employed SUVmax only. Two studies also could not establish significant correlations when taking into account of the HPV-positive population only. Because of the heterogeneity and lack of standardized methodology, no conclusions on optimal cut-off values can be drawn. Ten studies specifically evaluated HPV-positive patients: five showed positive correlation of pre-PET parameters and survival outcomes, but four of these studies did not include advanced T or N staging in multivariate analysis^1,6,15,22, and two studies only showed positive correlations after excluding high risk patients with smoking history⁷ or adverse CT features²². Two studies found that prePET parameters predicted treatment outcomes only in HPV-negative but not HPV-positive patients^10,16. Two studies found that iPET parameters could predict outcomes in HPV-positive patients but not prePET parameters^14,18.

CONCLUSION

The current literature supports high pre-treatment metabolic burden prior to definitive (chemo)radiotherapy can predict poor treatment outcomes for HPV-negative OPC patients. Evidence is conflicting and currently does not support correlation in HPV-positive patients.

18F-FDG PET/CT-Based Prognostic Survival Model After Surgery for Head and Neck Cancer

The aims of this multicenter study were to identify clinical and preoperative PET/CT parameters predicting overall survival (OS) and distant metastasis-free survival (DMFS) in a cohort of head and neck squamous cell carcinoma patients treated with surgery, to generate a prognostic model of OS and DMFS, and to validate this prognostic model with an independent cohort. Methods: A total of 382 consecutive patients with head and neck squamous cell carcinoma, divided into training (n = 318) and validation (n = 64) cohorts, were retrospectively included. The following PET/CT parameters were analyzed: clinical parameters, SUV_max, SUV_mean, metabolic tumor volume (MTV), total lesion glycolysis, and distance parameters for the primary tumor and lymph nodes defined by 2 segmentation methods (relative SUV_max threshold and absolute SUV threshold). Cox analyses were performed for OS and DMFS in the training cohort. The concordance index (c-index) was used to identify highly prognostic parameters. These prognostic parameters were externally tested in the validation cohort. Results: In multivariable analysis, the significant parameters for OS were T stage and nodal MTV, with a c-index of 0.64 (P < 0.001). For DMFS, the significant parameters were T stage, nodal MTV, and maximal tumor-node distance, with a c-index of 0.76 (P < 0.001). These combinations of parameters were externally validated, with c-indices of 0.63 (P < 0.001) and 0.71 (P < 0.001) for OS and DMFS, respectively. Conclusion: The nodal MTV associated with the maximal tumor-node distance was significantly correlated with the risk of DMFS. Moreover, this parameter, in addition to clinical parameters, was associated with a higher risk of death. These prognostic factors may be used to tailor individualized treatment.

Longitudinal CT Imaging to Explore the Predictive Power of 3D Radiomic Tumour Heterogeneity in Precise Imaging of Mantle Cell Lymphoma (MCL)

The study's primary aim is to evaluate the predictive performance of CT-derived 3D radiomics for MCL risk stratification. The secondary objective is to search for radiomic features associated with sustained remission. Included were 70 patients: 31 MCL patients and 39 control subjects with normal axillary lymph nodes followed over five years. Radiomic analysis of all targets (n = 745) was performed and features selected using the Mann Whitney U test; the discriminative power of identifying "high-risk MCL" was evaluated by receiver operating characteristics (ROC). The four radiomic features, "Uniformity", "Entropy", "Skewness" and "Difference Entropy" showed predictive significance for relapse (p < 0.05)-in contrast to the routine size measurements, which showed no relevant difference. The best prognostication for relapse achieved the feature "Uniformity" (AUC-ROC-curve 0.87; optimal cut-off ≤0.0159 to predict relapse with 87% sensitivity, 65% specificity, 69% accuracy). Several radiomic features, including the parameter "Short Axis," were associated with sustained remission. CT-derived 3D radiomics improves the predictive estimation of MCL patients; in combination with the ability to identify potential radiomic features that are characteristic for sustained remission, it may assist physicians in the clinical management of MCL.

Prediction of the local treatment outcome in patients with oropharyngeal squamous cell carcinoma using deep learning analysis of pretreatment FDG-PET images

Background

This study aimed to assess the utility of deep learning analysis using pretreatment FDG-PET images to predict local treatment outcome in oropharyngeal squamous cell carcinoma (OPSCC) patients.

Methods

One hundred fifty-four OPSCC patients who received pretreatment FDG-PET were included and divided into training (n = 102) and test (n = 52) sets. The diagnosis of local failure and local progression-free survival (PFS) rates were obtained from patient medical records. In deep learning analyses, axial and coronal images were assessed by three different architectures (AlexNet, GoogLeNET, and ResNet). In the training set, FDG-PET images were analyzed after the data augmentation process for the diagnostic model creation. A multivariate clinical model was also created using a binomial logistic regression model from a patient’s clinical characteristics. The test data set was subsequently analyzed for confirmation of diagnostic accuracy. Assessment of local PFS rates was also performed.

Results

Training sessions were successfully performed with an accuracy of 74–89%. ROC curve analyses revealed an AUC of 0.61–0.85 by the deep learning model in the test set, whereas it was 0.62 by T-stage, 0.59 by clinical stage, and 0.74 by a multivariate clinical model. The highest AUC (0.85) was obtained with deep learning analysis of ResNet architecture. Cox proportional hazards regression analysis revealed deep learning-based classification by a multivariate clinical model (P < .05), and ResNet (P < .001) was a significant predictor of the treatment outcome. In the Kaplan-Meier analysis, the deep learning-based classification divided the patient’s local PFS rate better than the T-stage, clinical stage, and a multivariate clinical model.

Conclusions

Deep learning-based diagnostic model with FDG-PET images indicated its possibility to predict local treatment outcomes in OPSCCs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08599-6.

Deep Learning for Fully Automated Prediction of Overall Survival in Patients with Oropharyngeal Cancer Using FDG-PET Imaging

PURPOSE

Accurate prognostic stratification of patients with oropharyngeal squamous cell carcinoma (OPSCC) is crucial. We developed an objective and robust deep learning-based fully-automated tool called the DeepPET-OPSCC biomarker for predicting overall survival (OS) in OPSCC using [¹⁸F]fluorodeoxyglucose (FDG)-PET imaging.

EXPERIMENTAL DESIGN

The DeepPET-OPSCC prediction model was built and tested internally on a discovery cohort (n = 268) by integrating five convolutional neural network models for volumetric segmentation and ten models for OS prognostication. Two external test cohorts were enrolled-the first based on the Cancer Imaging Archive (TCIA) database (n = 353) and the second being a clinical deployment cohort (n = 31)-to assess the DeepPET-OPSCC performance and goodness of fit.

RESULTS

After adjustment for potential confounders, DeepPET-OPSCC was found to be an independent predictor of OS in both discovery and TCIA test cohorts [HR = 2.07; 95% confidence interval (CI), 1.31-3.28 and HR = 2.39; 95% CI, 1.38-4.16; both P = 0.002]. The tool also revealed good predictive performance, with a c-index of 0.707 (95% CI, 0.658-0.757) in the discovery cohort, 0.689 (95% CI, 0.621-0.757) in the TCIA test cohort, and 0.787 (95% CI, 0.675-0.899) in the clinical deployment test cohort; the average time taken was 2 minutes for calculation per exam. The integrated nomogram of DeepPET-OPSCC and clinical risk factors significantly outperformed the clinical model [AUC at 5 years: 0.801 (95% CI, 0.727-0.874) vs. 0.749 (95% CI, 0.649-0.842); P = 0.031] in the TCIA test cohort.

CONCLUSIONS

DeepPET-OPSCC achieved an accurate OS prediction in patients with OPSCC and enabled an objective, unbiased, and rapid assessment for OPSCC prognostication.

Prediction of the treatment outcome using machine learning with FDG-PET image-based multiparametric approach in patients with oral cavity squamous cell carcinoma

AIM

To investigate the value of machine learning-based multiparametric analysis using 2-[¹⁸F]-fluoro-2-deoxy-d-glucose positron-emission tomography (FDG-PET) images to predict treatment outcome in patients with oral cavity squamous cell carcinoma (OCSCC).

MATERIALS AND METHODS

Ninety-nine patients with OCSCC who received pretreatment integrated FDG-PET/computed tomography (CT) were included. They were divided into the training (66 patients) and validation (33 patients) cohorts. The diagnosis of local control or local failure was obtained from patient's medical records. Conventional FDG-PET parameters, including the maximum and mean standardised uptake values (SUVmax and SUVmean), metabolic tumour volume (MTV), and total lesion glycolysis (TLG), quantitative tumour morphological parameters, intratumoural histogram, and texture parameters, as well as T-stage and clinical stage, were evaluated by a machine learning analysis. The diagnostic ability of T-stage, clinical stage, and conventional FDG-PET parameters (SUVmax, SUVmean, MTV, and TLG) was also assessed separately.

RESULTS

In support-vector machine analysis of the training dataset, the final selected parameters were T-stage, SUVmax, TLG, morphological irregularity, entropy, and run-length non-uniformity. In the validation dataset, the diagnostic performance of the created algorithm was as follows: sensitivity 0.82, specificity 0.7, positive predictive value 0.86, negative predictive value 0.64, and accuracy 0.79. In a univariate analysis using conventional FDG-PET parameters, T-stage and clinical stage, diagnostic accuracy of each variable was revealed as follows: 0.61 in T-stage, 0.61 in clinical stage, 0.64 in SUVmax, 0.61 in SUVmean, 0.64 in MTV, and 0.7 in TLG.

CONCLUSION

A machine-learning-based approach to analysing FDG-PET images by multiparametric analysis might help predict local control or failure in patients with OCSCC.

Evaluation of the Prognostic Value of FDG PET/CT Parameters for Patients With Surgically Treated Head and Neck Cancer: A Systematic Review

Importance

Head and neck squamous cell cancer (HNSCC) represents the seventh most frequent cancer worldwide. More than half of the patients diagnosed with HNSCC are treated with primary surgery.

Objective

To report the available evidence on the value of quantitative parameters of fluorodeoxyglucose F 18-labeled positron emission tomography and computed tomography (FDG-PET/CT) performed before surgical treatment of HNSCC to estimate overall survival (OS), disease-free survival (DFS), and distant metastasis (DM) and to discuss their limitations.

Evidence Review

A systematic review of the English-language literature in PubMed/MEDLINE and ScienceDirect published between January 2003 and February 15, 2019, was performed between March 1 and July 27, 2019, to identify articles addressing the association between preoperative FDG-PET/CT parameters and oncological outcomes among patients with HNSCC. Articles included those that addressed the following: (1) cancer of the oral cavity, oropharynx, hypopharynx, or larynx; (2) surgically treated (primary or for salvage); (3) pretreatment FDG-PET/CT; (4) quantitative or semiquantitative evaluation of the FDG-PET/CT parameters; and (5) the association between the value of FDG-PET/CT parameters and clinical outcomes. Quality assessment was performed using the Oxford Centre for Evidence-Based Medicine level of evidence.

Findings

A total of 128 studies were retrieved from the databases, and 36 studies met the inclusion criteria; these studies comprised 3585 unique patients with a median follow-up of 30.6 months (range, 16-53 months). Of these 36 studies, 32 showed an association between at least 1 FDG-PET/CT parameter and oncological outcomes (OS, DFS, and DM). The FDG-PET/CT volumetric parameters (metabolic tumor volume [MTV] and total lesion glycolysis [TLG]) were independent prognostic factors in most of the data, with a higher prognostic value than the maximum standard uptake value (SUVmax). For example, in univariate analysis of OS, the SUVmax was correlated with OS in 5 of 11 studies, MTV in 11 of 12 studies, and TLG in 6 of 9 studies. The spatial distribution of metabolism via textural indices seemed promising, although that factor is currently poorly evaluated: only 3 studies analyzed data from radiomics indices.

Conclusions and Relevance

The findings of this study suggest that the prognostic effectiveness of FDG-PET/CT parameters as biomarkers of OS, DFS, and DM among patients with HNSCC treated with surgery may be valuable. The volumetric parameters (MTV and TLG) seemed relevant for identifying patients with a higher risk of postsurgical disease progression who could receive early therapeutic intervention to improve their prognosis. However, further large-scale studies including exclusively surgery-treated patients stratified according to localization and further analysis of the textural indices are required to define a reliable FDG-PET/CT-based prognostic model of mortality and recurrence risk for these patients.

Texture Analysis Using Preoperative Positron Emission Tomography Images May Predict the Prognosis of Patients With Resectable Oral Squamous Cell Carcinoma

PURPOSE

Texture analysis is a computer-assisted technique used to measure intratumoral heterogeneity, which is known to have important roles in cancer research. This study aimed to assess the potential prognostic values of textural features extracted from preoperative ¹⁸F-fluorodeoxyglucose positron emission tomography images in patients with resectable oral squamous cell carcinoma.

PATIENTS AND METHODS

This retrospective cohort study included patients with oral squamous cell carcinoma who underwent resection surgery. We extracted 31 textural indices from preoperative positron emission tomography images. Overall survival (OS) and disease-free survival (DFS) were chosen as the primary outcome variables, and the primary predictor variables were age, sex, primary tumor location, pathological T and N classification, histologic differentiation, resected margin, perineural and lymphovascular invasion, maximum standardized uptake value, and the 14 textural indices selected in the factor analysis. We analyzed OS and DFS using Kaplan-Meier curves, and the differences between survival curves were determined using a log-rank test. The independent prognostic factors were assessed using the Cox-proportional hazards model.

RESULTS

We enrolled 81 patients (median age, 67.3 years; range, 32 to 88 years). The median follow-up duration was 50.1 months (range, 6.3 to 133.7 months). The univariable and multivariable analyses revealed that higher entropy values (≥1.91) were associated with worse OS (hazard ratio, 21.49; 95% confidence interval, 1.36 to 340.71; P = .03) and DFS (hazard ratio, 50.69; 95% confidence interval, 5.23 to 491.18; P = .001).

CONCLUSIONS

This study showed that entropy is a statistically significant prognostic factor of both OS and DFS. Texture analysis using preoperative positron emission tomography images may contribute to risk stratification.

Development and validation of a prognostic model incorporating [18F]FDG PET/CT radiomics for patients with minor salivary gland carcinoma

OBJECTIVES

The aim of this study was to develop and validate a prognostic model incorporating [¹⁸F]FDG PET/CT radiomics for patients of minor salivary gland carcinoma (MSGC).

METHODS

We retrospectively reviewed the pretreatment [¹⁸F]FDG PET/CT images of 75 MSGC patients treated with curative intent. Using a 1.5:1 ratio, the patients were randomly divided into a training and validation group. The main outcome measurements were overall survival (OS) and relapse-free survival (RFS). All of the patients were followed up for at least 30 months or until death. Following segmentation of tumors and lymph nodes on PET images, radiomic features were extracted. The prognostic significance of PET radiomics and clinical parameters in the training group was examined using receiver operating characteristic curve analysis. Variables showing a significant impact on OS and RFS were entered into multivariable Cox regression models. Recursive partitioning analysis was subsequently implemented to devise a prognostic index, whose performance was examined in the validation group. Finally, the performance of the index was compared with clinical variables in the entire cohort and nomograms for surgically treated cases.

RESULTS

The training and validation groups consisted of 45 and 30 patients, respectively. The median follow-up time in the entire cohort was 59.5 months. Eighteen relapse, 19 dead, and thirteen relapse, eight dead events were found in the training and validation cohorts, respectively. In the training group, two factors were identified as independently associated with poor OS, i.e., (1) tumors with both high maximum standardized uptake value (SUV_max) and discretized intensity entropy and (2) poor performance status or N2c-N3 stage. A prognostic model based on the above factors was devised and showed significant higher concordance index (C-index) for OS than those of AJCC stage and high-risk histology (C-index: 0.83 vs. 0.65, P = 0.005; 0.83 vs. 0.54, P < 0.001, respectively). This index also demonstrated superior performance than nomogram for OS (C-index: 0.88 vs. 0.70, P = 0.017) and that for RFS (C-index: 0.87 vs. 0.72, P = 0.004).

CONCLUSIONS

We devised a novel prognostic model that incorporates [¹⁸F]FDG PET/CT radiomics and may help refine outcome prediction in patients with MSGC.

Deep learning analysis using FDG-PET to predict treatment outcome in patients with oral cavity squamous cell carcinoma

OBJECTIVE

To assess the utility of deep learning analysis using ¹⁸F-fluorodeoxyglucose (FDG) uptake by positron emission tomography (PET/CT) to predict disease-free survival (DFS) in patients with oral cavity squamous cell carcinoma (OCSCC).

METHODS

One hundred thirteen patients with OCSCC who received pretreatment FDG-PET/CT were included. They were divided into training (83 patients) and test (30 patients) sets. The diagnosis of treatment control/failure and the DFS rate were obtained from patients' medical records. In deep learning analyses, three planes of axial, coronal, and sagittal FDG-PET images were assessed by ResNet-101 architecture. In the training set, image analysis was performed for the diagnostic model creation. The test data set was subsequently analyzed for confirmation of diagnostic accuracy. T-stage, clinical stage, and conventional FDG-PET parameters (the maximum and mean standardized uptake value (SUVmax and SUVmean), heterogeneity index, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were also assessed with determining the optimal cutoff from training dataset and then validated their diagnostic ability from test dataset.

RESULTS

In dividing into patients with treatment control and failure, the highest diagnostic accuracy of 0.8 was obtained using deep learning classification, with a sensitivity of 0.8, specificity of 0.8, positive predictive value of 0.89, and negative predictive value of 0.67. In the Kaplan-Meier analysis, the DFS rate was significantly different only with the analysis of deep learning-based classification (p < .01).

CONCLUSIONS

Deep learning-based diagnosis with FDG-PET images may predict treatment outcome in patients with OCSCC.

KEY POINTS

• Deep learning-based diagnosis of FDG-PET images showed the highest diagnostic accuracy to predict the treatment outcome in patients with oral cavity squamous cell carcinoma. • Deep learning-based diagnosis was shown to differentiate patients between good and poor disease-free survival more clearly than conventional T-stage, clinical stage, and conventional FDG-PET-based parameters.

Radiomic features of glucose metabolism enable prediction of outcome in mantle cell lymphoma

PURPOSE

To determine whether [¹⁸F]FDG PET/CT-derived radiomic features alone or in combination with clinical, laboratory and biological parameters are predictive of 2-year progression-free survival (PFS) in patients with mantle cell lymphoma (MCL), and whether they enable outcome prognostication.

METHODS

Included in this retrospective study were 107 treatment-naive MCL patients scheduled to receive CD20 antibody-based immuno(chemo)therapy. Standardized uptake values (SUV), total lesion glycolysis, and 16 co-occurrence matrix radiomic features were extracted from metabolic tumour volumes on pretherapy [¹⁸F]FDG PET/CT scans. A multilayer perceptron neural network in combination with logistic regression analyses for feature selection was used for prediction of 2-year PFS. International prognostic indices for MCL (MIPI and MIPI-b) were calculated and combined with the radiomic data. Kaplan-Meier estimates with log-rank tests were used for PFS prognostication.

RESULTS

SUVmean (OR 1.272, P = 0.013) and Entropy (heterogeneity of glucose metabolism; OR 1.131, P = 0.027) were significantly predictive of 2-year PFS: median areas under the curve were 0.72 based on the two radiomic features alone, and 0.82 with the addition of clinical/laboratory/biological data. Higher SUVmean in combination with higher Entropy (SUVmean >3.55 and entropy >3.5), reflecting high "metabolic risk", was associated with a poorer prognosis (median PFS 20.3 vs. 39.4 months, HR 2.285, P = 0.005). The best PFS prognostication was achieved using the MIPI-bm (MIPI-b and metabolic risk combined): median PFS 43.2, 38.2 and 20.3 months in the low-risk, intermediate-risk and high-risk groups respectively (P = 0.005).

CONCLUSION

In MCL, the [¹⁸F]FDG PET/CT-derived radiomic features SUVmean and Entropy may improve prediction of 2-year PFS and PFS prognostication. The best results may be achieved using a combination of metabolic, clinical, laboratory and biological parameters.