What is the best way to contour lung tumors on PET scans? Multiobserver validation of a gradient-based method using a NSCLC digital PET phantom

Prognostic Significance of 18 F-FDG PET/CT Radiomics in Patients With Resectable Pancreatic Ductal Adenocarcinoma Undergoing Curative Surgery

PURPOSE

This study aimed to investigate the prognostic significance of PET/CT radiomics to predict overall survival (OS) in patients with resectable pancreatic ductal adenocarcinoma (PDAC).

METHODS

We enrolled 627 patients with resectable PDAC who underwent preoperative 18 F-FDG PET/CT and subsequent curative surgery. Radiomics analysis of the PET/CT images for the primary tumor was performed using the Chang-Gung Image Texture Analysis toolbox. Radiomics features were subjected to least absolute shrinkage and selection operator (LASSO) regression to select the most valuable imaging features of OS. The prognostic significance was evaluated by Cox proportional hazards regression analysis. Conventional PET parameters and LASSO score were assessed as predictive factors for OS by time-dependent receiver operating characteristic curve analysis.

RESULTS

During a mean follow-up of 28.8 months, 378 patients (60.3%) died. In the multivariable Cox regression analysis, tumor differentiation, resection margin status, tumor stage, and LASSO score were independent prognostic factors for OS (HR, 1.753, 1.669, 2.655, and 2.946; all P < 0.001, respectively). The time-dependent receiver operating characteristic curve analysis showed that the LASSO score had better predictive performance for OS than conventional PET parameters.

CONCLUSIONS

The LASSO score using the 18 F-FDG PET/CT radiomics of the primary tumor was the independent prognostic factor for predicting OS in patients with resectable PDAC and may be helpful in determining therapeutic and follow-up plans for these patients.

Three-Dimensional Margin as a Predictor of Local Tumor Progression after Microwave Ablation: Intraprocedural versus 4-8-Week Postablation Assessment

PURPOSE

To evaluate the prognostic accuracy of intraprocedural and 4-8-week (current standard) post-microwave ablation zone (AZ) and margin assessments for prediction of local tumor progression (LTP) using 3-dimensional (3D) software.

MATERIALS AND METHODS

Data regarding 100 colorectal liver metastases (CLMs) in 75 patients were collected from 2 prospective fluorodeoxyglucose positron emission tomography (PET)/computed tomography (CT)-guided microwave ablation (MWA) trials. The target CLMs and theoretical 5- and 10-mm margins were segmented and registered intraprocedurally and at 4-8 weeks after MWA contrast-enhanced CT (or magnetic resonance [MR] imaging) using the same methodology and 3D software. Tumor and 5- and 10-mm minimal margin (MM) volumes not covered by the AZ were defined as volumes of insufficient coverage (VICs). The intraprocedural and 4-8-week post-MWA VICs were compared as predictors of LTP using receiver operating characteristic curve analysis.

RESULTS

The median follow-up time was 19.6 months (interquartile range, 7.97-36.5 months). VICs for 5- and 10-mm MMs were predictive of LTP at both time assessments. The highest accuracy for the prediction of LTP was documented with the intra-ablation 5-mm VIC (area under the curve [AUC], 0.78; 95% confidence interval, 0.66-0.89). LTP for a VIC of 6-10-mm margin category was 11.4% compared with 4.3% for >10-mm margin category (P < .001).

CONCLUSIONS

A 3D 5-mm MM is a critical endpoint of thermal ablation, whereas optimal local tumor control is noted with a 10-mm MM. Higher AUCs for prediction of LTP were achieved for intraprocedural evaluation than for the 4-8-week postablation 3D evaluation of the AZ.

Methyl-11C-L-methionine positron emission tomography for radiotherapy planning for recurrent malignant glioma

OBJECTIVE

To investigate differences in uptake regions between methyl-11C-L-methionine positron emission tomography (11C-MET PET) and gadolinium (Gd)-enhanced magnetic resonance imaging (MRI), and their impact on dose distribution, including changing of the threshold for tumor boundaries.

METHODS

Twenty consecutive patients with grade 3 or 4 glioma who had recurrence after postoperative radiotherapy (RT) between April 2016 and October 2017 were examined. The study was performed using simulation with the assumption that all patients received RT. The clinical target volume (CTV) was contoured using the Gd-enhanced region (CTV(Gd)), the tumor/normal tissue (T/N) ratios of 11C-MET PET of 1.3 and 2.0 (CTV (T/N 1.3), CTV (T/N 2.0)), and the PET-edge method (CTV(P-E)) for stereotactic RT planning. Differences among CTVs were evaluated. The brain dose at each CTV and the dose at each CTV defined by 11C-MET PET using MRI as the reference were evaluated.

RESULTS

The Jaccard index (JI) for concordance of CTV (Gd) with CTVs using 11C-MET PET was highest for CTV (T/N 2.0), with a value of 0.7. In a comparison of pixel values of MRI and PET, the correlation coefficient for cases with higher JI was significantly greater than that for lower JI cases (0.37 vs. 0.20, P = 0.007). D50% of the brain in RT planning using each CTV differed significantly (P = 0.03) and that using CTV (T/N 1.3) were higher than with use of CTV (Gd). V90% and V95% for each CTV differed in a simulation study for actual treatment using CTV (Gd) (P = 1.0 × 10-7 and 3.0 × 10-9, respectively) and those using CTV (T/N 1.3) and CTV (P-E) were lower than with CTV (Gd).

CONCLUSIONS

The region of 11C-MET accumulation is not necessarily consistent with and larger than the Gd-enhanced region. A change of the tumor boundary using 11C-MET PET can cause significant changes in doses to the brain and the CTV.

Selective Personalized RadioImmunotherapy for Locally Advanced Non-Small-Cell Lung Cancer Trial (SPRINT)

PURPOSE

Standard therapy for locally advanced non-small-cell lung cancer (LA-NSCLC) is concurrent chemoradiotherapy followed by adjuvant durvalumab. For biomarker-selected patients with LA-NSCLC, we hypothesized that sequential pembrolizumab and risk-adapted radiotherapy, without chemotherapy, would be well-tolerated and effective.

METHODS

Patients with stage III NSCLC or unresectable stage II NSCLC and an Eastern Cooperative Oncology Group performance status of 0-1 were eligible for this trial. Patients with a PD-L1 tumor proportion score (TPS) of ≥50% received three cycles of induction pembrolizumab (200 mg, once every 21 days), followed by a 20-fraction course of risk-adapted thoracic radiotherapy (55 Gy delivered to tumors or lymph nodes with metabolic volume exceeding 20 cc, 48 Gy delivered to smaller lesions), followed by consolidation pembrolizumab to complete a 1-year treatment course. The primary study end point was 1-year progression-free survival (PFS). Secondary end points included response rates after induction pembrolizumab, overall survival (OS), and adverse events.

RESULTS

Twenty-five patients with a PD-L1 TPS of ≥50% were enrolled. The median age was 71, most patients (88%) had stage IIIA or IIIB disease, and the median PD-L1 TPS was 75%. Two patients developed disease progression during induction pembrolizumab, and two patients discontinued pembrolizumab after one infusion because of immune-related adverse events. Using RECIST criteria, 12 patients (48%) exhibited a partial or complete response after induction pembrolizumab. Twenty-four patients (96%) received definitive thoracic radiotherapy. The 1-year PFS rate is 76%, satisfying our efficacy objective. One- and 2-year OS rates are 92% and 76%, respectively. The most common grade 3 adverse events were colitis (n = 2, 8%) and esophagitis (n = 2, 8%), and no higher-grade treatment-related adverse events have occurred.

CONCLUSION

Pembrolizumab and risk-adapted radiotherapy, without chemotherapy, are a promising treatment approach for patients with LA-NSCLC with a PD-L1 TPS of ≥50%.

Prognostic Value of Radiomic Analysis Using Pre- and Post-Treatment 18F-FDG-PET/CT in Patients with Laryngeal Cancer and Hypopharyngeal Cancer

BACKGROUND

The prognostic value of conducting 18F-FDG PET/CT imaging has yielded different results in patients with laryngeal cancer and hypopharyngeal cancer, but these results are controversial, and there is a lack of dedicated studies on each type of cancer. This study aimed to evaluate whether combining radiomic analysis of pre- and post-treatment 18F-FDG PET/CT imaging features and clinical parameters has additional prognostic value in patients with laryngeal cancer and hypopharyngeal cancer.

METHODS

From 2008 to 2016, data on patients diagnosed with cancer of the larynx and hypopharynx were retrospectively collected. The patients underwent pre- and post-treatment 18F-FDG PET/CT imaging. The values of ΔPre-Post PET were measured from the texture features. Least absolute shrinkage and selection operator (LASSO) Cox regression was used to select the most predictive features to formulate a Rad-score for both progression-free survival (PFS) and overall survival (OS). Kaplan-Meier curve analysis and Cox regression were employed to assess PFS and OS. Then, the concordance index (C-index) and calibration plot were used to evaluate the performance of the radiomics nomogram.

RESULTS

Study data were collected for a total of 91 patients. The mean follow-up period was 71.5 mo. (8.4-147.3). The Rad-score was formulated based on the texture parameters and was significantly associated with both PFS (p = 0.024) and OS (p = 0.009). When predicting PFS, only the Rad-score demonstrated a significant association (HR 2.1509, 95% CI [1.100-4.207], p = 0.025). On the other hand, age (HR 1.116, 95% CI [1.041-1.197], p = 0.002) and Rad-score (HR 33.885, 95% CI [2.891-397.175], p = 0.005) exhibited associations with OS. The Rad-score value showed good discrimination when it was combined with clinical parameters in both PFS (C-index 0.802-0.889) and OS (C-index 0.860-0.958). The calibration plots also showed a good agreement between the observed and predicted survival probabilities.

CONCLUSIONS

Combining clinical parameters with radiomics analysis of pre- and post-treatment 18F-FDG PET/CT parameters in patients with laryngeal cancer and hypopharyngeal cancer might have additional prognostic value.

Clinico-biological-radiomics (CBR) based machine learning for improving the diagnostic accuracy of FDG-PET false-positive lymph nodes in lung cancer

BACKGROUND

The main problem of positron emission tomography/computed tomography (PET/CT) for lymph node (LN) staging is the high false positive rate (FPR). Thus, we aimed to explore a clinico-biological-radiomics (CBR) model via machine learning (ML) to reduce FPR and improve the accuracy for predicting the hypermetabolic mediastinal-hilar LNs status in lung cancer than conventional PET/CT.

METHODS

A total of 260 lung cancer patients with hypermetabolic mediastinal-hilar LNs (SUVmax ≥ 2.5) were retrospectively reviewed. Patients were treated with surgery with systematic LN resection and pathologically divided into the LN negative (LN-) and positive (LN +) groups, and randomly assigned into the training (n = 182) and test (n = 78) sets. Preoperative CBR dataset containing 1738 multi-scale features was constructed for all patients. Prediction models for hypermetabolic LNs status were developed using the features selected by the supervised ML algorithms, and evaluated using the classical diagnostic indicators. Then, a nomogram was developed based on the model with the highest area under the curve (AUC) and the lowest FPR, and validated by the calibration plots.

RESULTS

In total, 109 LN- and 151 LN + patients were enrolled in this study. 6 independent prediction models were developed to differentiate LN- from LN + patients using the selected features from clinico-biological-image dataset, radiomics dataset, and their combined CBR dataset, respectively. The DeLong test showed that the CBR Model containing all-scale features held the highest predictive efficiency and the lowest FPR among all of established models (p < 0.05) in both the training and test sets (AUCs of 0.90 and 0.89, FPRs of 12.82% and 6.45%, respectively) (p < 0.05). The quantitative nomogram based on CBR Model was validated to have a good consistency with actual observations.

CONCLUSION

This study presents an integrated CBR nomogram that can further reduce the FPR and improve the accuracy of hypermetabolic mediastinal-hilar LNs evaluation than conventional PET/CT in lung cancer, thereby greatly reducing the risk of overestimation and assisting for precision treatment.

The usefulness of [68 Ga]Ga-DOTA-JR11 PET/CT in patients with meningioma: comparison with MRI

PURPOSE

Clinical studies of PET imaging using SSTR2 agonists have demonstrated high accuracy and correlation with SSTR2 expression in meningiomas. However, the usefulness of the SSTR2 antagonist with [68 Ga]Ga-DOTA-JR11 is uncertain. To evaluate the diagnostic performance of [68 Ga]Ga-DOTA-JR11 PET/CT and to clarify tumor characteristics in patients with suspected meningiomas.

MATERIALS AND METHODS

Patients with suspected de novo or recurrent meningioma in complex locations or atypical images were enrolled from August 2021 to October 2022 in prospective study. All patients underwent contrast-enhanced MRI (CE-MRI), [68 Ga]Ga-DOTA-JR11 PET/CT, and histopathological evaluation. Tumor uptake of [68 Ga]Ga-DOTA-JR11 was measured by SUVmax and tumor-endocranium ratio (TBR). Diagnostic performance was compared between PET and MRI.

RESULTS

Of 36 (50.0 ± 13.0 years of age, 20 women) patients, 32 were histopathologically confirmed meningiomas and four with other tumors. [68 Ga]Ga-DOTA-JR11 uptake was significantly higher in meningioma patients than in those with other tumors (SUVmax: 13.6 ± 7.7 vs. 5.2 ± 3.0, P < 0.001; TBR: 64.2 ± 27.7 vs. 25.0 ± 18.9, P = 0.001). [68 Ga]Ga-DOTA-JR11 PET/CT detected 31 meningiomas, while CE-MRI detected 17 meningiomas of 25 initial diagnosis and 11 recurrent tumors; [68 Ga]Ga-DOTA-JR11 PET had an incremental diagnostic value of 24% (6/25) over MRI in the group of initial diagnosis. There was no statistically significant difference in diagnostic efficacy between PET and MRI (P = 0.45) for all 36 patients. In skull base meningiomas, PET provided a more definitive diagnosis of pituitary involvement (in 12, not in12), compared to MRI (in eight, possible in six, possible not in six, not in four). PET revealed bone involvement in all 14 patients proven by pathology, while MRI identified only 11.

CONCLUSIONS

[68 Ga]Ga-DOTA-JR11 PET/CT provided high image quality and presented an ideal diagnostic performance in detecting meningioma and evaluating the involvement of the pituitary and bone. The study provides valuable evidence for the use of [68 Ga]Ga-DOTA-JR11 PET/CT as a complementary imaging modality to CE-MRI in the evaluation of meningiomas.

Prediction of Response to 177Lu-PSMA Therapy Based on Tumor-to-Kidney Ratio on Pretherapeutic PSMA PET/CT and Posttherapeutic Tumor-Dose Evaluation in mCRPC

The aim of this study was to analyze the absorbed dose of 177Lu-PSMA in osseous versus lymphatic metastases in patients with metastatic castration-resistant prostate cancer across therapy cycles and to relate those data to therapeutic success. In addition, pretherapeutic prostate-specific membrane antigen (PSMA) PET/CT was evaluated for its ability to predict response behavior. Methods: The study comprised 30 patients with metastatic castration-resistant prostate cancer, each receiving at least 3 cycles of 177Lu-PSMA therapy. Prostate-specific antigen (PSA) values between baseline and 6 wk after the third therapy cycle were used to classify the patients as responders (PSA decline ≥ 50%) or nonresponders (unchanged or increasing PSA level). Quantitative SPECT/CT images were acquired 24, 48, and 168 h after application of 177Lu-PSMA. The absorbed dose for tumor lesions was calculated with dosimetry software. From the pretherapeutic PET/CT scan, the tumor-to-kidney uptake ratio was determined for different SUVs. Results: Regardless of patient response, the kidneys received a mean dose of 0.55 ± 0.20 Gy/GBq per cycle. In the first therapy cycle, the lymph node lesions received a mean dose of 3.73 ± 1.65 Gy/GBq in responders and 1.86 ± 1.25 Gy/GBq in nonresponders (P < 0.01). For bone lesions, the respective mean doses were 3.47 ± 2.00 Gy/GBq and 1.48 ± 0.95 Gy/GBq (P < 0.01). When successive therapy cycles were compared, the mean dose was found to have been reduced from the first to the second cycle by 27% for lymph nodes and by 33% for bone lesions. A significant difference (P < 0.01) in the ratio of lymph node and bone lesion uptake to kidney uptake between responders and nonresponders could be deduced from the pretherapeutic PET/CT scan. Conclusion: Significantly higher doses were achieved for lymph node and bone lesions in responders. The highest absorbed dose, for both lymphatic and osseous lesions, was achieved in the first cycle, decreasing in the second therapy cycle thereafter despite unchanged therapy activities. It may be possible to estimate the response to therapy from the ratio of tumor uptake to kidney uptake obtained from the pretherapeutic PSMA PET/CT scans.

Semi-automated segmentation methods of SSTR PET for dosimetry prediction in refractory meningioma patients treated by SSTR-targeted peptide receptor radionuclide therapy

OBJECTIVES

Tumor dosimetry with somatostatin receptor-targeted peptide receptor radionuclide therapy (SSTR-targeted PRRT) by 177Lu-DOTATATE may contribute to improved treatment monitoring of refractory meningioma. Accurate dosimetry requires reliable and reproducible pretherapeutic PET tumor segmentation which is not currently available. This study aims to propose semi-automated segmentation methods to determine metabolic tumor volume with pretherapeutic 68Ga-DOTATOC PET and evaluate SUVmean-derived values as predictive factors for tumor-absorbed dose.

METHODS

Thirty-nine meningioma lesions from twenty patients were analyzed. The ground truth PET and SPECT volumes (VolGT-PET and VolGT-SPECT) were computed from manual segmentations by five experienced nuclear physicians. SUV-related indexes were extracted from VolGT-PET and the semi-automated PET volumes providing the best Dice index with VolGT-PET (Volopt) across several methods: SUV absolute-value (2.3)-threshold, adaptative methods (Jentzen, Otsu, Contrast-based method), advanced gradient-based technique, and multiple relative thresholds (% of tumor SUVmax, hypophysis SUVmean, and meninges SUVpeak) with optimal threshold optimized. Tumor-absorbed doses were obtained from the VolGT-SPECT, corrected for partial volume effect, performed on a 360° whole-body CZT-camera at 24, 96, and 168 h after administration of 177Lu-DOTATATE.

RESULTS

Volopt was obtained from 1.7-fold meninges SUVpeak (Dice index 0.85 ± 0.07). SUVmean and total lesion uptake (SUVmeanxlesion volume) showed better correlations with tumor-absorbed doses than SUVmax when determined with the VolGT (respective Pearson correlation coefficients of 0.78, 0.67, and 0.56) or Volopt (0.64, 0.66, and 0.56).

CONCLUSION

Accurate definition of pretherapeutic PET volumes is justified since SUVmean-derived values provide the best tumor-absorbed dose predictions in refractory meningioma patients treated by 177Lu-DOTATATE. This study provides a semi-automated segmentation method of pretherapeutic 68Ga-DOTATOC PET volumes to achieve good reproducibility between physicians.

CLINICAL RELEVANCE STATEMENT

SUVmean-derived values from pretherapeutic 68Ga-DOTATOC PET are predictive of tumor-absorbed doses in refractory meningiomas treated by 177Lu-DOTATATE, justifying to accurately define pretherapeutic PET volumes. This study provides a semi-automated segmentation of 68Ga-DOTATOC PET images easily applicable in routine.

KEY POINTS

• SUVmean-derived values from pretherapeutic 68Ga-DOTATOC PET images provide the best predictive factors of tumor-absorbed doses related to 177Lu-DOTATATE PRRT in refractory meningioma. • A 1.7-fold meninges SUVpeak segmentation method used to determine metabolic tumor volume on pretherapeutic 68Ga-DOTATOC PET images of refractory meningioma treated by 177Lu-DOTATATE is as efficient as the currently routine manual segmentation method and limits inter- and intra-observer variabilities. • This semi-automated method for segmentation of refractory meningioma is easily applicable to routine practice and transferrable across PET centers.

A robust semi-automatic delineation workflow using denoised diffusion weighted magnetic resonance imaging for response assessment of patients with esophageal cancer treated with neoadjuvant chemoradiotherapy

Background and Purpose

Diffusion weighted magnetic resonance imaging (DW-MRI) can be prognostic for response to neoadjuvant chemotherapy (nCRT) in patients with esophageal cancer. However, manual tumor delineation is labor intensive and subjective. Furthermore, noise in DW-MRI images will propagate into the corresponding apparent diffusion coefficient (ADC) signal. In this study a workflow is investigated that combines a denoising algorithm with semi-automatic segmentation for quantifying ADC changes.

Materials and Methods

Twenty patients with esophageal cancer who underwent nCRT before esophagectomy were included. One baseline and five weekly DW-MRI scans were acquired for every patient during nCRT. A self-supervised learning denoising algorithm, Patch2Self, was used to denoise the DWI-MRI images. A semi-automatic delineation workflow (SADW) was next developed and compared with a manually adjusted workflow (MAW). The agreement between workflows was determined using the Dice coefficients and Brand Altman plots. The prognostic value of ADCmean increases (%/week) for pathologic complete response (pCR) was assessed using c-statistics.

Results

The median Dice coefficient between the SADW and MAW was 0.64 (interquartile range 0.20). For the MAW, the c-statistic for predicting pCR was 0.80 (95% confidence interval (CI):0.56-1.00). The SADW showed a c-statistic of 0.84 (95%CI:0.63-1.00) after denoising. No statistically significant differences in c-statistics were observed between the workflows or after applying denoising.

Conclusions

The SADW resulted in non-inferior prognostic value for pCR compared to the more laborious MAW, allowing broad scale applications. The effect of denoising on the prognostic value for pCR needs to be investigated in larger cohorts.

The Role of Multimodal Imaging in Pathological Response Prediction of Locally Advanced Cervical Cancer Patients Treated by Chemoradiation Therapy Followed by Radical Surgery

PURPOSE

This study aimed to develop predictive models for pathological residual disease after neoadjuvant chemoradiation (CRT) in locally advanced cervical cancer (LACC) by integrating parameters derived from transvaginal ultrasound, MRI and PET/CT imaging at different time points and time intervals.

METHODS

Patients with histologically proven LACC, stage IB2-IVA, were prospectively enrolled. For each patient, the three examinations were performed before, 2 and 5 weeks after treatment ("baseline", "early" and "final", respectively). Multivariable logistic regression models to predict complete vs. partial pathological response (pR) were developed and a cost analysis was performed.

RESULTS

Between October 2010 and June 2014, 88 patients were included. Complete or partial pR was found in 45.5% and 54.5% of patients, respectively. The two most clinically useful models in pR prediction were (1) using percentage variation of SUV_max retrieved at PET/CT "baseline" and "final" examination, and (2) including high DWI signal intensity (SI) plus, ADC, and SUV_max collected at "final" evaluation (area under the curve (95% Confidence Interval): 0.80 (0.71-0.90) and 0.81 (0.72-0.90), respectively).

CONCLUSION

The percentage variation in SUV_max in the time interval before and after completing neoadjuvant CRT, as well as DWI SI plus ADC and SUV_max obtained after completing neoadjuvant CRT, could be used to predict residual cervical cancer in LACC patients. From a cost point of view, the use of MRI and PET/CT is preferable.

Gradient-based Volumetric PET Parameters on Immediate Pre-ablation FDG-PET Predict Local Tumor Progression in Patients with Colorectal Liver Metastasis Treated by Microwave Ablation

PURPOSE

This study aimed to evaluate the optimal method of segmentation of colorectal liver metastasis (CLM) on immediate pre-ablation PET scans and assess the prognostic value of quantitative pre-ablation PET parameters with regards to local tumor control. A secondary objective was to correlate the target tumor size estimation by PET methods with the tumor measurements on anatomical imaging.

METHODOLOGY

A prospectively accrued cohort of 55 CLMs (46 patients) treated with real-time ¹⁸F-FDG-PET/CT-guided percutaneous microwave ablation was followed-up for a median of 10.8 months (interquartile: 5.5-20.2). Total lesion glycolysis (TLG) and metabolic tumor volume (MTV) values of each CLM were derived from pre-ablation ¹⁸F-FDG-PET with gradient and threshold PET segmentation methodologies. The event was defined as local tumor progression (LTP). Time-dependent receiver operating characteristic (ROC) curve analyses were used to assess area under the curves (AUCs). Intraclass correlation (ICC) and 95.0% confidence interval (CI) were performed to measure the linear relationships between the continuous variables.

RESULTS

AUCs for prediction of LTP obtained from time-dependent ROC analysis for the gradient technique were higher in comparison to the threshold methodologies (AUCs for TLG and volume were: 0.790 and 0.807, respectively). ICC between PET gradient-based and anatomical measurements were higher in comparison to threshold methodologies (ICC for the longest diameter: 733 (95.0% CI 0.538-0.846), ICC for the shortest diameter: .747 (95.0% CI 0.546-0.859), p-values < 0.001).

CONCLUSIONS

The gradient-based technique had a higher AUC for prediction of LTP after microwave ablation of CLM and showed the highest correlation with anatomical imaging tumor measurements.

Baseline 18F-FDG PET/CT Radiomics in Classical Hodgkin's Lymphoma: The Predictive Role of the Largest and the Hottest Lesions

This study investigated the predictive role of baseline 18F-FDG PET/CT (bPET/CT) radiomics from two distinct target lesions in patients with classical Hodgkin's lymphoma (cHL). cHL patients examined with bPET/CT and interim PET/CT between 2010 and 2019 were retrospectively included. Two bPET/CT target lesions were selected for radiomic feature extraction: Lesion_A, with the largest axial diameter, and Lesion_B, with the highest SUVmax. Deauville score at interim PET/CT (DS) and 24-month progression-free-survival (PFS) were recorded. Mann-Whitney test identified the most promising image features (p < 0.05) from both lesions with regards to DS and PFS; all possible radiomic bivariate models were then built through a logistic regression analysis and trained/tested with a cross-fold validation test. The best bivariate models were selected based on their mean area under curve (mAUC). A total of 227 cHL patients were included. The best models for DS prediction had 0.78 ± 0.05 maximum mAUC, with a predominant contribution of Lesion_A features to the combinations. The best models for 24-month PFS prediction reached 0.74 ± 0.12 mAUC and mainly depended on Lesion_B features. bFDG-PET/CT radiomic features from the largest and hottest lesions in patients with cHL may provide relevant information in terms of early response-to-treatment and prognosis, thus representing an earlier and stronger decision-making support for therapeutic strategies. External validations of the proposed model are planned.

Radiomics Analysis of 18F-FDG PET/CT for Prognosis Prediction in Patients with Stage III Non-Small Cell Lung Cancer Undergoing Neoadjuvant Chemoradiation Therapy Followed by Surgery

We investigated the prognostic significance of radiomic features from 18F-FDG PET/CT to predict overall survival (OS) in patients with stage III NSCLC undergoing neoadjuvant chemoradiation therapy followed by surgery. We enrolled 300 patients with stage III NSCLC who underwent PET/CT at the initial work-up (PET1) and after neoadjuvant concurrent chemoradiotherapy (PET2). Radiomic primary tumor features were subjected to LASSO regression to select the most useful prognostic features of OS. The prognostic significance of the LASSO score and conventional PET parameters was assessed by Cox proportional hazards regression analysis. In conventional PET parameters, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of each PET1 and PET2 were significantly associated with OS. In addition, both the PET1-LASSO score and the PET2-LASSO score were significantly associated with OS. In multivariate Cox regression analysis, only the PET2-LASSO score was an independently significant factor for OS. The LASSO score showed better predictive performance for OS regarding the time-dependent receiver operating characteristic curve and decision curve analysis than conventional PET parameters. Radiomic features from PET/CT were an independent prognostic factor for the estimation of OS in stage III NSCLC. The newly developed LASSO score using radiomic features showed better prognostic results for individualized OS estimation than conventional PET parameters.

Artificial intelligence-based computer-aided diagnosis system supports diagnosis of lymph node metastasis in esophageal squamous cell carcinoma: A multicenter study

Background

This study aimed to develop an artificial intelligence-based computer-aided diagnosis system (AI-CAD) emulating the diagnostic logic of radiologists for lymph node metastasis (LNM) in esophageal squamous cell carcinoma (ESCC) patients, which contributed to clinical treatment decision-making.

Methods

A total of 689 ESCC patients with PET/CT images were enrolled from three hospitals and divided into a training cohort and two external validation cohorts. 452 CT images from three publicly available datasets were also included for pretraining the model. Anatomic information from CT images was first obtained automatically using a U-Net-based multi-organ segmentation model, and metabolic information from PET images was subsequently extracted using a gradient-based approach. AI-CAD was developed in the training cohort and externally validated in two validation cohorts.

Results

The AI-CAD achieved an accuracy of 0.744 for predicting pathological LNM in the external cohort and a good agreement with a human expert in two external validation cohorts (kappa = 0.674 and 0.587, p < 0.001). With the aid of AI-CAD, the human expert's diagnostic performance for LNM was significantly improved (accuracy [95% confidence interval]: 0.712 [0.669-0.758] vs. 0.833 [0.797-0.865], specificity [95% confidence interval]: 0.697 [0.636-0.753] vs. 0.891 [0.851-0.928]; p < 0.001) among patients underwent lymphadenectomy in the external validation cohorts.

Conclusions

The AI-CAD could aid in preoperative diagnosis of LNM in ESCC patients and thereby support clinical treatment decision-making.

Application of 18 F-fluorodeoxyglucose PET/CT radiomic features and machine learning to predict early recurrence of non-small cell lung cancer after curative-intent therapy

OBJECTIVE

To predict the recurrence of non-small cell lung cancer (NSCLC) within 2 years after curative-intent treatment using a machine-learning approach with PET/CT-based radiomics.

PATIENTS AND METHODS

A total of 77 NSCLC patients who underwent pretreatment 18 F-fluorodeoxyglucose PET/CT were retrospectively analyzed. Five clinical features (age, sex, tumor stage, tumor histology, and smoking status) and 48 radiomic features extracted from primary tumors on PET were used for binary classifications. These were ranked, and a subset of useful features was selected based on Gini coefficient scores in terms of associations with relapsed status. Areas under the receiver operating characteristics curves (AUC) were yielded by six machine-learning algorithms (support vector machine, random forest, neural network, naive Bayes, logistic regression, and gradient boosting). Model performances were compared and validated via random sampling.

RESULTS

A PET/CT-based radiomic model was developed and validated for predicting the recurrence of NSCLC during the first 2 years after curation. The most important features were SD and variance of standardized uptake value, followed by low-intensity short-zone emphasis and high-intensity zone emphasis. The naive Bayes model with the 15 best-ranked features displayed the best performance (AUC: 0.816). Prediction models using the five best PET-derived features outperformed those using five clinical variables.

CONCLUSION

The machine learning model using PET-derived radiomic features showed good performance for predicting the recurrence of NSCLC during the first 2 years after a curative intent therapy. PET/CT-based radiomic features may help clinicians improve the risk stratification of relapsed NSCLC.

Quantitative [99mTc]Tc-MDP SPECT/CT correlated with [18F]NaF PET/CT for bone metastases in patients with prostate cancer

BACKGROUND

The purpose of the present study was to elucidate the correlation between standardized uptake value (SUV) and volume-based parameters measured by quantitative [^99mTc]Tc-methylene diphosphonate (MDP) single photon emission computed tomography (SPECT)/CT and [¹⁸F]-sodium fluoride ([¹⁸F]NaF) positron emission tomography (PET)/CT in the assessment of bone metastases in patients with prostate cancer.

METHODS

The study included 26 male prostate cancer patients with confirmed or suspected bone metastases who underwent both [^99mTc]Tc-MDP SPECT/CT and [¹⁸F]NaF PET/CT studies. Skeletal lesions visible on both SPECT/CT and PET/CT were classified as benign or metastases. The maximum SUV (SUVmax), peak SUV (SUVpeak), mean SUV (SUVmean), metabolic bone volume (MBV), and total bone uptake (TBU) were calculated for every lesion showing abnormal uptake.

RESULTS

A total of 202 skeletal lesions (147 benign and 55 metastases) were detected in the 26 patients. Strong significant correlations were noted between SPECT/CT and PET/CT for the SUV- and volume-based parameters (all P < 0.001). The SUVmax, SUVpeak, SUVmean, and TBU values obtained with SPECT/CT were significantly lower than the corresponding values obtained with PET/CT (all P < 0.001). The MBV in SPECT/CT was significantly higher than that in PET/CT (P < 0.001). All SUV- and volume-based parameters obtained with both SPECT/CT and PET/CT for metastatic lesions were significantly higher than the corresponding parameters for benign lesions (P values from 0.036 to < 0.001).

CONCLUSIONS

These preliminary results demonstrate that the SUV- and volume-based parameters for bone uptake obtained with quantitative SPECT/CT and PET/CT are strongly correlated in patients with prostate cancer. The SUV parameters obtained with SPECT/CT were significantly lower than those obtained with PET/CT, whereas the uptake volume obtained with SPECT/CT was significantly higher than that obtained with PET/CT.

Development of scalable lymphatic system in the 4D XCAT phantom: Application to quantitative evaluation of lymphoma PET segmentations

BACKGROUND

Digital anthropomorphic phantoms, such as the 4D extended cardiac-torso (XCAT) phantom, are actively used to develop, optimize, and evaluate a variety of imaging applications, allowing for realistic patient modeling and knowledge of ground truth. The XCAT phantom defines the activity and attenuation for a simulated patient, which includes a complete set of organs, muscle, bone, and soft tissue, while also accounting for cardiac and respiratory motion. However, the XCAT phantom does not currently include the lymphatic system, critical for evaluating medical imaging tasks such as sentinel node detection, node density measurement, and radiation dosimetry.

PURPOSE

In this study, we aimed to develop a scalable lymphatic system in the XCAT phantom, to facilitate improved research of the lymphatic system in medical imaging. Using this scalable lymphatic system, we modeled the lymph node conglomerate pathology that is characteristically observed in primary mediastinal B-cell lymphoma (PMBCL). As an extended application, we evaluated positron emission tomography (PET) image quantification of metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of these simulated lymphomas, though the phantoms may be applied to other imaging modalities and study design paradigms (e.g., image quality, detection).

METHODS

A template model for the lymphatic system was developed based on anatomical data from the Visible Human Project of the National Library of Medicine. The segmented nodes and vessels were fit with non-uniform rational basis spline surfaces, and multichannel large deformation diffeomorphic metric mapping was used to propagate the template to different XCAT anatomies. To model conglomerates observed in PMBCL, lymph nodes were enlarged, converged within the mediastinum, and tracer concentration was increased. We used the phantoms as inputs to a PET simulation tool, which generated images using ordered subsets expectation maximization reconstruction with 2-8 mm Gaussian filters. Fixed thresholding (FT) and gradient segmentation were used to determine MTV and TLG. Percent bias (%Bias) and coefficient of variation (COV) were computed as measures of accuracy and precision, respectively, for each MTV and TLG measurement.

RESULTS

Using the methodology described above, we introduced a scalable lymphatic system in the XCAT phantom, which allows for the radioactivity and attenuation ground truth to be generated in 116 ± 2.5 s using a 2.3 GHz processor. Within the Rhinoceros interface, lymph node anatomy and function were modified to create a cohort of 10 phantoms with lymph node conglomerates. Using the lymphoma phantoms to evaluate PET quantification of MTV, mean %Bias values were -9.3%, -41.3%, and 20.9%, while COV values were 4.08%, 7.6%, and 3.4% using 25% FT, 40% FT, and gradient segmentations, respectively. Comparatively for TLG, mean %Bias values were -27.4%, -45.8%, and -16.0%, while COV values were 1.9%, 5.7%, and 1.4%, for the 25% FT, 40% FT, and gradient segmentations, respectively.

CONCLUSIONS

In this work, we upgraded the XCAT phantom to include a lymphatic system, comprised of a network of 276 scalable lymph nodes and corresponding vessels. As an application, we created a cohort of phantoms with lymph node conglomerates to evaluate lymphoma quantification in PET imaging, which highlights an important application of this work.

Standardised lesion segmentation for imaging biomarker quantitation: a consensus recommendation from ESR and EORTC

BACKGROUND

Lesion/tissue segmentation on digital medical images enables biomarker extraction, image-guided therapy delivery, treatment response measurement, and training/validation for developing artificial intelligence algorithms and workflows. To ensure data reproducibility, criteria for standardised segmentation are critical but currently unavailable.

METHODS

A modified Delphi process initiated by the European Imaging Biomarker Alliance (EIBALL) of the European Society of Radiology (ESR) and the European Organisation for Research and Treatment of Cancer (EORTC) Imaging Group was undertaken. Three multidisciplinary task forces addressed modality and image acquisition, segmentation methodology itself, and standards and logistics. Devised survey questions were fed via a facilitator to expert participants. The 58 respondents to Round 1 were invited to participate in Rounds 2-4. Subsequent rounds were informed by responses of previous rounds.

RESULTS/CONCLUSIONS

Items with ≥ 75% consensus are considered a recommendation. These include system performance certification, thresholds for image signal-to-noise, contrast-to-noise and tumour-to-background ratios, spatial resolution, and artefact levels. Direct, iterative, and machine or deep learning reconstruction methods, use of a mixture of CE marked and verified research tools were agreed and use of specified reference standards and validation processes considered essential. Operator training and refreshment were considered mandatory for clinical trials and clinical research. Items with a 60-74% agreement require reporting (site-specific accreditation for clinical research, minimal pixel number within lesion segmented, use of post-reconstruction algorithms, operator training refreshment for clinical practice). Items with ≤ 60% agreement are outside current recommendations for segmentation (frequency of system performance tests, use of only CE-marked tools, board certification of operators, frequency of operator refresher training). Recommendations by anatomical area are also specified.

Reliability of gradient-based segmentation for measuring metabolic parameters influenced by uptake time on 18F-PSMA-1007 PET/CT for prostate cancer

Purpose

To determine an optimal setting for functional contouring and quantification of prostate cancer lesions with minimal variation by evaluating metabolic parameters on 18F-PSMA-1007 PET/CT measured by threshold-based and gradient-based methods under the influence of varying uptake time.

Methods and materials

Dual time point PET/CT was chosen to mimic varying uptake time in clinical setting. Positive lesions of patients who presented with newly diagnosed disease or biochemical recurrence after total prostatectomy were reviewed retrospectively. Gradient-based and threshold-based tools at 40%, 50% and 60% of lesion SUVmax (MIM 6.9) were used to create contours on PET. Contouring was considered completed if the target lesion, with its hottest voxel, was delineated from background tissues and nearby lesions under criteria specific to their operations. The changes in functional tumour volume (FTV) and metabolic tumour burden (MTB, defined as the product of SUVmean and FTV) were analysed. Lesion uptake patterns (increase/decrease/stable) were determined by the percentage change in tumour SUVmax at ±10% limit.

Results

A total of 275 lesions (135 intra-prostatic lesions, 65 lymph nodes, 45 bone lesions and 30 soft tissue lesions in pelvic region) in 68 patients were included. Mean uptake time of early and delayed imaging were 94 and 144 minutes respectively. Threshold-based method using 40% to 60% delineated only 85 (31%), 110 (40%) and 137 (50%) of lesions which all were contoured by gradient-based method. Although the overall percentage change using threshold at 50% was the smallest among other threshold levels in FTV measurement, it was still larger than gradient-based method (median: 50%=-7.6% vs gradient=0%). The overall percentage increase in MTB of gradient-based method (median: 6.3%) was compatible with the increase in tumour SUVmax. Only a small proportion of intra-prostatic lesions (&lt;2%), LN (&lt;4%), bone lesions (0%) and soft tissue lesions (&lt;4%) demonstrated decrease uptake patterns.

Conclusions

With a high completion rate, gradient-based method is reliable for prostate cancer lesion contouring on 18F-PSMA-1007 PET/CT. Under the influence of varying uptake time, it has smaller variation than threshold-based method for measuring volumetric parameters. Therefore, gradient-based method is recommended for tumour delineation and quantification on 18F-PSMA-1007 PET/CT.

The role of 18F-FDG PET/CT in patients with diffuse large B-cell lymphoma after radioimmunotherapy using 131I-rituximab as consolidation therapy

Purpose

To evaluate the prognostic value of pretreatment ¹⁸F-FDG PET/CT after consolidation therapy of ¹³¹I-rituximab in patients with diffuse large B-cell lymphoma (DLBCL) who had acquired complete remission after receiving chemotherapy.

Methods

Patients who were diagnosed with DLBCL via histologic confirmation were retrospectively reviewed. All patients had achieved complete remission after 6 to 8 cycles of R-CHOP (rituximab, cyclophosphamide, vincristine, doxorubicin, and prednisolone) chemotherapy after which they underwent consolidation treatment with ¹³¹I-rituximab. ¹⁸F-FDG PET/CT scans were performed before R-CHOP for initial staging. The largest diameter of tumor, maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were obtained from pretreatment ¹⁸F-FDG PET/CT scans. Receiver-operating characteristic curves analysis was introduced for assessing the optimal criteria. Kaplan-Meier curve survival analysis was performed to evaluate both relapse free survival (RFS) and overall survival (OS).

Results

A total of 15 patients (12 males and 3 females) with a mean age of 56 (range, 30–73) years were enrolled. The median follow-up period of these patients was 73 months (range, 11–108 months). Four (27%) patients relapsed. Of them, three died during follow-up. Median values of the largest tumor size, highest SUVmax, MTV, and TLG were 5.3 cm (range, 2.0–16.4 cm), 20.2 (range, 11.1–67.4), 231.51 (range, 15–38.34), and 1277.95 (range, 238.37–10341.04), respectively. Patients with SUVmax less than or equal to 16.9 showed significantly worse RFS than patients with SUVmax greater than 16.9 (5-year RFS rate: 60% vs. 100%, p = 0.008). Patients with SUVmax less than or equal to 16.9 showed significantly worse OS than patients with SUVmax greater than 16.9 (5-year OS rate: 80% vs. 100% p = 0.042).

Conclusion

Higher SUVmax at pretreatment ¹⁸F-FDG PET/CT was associated with better relapse free survival and overall survival in DLBCL patients after consolidation therapy with ¹³¹I-rituximab. However, because this study has a small number of patients, a phase 3 study with a larger number of patients is needed for clinical application in the future.

Prognostic Value of Axillary Lymph Node Texture Parameters Measured by Pretreatment 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Locally Advanced Breast Cancer with Neoadjuvant Chemotherapy

Background: This study investigated the prognostic value of axillary lymph node (ALN) heterogeneity texture features through ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) in patients with locally advanced breast cancer (LABC). Methods: We retrospectively analyzed 158 LABC patients with FDG-avid, pathology-proven, metastatic ALN who underwent neoadjuvant chemotherapy (NAC) and curative surgery. Tumor and ALN texture parameters were extracted from pretreatment ¹⁸F-FDG PET/CT using Chang-Gung Image Texture Analysis software. The least absolute shrinkage and selection operator regression was performed to select the most significant predictive texture parameters. The predictive impact of texture parameters was evaluated for both progression-free survival and pathologic NAC response. Results: The median follow-up period of 36.8 months and progression of disease (PD) was observed in 36 patients. In the univariate analysis, ALN textures (minimum standardized uptake value (SUV) (p = 0.026), SUV skewness (p = 0.038), SUV bias-corrected Kurtosis (p = 0.034), total lesion glycolysis (p = 0.011)), tumor textures (low-intensity size zone emphasis (p = 0.045), minimum SUV (p = 0.047), and homogeneity (p = 0.041)) were significant texture predictors. On the Cox regression analysis, ALN SUV skewness was an independent texture predictor of PD (p = 0.016, hazard ratio 2.3, 95% confidence interval 1.16–4.58). Conclusions: ALN texture feature from pretreatment ¹⁸F-FDG PET/CT is useful for the prediction of LABC progression.

A pilot study of 68 Ga-PSMA-617 PET/CT imaging and 177Lu-EB-PSMA-617 radioligand therapy in patients with adenoid cystic carcinoma

Background

This pilot study was designed to evaluate the diagnostic value of ⁶⁸ Ga-PSMA-617 and ¹⁸F-FDG PET/CT in adenoid cystic carcinoma (ACC) and to assess the safety and therapeutic response to PSMA radioligand therapy (RLT) in ACC patients.

Methods

Thirty patients pathologically diagnosed with ACC were recruited into the cohort. Each patient underwent ⁶⁸ Ga-PSMA-617 and ¹⁸F-FDG PET/CT within 1 week. The number and SUVmax of PET-positive lesions were recorded and compared. Four patients accepted RLT using ¹⁷⁷Lu-EB-PSMA-617, in a dosage of approximately 1.85 GBq (50 mCi) per cycle for up to 3 cycles.

Results

Compared with ¹⁸F-FDG, ⁶⁸ Ga-PSMA-617 revealed more PET-positive extrapulmonary tumors (157 vs. 141, P = 0.016) and higher SUVmax (8.8 ± 3.6 vs. 6.4 ± 4.2, P = 0.027). However, ⁶⁸ Ga-PSMA-617 revealed less PET-positive pulmonary lesions (202 vs. 301, P < 0.001) and lower SUVmax of tumors (3.1 ± 3.0 vs. 4.2 ± 3.9, P < 0.001) than ¹⁸F-FDG. The combination of ⁶⁸ Ga-PSMA-617 and ¹⁸F-FDG can detect 469 PET-positive lesions, which was superior to each alone (469 vs. 359 vs. 442, P < 0.001). Two patients achieved remarkable response after PSMA RLT, while the other two patients showed reduced tumor uptake of recurrent foci, lung and liver metastases, whereas increased SUVmax of bone metastases.

Conclusions

⁶⁸ Ga-PSMA-617 PET/CT is a valuable imaging modality for the detection of ACC and combining with ¹⁸F-FDG PET/CT will achieve a higher detection efficiency. PSMA RLT may be a promising treatment for ACC and is worth of further investigation.

Trial registration: Diagnosis of Adenoid Cystic Carcinoma on ⁶⁸ Ga-PSMA-617 PET-CT and Therapy With ¹⁷⁷Lu-EB-PSMA-617 (NCT04801264, Registered 16 March 2021, retrospectively registered).

URL of registry: https://clinicaltrials.gov/ct2/show/NCT04801264.

Predicting time to treatment in follicular lymphoma on watchful waiting using baseline metabolic tumour burden

PURPOSE

Asymptomatic patients with follicular lymphoma (FL) and a low tumour burden can be followed without initial therapy, a strategy called watchful waiting (WW). Prediction of the time to treatment (TTT) is still a challenge. We investigated the prognostic value of baseline total metabolic tumour volume (TMTV) and whole-body total lesion glycolysis (WB-TLG) to predict TTT in patients with FL on WW.

METHODS

We conducted a retrospective study of 54 patients with FL (grade 1-3a) diagnosed between June 2013 and December 2019, staged with FDG PET/CT, and managed on WW. Median age was 62 years (range 34-85), stage was advanced (III-IV) in 57%, and FLIPI score was intermediate to high (≥ 2) in 52% of the patients.

RESULTS

The median TMTV and WB-TLG were 7.1 and 43.3, respectively. With a median follow-up of 59 months, 41% of patients started immuno-chemotherapy. The optimal cut-points to identify patients with TTT within 24 months were 14 for TMTV (AUC 0.70; 95% CI 51-88) and 64 for WB-TLG (AUC 0.71; 95% CI 52-89) (p < 0.005). The probability of not having started treatment within 24 months was 87% for TMTV < 14 and 53% for TMTV ≥ 14 (p < 0.005). TMTV was independent of the FLIPI score for TTT prediction. Patients with both FLIPI ≥ 2 and TMTV ≥ 14 had only an 18% probability of not having started treatment at 36 months, while this probability was 75% in patients with TMTV < 14.

CONCLUSION

Metabolic tumour volume parameters may add information to clinical scores to better predict TTT and better stratify patients for interventional studies.

The value of the Standardized Uptake Value (SUV) and Metabolic Tumor Volume (MTV) in lung cancer

The diagnosis, staging and therapeutic monitoring of lung cancer were amongst the first applications for which the utility of FDG PET was documented and FDG PET/CT is now a routine diagnostic tool for clinical decision-making. As well as having high sensitivity for detection of disease sites, which provides critical information about stage, the intensity of uptake provides deeper biological characterization, while the burden of disease also has potential clinical significance. These disease characteristics can easily be quantified on delayed whole-body imaging as the maximum standardized uptake value (SUV_max) and metabolic tumor volume (MTV), respectively. There have been significant efforts to harmonize the measurement of these features, particularly within the context of clinical trials. Nevertheless, however calculated, in general, a high SUV_max and large MTV have been shown to have an adverse prognostic significance. Nevertheless, the use of these parameters in the interpretation and reporting of clinical scans remains inconsistent and somewhat controversial. This review details the current status of semi-quantitative FDG PET/CT in the evaluation of lung cancer.

PSMA as a Theranostic Target in Hepatocellular Carcinoma: Immunohistochemistry and 68 Ga-PSMA-11 PET Using Cyclotron-Produced 68 Ga

Prostate-specific membrane antigen (PSMA) is a validated target for molecular diagnostics and targeted radionuclide therapy. Our purpose was to evaluate PSMA expression in hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA), and hepatic adenoma (HCA); investigate the genetic pathways in HCC associated with PSMA expression; and evaluate HCC detection rate with 68 Ga-PSMA-11 positron emission tomography (PET). In phase 1, PSMA immunohistochemistry (IHC) on HCC (n = 148), CCA (n = 111), and HCA (n = 78) was scored. In a subset (n = 30), messenger RNA (mRNA) data from the Cancer Genome Atlas HCC RNA sequencing were correlated with PSMA expression. In phase 2, 68 Ga-PSMA-11 PET was prospectively performed in patients with treatment-naïve HCC on a digital PET scanner using cyclotron-produced 68 Ga. Uptake was graded qualitatively and semi-quantitatively using standard metrics. On IHC, PSMA expression was significantly higher in HCC compared with CCA and HCA (P < 0.0001); 91% of HCCs (n = 134) expressed PSMA, which principally localized to tumor-associated neovasculature. Higher tumor grade was associated with PSMA expression (P = 0.012) but there was no association with tumor size (P = 0.14), fibrosis (P = 0.35), cirrhosis (P = 0.74), hepatitis B virus (P = 0.31), or hepatitis C virus (P = 0.15). Overall survival tended to be longer in patients without versus with PSMA expression (median overall survival: 4.2 vs. 1.9 years; P = 0.273). FGF14 (fibroblast growth factor 14) mRNA expression correlated positively (rho = 0.70; P = 1.70 × 10-5 ) and MAD1L1 (Mitotic spindle assembly checkpoint protein MAD1) correlated negatively with PSMA expression (rho = -0.753; P = 1.58 × 10-6 ). Of the 190 patients who met the eligibility criteria, 31 patients with 39 HCC lesions completed PET; 64% (n = 25) lesions had pronounced 68 Ga-PSMA-11 standardized uptake value: SUVmax (median [range] 9.2 [4.9-28.4]), SUVmean 4.7 (2.4-12.7), and tumor-to-liver background ratio 2 (1.1-11). Conclusion: Ex vivo expression of PSMA in neovasculature of HCC translates to marked tumor avidity on 68 Ga-PSMA-11 PET, which suggests that PSMA has the potential as a theranostic target in patients with HCC.

Prognostic Value of Early Fluorodeoxyglucose-Positron Emission Tomography Response Imaging and Peripheral Immunologic Biomarkers: Substudy of a Phase II Trial of Risk-Adaptive Chemoradiation for Unresectable Non-Small Cell Lung Cancer

Purpose

We sought to examine the prognostic value of fluorodeoxyglucose-positron emission tomography (PET) imaging during chemoradiation for unresectable non-small cell lung cancer for survival and hypothesized that tumor PET response is correlated with peripheral T-cell function.

Methods and Materials

Forty-five patients with American Joint Committee on Cancer version 7 stage IIB-IIIB non-small cell lung cancer enrolled in a phase II trial and received platinum-doublet chemotherapy concurrent with 6 weeks of radiation (NCT02773238). Fluorodeoxyglucose-PET was performed before treatment start and after 24 Gy of radiation (week 3). PET response status was prospectively defined by multifactorial radiologic interpretation. PET responders received 60 Gy in 30 fractions, while nonresponders received concomitant boosts to 74 Gy in 30 fractions. Peripheral blood was drawn synchronously with PET imaging, from which germline DNA sequencing, T-cell receptor sequencing, and plasma cytokine analysis were performed.

Results

Median follow-up was 18.8 months, 1-year overall survival (OS) 82%, 1-year progression-free survival 53%, and 1-year locoregional control 88%. Higher midtreatment PET total lesion glycolysis was detrimental to OS (1 year 87% vs 63%, P < .001), progression-free survival (1 year 60% vs 26%, P = .044), and locoregional control (1 year 94% vs 65%, P = .012), even after adjustment for clinical/treatment factors. Twenty-nine of 45 patients (64%) were classified as PET responders based on a priori definition. Higher tumor programmed death-ligand 1 expression was correlated with response on PET (P = .017). Higher T-cell receptor richness and clone distribution slope were associated with improved OS (P = .018-0.035); clone distribution slope was correlated with PET response (P = .031).

Conclusions

Midchemoradiation PET imaging is prognostic for survival; PET response may be linked to tumor and peripheral T-cell biomarkers.

Joint EANM/SNMMI/ESTRO practice recommendations for the use of 2-[18F]FDG PET/CT external beam radiation treatment planning in lung cancer V1.0

PURPOSE

2-[18F]FDG PET/CT is of utmost importance for radiation treatment (RT) planning and response monitoring in lung cancer patients, in both non-small and small cell lung cancer (NSCLC and SCLC). This topic has been addressed in guidelines composed by experts within the field of radiation oncology. However, up to present, there is no procedural guideline on this subject, with involvement of the nuclear medicine societies.

METHODS

A literature review was performed, followed by a discussion between a multidisciplinary team of experts in the different fields involved in the RT planning of lung cancer, in order to guide clinical management. The project was led by experts of the two nuclear medicine societies (EANM and SNMMI) and radiation oncology (ESTRO).

RESULTS AND CONCLUSION

This guideline results from a joint and dynamic collaboration between the relevant disciplines for this topic. It provides a worldwide, state of the art, and multidisciplinary guide to 2-[18F]FDG PET/CT RT planning in NSCLC and SCLC. These practical recommendations describe applicable updates for existing clinical practices, highlight potential flaws, and provide solutions to overcome these as well. Finally, the recent developments considered for future application are also reviewed.

Development of High-Resolution Dedicated PET-Based Radiomics Machine Learning Model to Predict Axillary Lymph Node Status in Early-Stage Breast Cancer

Simple Summary

Accurate clinical axillary evaluation plays an important role in the diagnosis of and treatment planning for breast cancer (BC). This study aimed to develop a machine learning model integrating dedicated breast PET and clinical characteristics for prediction of axillary lymph node status in cT1-2N0-1M0 BC non-invasively. The performance of this integrating model in identifying pN0 and pN1 with the AUC was 0.94. We achieved an NPV of 96.88% in the cN0 and PPV of 92.73% in the cN1 subgroup. The higher true positive and true negative rate could delineate clinical subtypes and apply more precise treatment for patients with early-stage BC.

Abstract

Purpose of the Report: Accurate clinical axillary evaluation plays an important role in the diagnosis and treatment planning for early-stage breast cancer (BC). This study aimed to develop a scalable, non-invasive and robust machine learning model for predicting of the pathological node status using dedicated-PET integrating the clinical characteristics in early-stage BC. Materials and Methods: A total of 420 BC patients confirmed by postoperative pathology were retrospectively analyzed. 18F-fluorodeoxyglucose (¹⁸F-FDG) Mammi-PET, ultrasound, physical examination, Lymph-PET, and clinical characteristics were analyzed. The least absolute shrinkage and selection operator (LASSO) regression analysis were used in developing prediction models. The characteristic curve (ROC) of the area under receiver-operator (AUC) and DeLong test were used to evaluate and compare the performance of the models. The clinical utility of the models was determined via decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots. Results: A total of 290 patients were enrolled in this study. The AUC of the integrated model diagnosed performance was 0.94 (95% confidence interval (CI), 0.91–0.97) in the training set (n = 203) and 0.93 (95% CI, 0.88–0.99) in the validation set (n = 87) (both p < 0.05). In clinical N0 subgroup, the negative predictive value reached 96.88%, and in clinical N1 subgroup, the positive predictive value reached 92.73%. Conclusions: The use of a machine learning integrated model can greatly improve the true positive and true negative rate of identifying clinical axillary lymph node status in early-stage BC.

Quantitative evaluation of PSMA PET imaging using a realistic anthropomorphic phantom and shell-less radioactive epoxy lesions

BACKGROUND

Positron emission tomography (PET) with prostate specific membrane antigen (PSMA) have shown superior performance in detecting metastatic prostate cancers. Relative to [18F]fluorodeoxyglucose ([18F]FDG) PET images, PSMA PET images tend to visualize significantly higher-contrast focal lesions. We aim to evaluate segmentation and reconstruction algorithms in this emerging context. Specifically, Bayesian or maximum a posteriori (MAP) image reconstruction, compared to standard ordered subsets expectation maximization (OSEM) reconstruction, has received significant interest for its potential to reach convergence with minimal noise amplifications. However, few phantom studies have evaluated the quantitative accuracy of such reconstructions for high contrast, small lesions (sub-10 mm) that are typically observed in PSMA images. In this study, we cast 3 mm-16-mm spheres using epoxy resin infused with a long half-life positron emitter (sodium-22; 22Na) to simulate prostate cancer metastasis. The anthropomorphic Probe-IQ phantom, which features a liver, bladder, lungs, and ureters, was used to model relevant anatomy. Dynamic PET acquisitions were acquired and images were reconstructed with OSEM (varying subsets and iterations) and BSREM (varying β parameters), and the effects on lesion quantitation were evaluated.

RESULTS

The 22Na lesions were scanned against an aqueous solution containing fluorine-18 (18F) as the background. Regions-of-interest were drawn with MIM Software using 40% fixed threshold (40% FT) and a gradient segmentation algorithm (MIM's PET Edge+). Recovery coefficients (RCs) (max, mean, peak, and newly defined "apex"), metabolic tumour volume (MTV), and total tumour uptake (TTU) were calculated for each sphere. SUVpeak and SUVapex had the most consistent RCs for different lesion-to-background ratios and reconstruction parameters. The gradient-based segmentation algorithm was more accurate than 40% FT for determining MTV and TTU, particularly for lesions [Formula: see text] 6 mm in diameter (R2 = 0.979-0.996 vs. R2 = 0.115-0.527, respectively).

CONCLUSION

An anthropomorphic phantom was used to evaluate quantitation for PSMA PET imaging of metastatic prostate cancer lesions. BSREM with β = 200-400 and OSEM with 2-5 iterations resulted in the most accurate and robust measurements of SUVmean, MTV, and TTU for imaging conditions in 18F-PSMA PET/CT images. SUVapex, a hybrid metric of SUVmax and SUVpeak, was proposed for robust, accurate, and segmentation-free quantitation of lesions for PSMA PET.

Geometrical Comparison and Quantitative Evaluation of 18F-FDG PET/CT- and DW-MRI-Based Target Delineation Before and During Radiotherapy for Esophageal Squamous Carcinoma

Background and Purpose

This study aimed to evaluate the geometrical differences in and metabolic parameters of ¹⁸F-fluorodeoxyglucose positron emission tomography–computed tomography (¹⁸F-FDG PET-CT) and diffusion-weighted magnetic resonance imaging (DW-MRI) performed before and during radiotherapy (RT) for patients with esophageal cancer based on the three-dimensional CT (3DCT) medium and explore whether the high signal area derived from DW-MRI can be used as a tool for an individualized definition of the volume in need of dose escalation for esophageal squamous cancer.

Materials and Methods

Thirty-two patients with esophageal squamous cancer sequentially underwent repeated 3DCT, ¹⁸F-FDG PET-CT, and enhanced MRI before the initiation of RT and after the 15th fraction. All images were fused with 3DCT images through deformable registration. The gross tumor volume (GTV) was delineated based on PET Edge on the first and second PET-CT images and defined as GTV_PETpre and GTV_PETdur, respectively. GTV_DWIpre and GTV_DWIdur were delineated on the first and second DWI and corresponding T₂-weighted MRI (T₂W-MRI)-fused images. The maximum, mean, and peak standardized uptake values (SUVs; SUV_max, SUV_mean, and SUV_peak, respectively); metabolic tumor volume (MTV); and total lesion glycolysis(TLG) and its relative changes were calculated automatically on PET. Similarly, the minimum and mean apparent diffusion coefficient (ADC; ADC_min and ADC_mean) and its relative changes were measured manually using ADC maps.

Results

The volume of GTV_CT exhibited a significant positive correlation with that of GTV_PET and GTV_DWI (both p < 0.001). Significant differences were observed in both ADCs and ¹⁸F-FDG PET metabolic parameters before and during RT (both p < 0.001). No significant correlation was observed between SUVs and ADCs before and during RT (p = 0.072–0.944) and between ∆ADCs and ∆SUVs (p = 0.238–0.854). The conformity index and degree of inclusion of GTV_PETpre to GTV_DWIpre were significantly higher than those of GTV_PETdur to GTV_DWIdur (both p < 0.001). The maximum diameter shrinkage rate (∆LD_DWI) (24%) and the tumor volume shrinkage rate (VRR_DWI) (60%) based on DW-MRI during RT were significantly greater than the corresponding PET-based ∆LD_PET (14%) and VRR_PET (41%) rates (p = 0.017 and 0.000, respectively).

Conclusion

Based on the medium of CT images, there are significant differences in spatial position, biometabolic characteristics, and the tumor shrinkage rate for GTVs derived from ¹⁸F-FDG PET-CT and DW-MRI before and during RT for esophageal squamous cancer. Further studies are needed to determine if DW-MRI will be used as tool for an individualized definition of the volume in need of dose escalation.

Reliability of Quantitative 18F-FDG PET/CT Imaging Biomarkers for Classifying Early Response to Chemoradiotherapy in Patients With Locally Advanced Non-Small Cell Lung Cancer

PURPOSE OF THE REPORT

We evaluated the reliability of 18F-FDG PET imaging biomarkers to classify early response status across observers, scanners, and reconstruction algorithms in support of biologically adaptive radiation therapy for locally advanced non-small cell lung cancer.

PATIENTS AND METHODS

Thirty-one patients with unresectable locally advanced non-small cell lung cancer were prospectively enrolled on a phase 2 trial (NCT02773238) and underwent 18F-FDG PET on GE Discovery STE (DSTE) or GE Discovery MI (DMI) PET/CT systems at baseline and during the third week external beam radiation therapy regimens. All PET scans were reconstructed using OSEM; GE-DMI scans were also reconstructed with BSREM-TOF (block sequential regularized expectation maximization reconstruction algorithm incorporating time of flight). Primary tumors were contoured by 3 observers using semiautomatic gradient-based segmentation. SUVmax, SUVmean, SUVpeak, MTV (metabolic tumor volume), and total lesion glycolysis were correlated with midtherapy multidisciplinary clinical response assessment. Dice similarity of contours and response classification areas under the curve were evaluated across observers, scanners, and reconstruction algorithms. LASSO logistic regression models were trained on DSTE PET patient data and independently tested on DMI PET patient data.

RESULTS

Interobserver variability of PET contours was low for both OSEM and BSREM-TOF reconstructions; intraobserver variability between reconstructions was slightly higher. ΔSUVpeak was the most robust response predictor across observers and image reconstructions. LASSO models consistently selected ΔSUVpeak and ΔMTV as response predictors. Response classification models achieved high cross-validated performance on the DSTE cohort and more variable testing performance on the DMI cohort.

CONCLUSIONS

The variability FDG PET lesion contours and imaging biomarkers was relatively low across observers, scanners, and reconstructions. Objective midtreatment PET response assessment may lead to improved precision of biologically adaptive radiation therapy.

PET Imaging and Rate of Pathologic Complete Response in Esophageal Squamous Cell Carcinoma

BACKGROUND

For locally advanced esophageal squamous cell carcinoma (ESCC), chemoradiation (ChemoRT) followed by surgery offers the best chance of cure, with a 35-50% pathologic complete response (pCR) rate. Given the morbidity of esophagectomy and the possibility of pCR with ChemoRT, a 'watch and wait' strategy has been proposed, particularly for squamous cell carcinoma. The ability to accurately predict which patients will have pCR from ChemoRT is critical in treatment decision making. This study assessed positron emission tomography (PET) in predicting pCR after neoadjuvant ChemoRT for ESCC.

METHODS

ESCC patients treated with ChemoRT followed by surgery were identified. Maximum standard uptake value (SUV), metabolic tumor volume, total lesion glycolysis, and first-order textual features of standard deviation, kurtosis and skewness were measured from PET. Univariable and multivariable generalized linear method analyses were performed. A metabolic complete response (mCR) was defined as a post-therapy PET scan with maximum SUV < 4.0.

RESULTS

Twenty-seven patients underwent ChemoRT followed by surgery, with overall pCR seen in 11 (41%) patients and radiographic mCR seen in 12 (44%) patients. Final pathology for these 12 patients revealed pCR (ypT0N0M0) in 5 (42%) patients and persistent disease in 7 (58%) patients. Univariate analysis did not reveal PET parameters predictive of pCR.

CONCLUSION

Treatment of ESCC with ChemoRT often results in a robust clinical response. Among patients with an mCR after ChemoRT, disease persistence was found in 58%. The inability of PET to predict pCR is important in the context of a 'watch and wait' strategy for ESCC treated with ChemoRT.

Chordoma: 18F-FDG PET/CT and MRI imaging features

OBJECTIVE

Examine the ¹⁸F-FDG PET/CT and MRI imaging characteristics of chordoma.

MATERIALS AND METHODS

Biopsy-proven chordoma with a pre-therapy ¹⁸F-FDG PET/CT from 2001 through 2019 in patients > 18 years old were retrospectively reviewed. Multiple PET/CT and MRI imaging parameters were assessed.

RESULTS

A total of 23 chordoma patients were included (16 M, 7 F; average age of 60.1 ± 13.0 years) with comparative MRI available in 22 cases. This included 13 sacrococcygeal, 9 mobile spine, and one clival lesions. On ¹⁸F-FDG PET/CT, chordomas demonstrated an average SUVmax of 5.8 ± 3.7, average metabolic tumor volume (MTV) of 160.2 ± 263.8 cm³, and average total lesion glycolysis (TLG) of 542.6 ± 1210 g. All demonstrated heterogeneous FDG activity. On MRI, chordomas were predominantly T2 hyperintense (22/22) and T1 isointense (18/22), contained small foci of T1 hyperintensity (17/22), and demonstrated heterogeneous enhancement (14/20). There were no statistically significant associations found between ¹⁸F-FDG PET/CT and MRI imaging features. There was no relationship of SUVmax (p = 0.53), MTV (p = 0.47), TLG (p = 0.48), maximal dimension (p = 0.92), or volume (p = 0.45) to the development of recurrent or metastatic disease which occurred in 6/22 patients over a mean follow-up duration of 4.1 ± 2.0 years.

CONCLUSION

On ¹⁸F-FDG PET/CT imaging, chordomas demonstrate moderate, heterogeneous FDG uptake. Predominant T2 hyperintensity and small foci of internal increased T1 signal are common on MRI. The inherent FDG avidity of chordomas suggests that ¹⁸F-FDG PET/CT may be a useful modality for staging, evaluating treatment response, and assessing for recurrent or metastatic disease.

18F-Fluorothymidine PET is an early and superior predictor of progression-free survival following chemoimmunotherapy of diffuse large B cell lymphoma: a multicenter study

Purpose

To determine whether interim 3′-deoxy-3′-[¹⁸F]fluorothymidine (iFLT) PET/CT is a superior predictor of progression-free survival (PFS) compared with interim ¹⁸F-fluorodeoxyglucose (iFDG) PET/CT in patients with diffuse large B cell lymphoma (DLBCL) treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) or rituximab, etoposide, prednisone, vincristine, cyclophosphamide, and doxorubicin (R-EPOCH).

Methods

Ninety-two prospectively enrolled patients with DLBCL underwent both FLT-PET/CT and FDG-PET/CT 18–24 days after two cycles of R-CHOP/R-EPOCH. Deauville-criteria, PERCIST1.0, standardized uptake value (SUV), total lesion glycolysis (TLG), and metabolic tumor volume were used to interpret iFDG-PET/CT while dichotomous visual interpretation was used to interpret iFLT-PET/CT and the results were compared with the 3- and 5-year PFS.

Results

iFLT-PET/CT was negative in 67 (73%) and positive in 25 (27%) patients. iFDG-PET/CT by Deauville criteria was negative (Deauville scores [DS] of 1–3) in 53 (58%) and positive (DS = 4–5) in 39 (42%) patients. Of the 67 iFLT-PET/CT-negative patients, 7 (10.4%) progressed at a median of 14.1 months whereas 14/25 (56.0%) iFLT-PET/CT-positive patients progressed at a median of 7.8 months (P < .0001). Of the 53 Deauville-negative patients, 9 (17.0%) progressed at a median of 14.1 months whereas 12/39 (30.8%) Deauville-positive patients progressed at a median of 5.6 months (P = .11). In multivariate analysis, including iFLT-PET/CT, PERCIST, interim TLG, and interim SUV_max, only iFLT-PET/CT was an independent predictor for 3- and 5-year PFS (P < .0001 and P = .001, respectively).

Conclusions

In patients with DLBCL given R-CHOP/R-EPOCH, iFLT-PET/CT is a superior independent predictor of outcome compared with iFDG-PET/CT.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-021-05353-9.

FDG PET/CT and MRI Features of Pathologically Proven Schwannomas

PURPOSE

The aim of this study was to examine the MRI and FDG PET/CT imaging features of pathologically proven schwannomas.

PATIENTS AND METHODS

This institutional review board-approved retrospective study examined biopsy-proven schwannomas that underwent FDG PET/CT and/or MRI at our institution between January 1, 2002, and April 1, 2018. PET/CT features analyzed included SUVmax, metabolic ratios, volumetric metabolic measures, presence of calcification, and pattern of FDG activity. MRI features included T1/T2 signal, enhancement pattern, margins, perilesional edema, presence of muscular denervation, and size.

RESULTS

Ninety-five biopsy-proven schwannomas were identified (40 with both PET and MRI, 35 with PET only, and 20 with MRI only), 46 females and 49 males, average age of 57.7 ± 15.3 years. The average largest dimension was 4.6 ± 2.7 cm, the average SUVmax was 5.4 ± 2.7, and lesion SUVmax/liver SUVmean was 2.2 ± 1.2. Eleven (15%) of 75 lesions had SUVmax greater than 8.1, 26/75 (35%) had SUVmax greater than 6.1, and 14/75 (19%) had lesion SUVmax/liver SUVmean greater than 3.0. On MRI, 29/53 (55%) demonstrated internal nonenhancing areas. Twenty-eight (70%) of 40 lesions with both MRI and PET demonstrated at least 1 imaging feature concerning for malignant peripheral nerve sheath tumor (irregular margins, internal nonenhancement, perilesional edema, heterogeneous FDG uptake, or SUVmax >8.1). Lesions with heterogeneous FDG activity had higher SUVmax (6.5 ± 0.5 vs 4.7 ± 0.4, P = 0.0031) and more frequent internal nonenhancement on MRI (P = 0.0218).

CONCLUSIONS

Schwannomas may be large, be intensely FDG avid, and demonstrate significant heterogeneity, features typically associated with malignant peripheral nerve sheath tumors. A significant proportion exhibit FDG activity above cutoff levels previously thought useful in differentiating malignant from benign peripheral nerve sheath tumors.

Metabolic activity of extranodal NK/T cell lymphoma on 18F-FDG PET/CT according to immune subtyping

Disseminated extranodal NK/T cell lymphoma (ENKTL) is associated with dismal prognosis. Hence, distinct tumor immune microenvironment (TIME) subtypes were proposed to explain their influence on ENKTL progression and help predict treatment response. In this study, we investigated the capacity of FDG PET/CT to discern ENKTL TIME subtypes. A total of 108 pretreatment FDG PET/CT scans of 103 patients with newly diagnosed or relapsed ENKTL were retrospectively analyzed. TIME subtype was determined using three key immunohistochemical markers. SUVmax, MTV and TLG were measured, and metabolic features associated with TIME subtype were statistically extracted. TIME subtype was immune tolerance (IT) in 13.9%, immune evasion A (IE-A) in 56.5%, immune evasion B (IE-B) in 21.3%, and immune silenced (IS) in 8%. The IS group showed the highest SUVmax (15.9 ± 6.4, P = 0.037), followed by IE-A (14.1 ± 7.8), IE-B (10.9 ± 5.6), and IT groups (9.6 ± 5.1). Among 53 with only nasal FDG lesions, 52 had non-IS subtype. Among 55 with extra-nasal FDG lesions, those with IS subtype more often had adrenal (P = 0.001) or testis involvement (P = 0.043), greater MTV (P = 0.005), greater TLG (P = 0.005), and SUVmax located at extra-nasal sites. The presence of 0–2 and 3–4 of these four findings was associated with low probability (2/46) and high probability (6/9) of IS subtype, respectively. Furthermore, patients showing IS subtype-favoring PET/CT pattern had worse overall survival compared to their counterparts. These results demonstrate that FDG PET/CT can help predict immune subtype in ENKTL patients. The different patterns between glycolytic activity and involved site according to TIME subtype might be related to the interplay between tumor cells and immune cells in the tumor microenvironment.

Optimal Standardized Uptake Value Threshold for Auto contouring of Gross Tumor Volume using Positron Emission Tomography/Computed Tomography in Patients with Operable Nonsmall-Cell Lung Cancer: Comparison with Pathological Tumor Size

Purpose:

Incorporating ¹⁸F-fluorodeoxyglucose positron emission tomography-computed tomography (¹⁸F-FDG-PET/CT) for gross tumor volume (GTV) delineation is challenging due to varying tumor edge based on the set threshold of the standardized uptake value (SUV). This study aims to determine an optimal SUV threshold that correlates best with the pathological tumor size.

Materials and Methods:

From January 2013 to July 2014, 25 consecutive patients of operable nonsmall-cell lung cancer (NSCLC) who underwent staging¹⁸F-FDG-PET/CT before surgical resection were included in the test cohort and 12 patients in the validation cohort. GTVs were delineated on the staging PET/CT by automatic delineation using various percentage threshold of maximum SUV (SUVmax) and absolute SUV. The maximum pathological tumor diameter was then matched with the maximum auto-delineated tumor diameter with varying SUV thresholds. First-order linear regression and Bland–Altman plots were used to obtain an optimal SUV threshold for each patient. Three radiation oncologists with varying degrees of experiences also delineated GTVs with the visual aid of PET/CT to assess interobserver variation in delineation.

Results:

In the test set, the mean optimal percentage threshold for GTV was SUVmax of 35.6%±18.6% and absolute SUV of 4.35 ± 1.7. In the validation set, the mean optimal percentage threshold SUV and absolute SUV were 36.9 ± 16.9 and 4.1 ± 1.6, respectively. After a combined analysis of all 37 patients, the mean optimal threshold was 36% ± 17.9% and 4.27 ± 1.7, respectively. Using Bland–Altman plots, auto-contouring with 40% SUVmax and SUV 4 was in greater agreement with the pathological tumor diameter.

Conclusion:

Automatic GTV delineation on PETCT in NSCLC with percentage threshold SUV of 40% and absolute SUV of 4 correlated best with pathological tumor size. Auto-contouring using these thresholds will increase the precision of radiotherapy contouring of GTV and will save time.

Impact of segmentation and discretization on radiomic features in 68Ga-DOTA-TOC PET/CT images of neuroendocrine tumor

OBJECTIVE

To identify the impact of segmentation methods and intensity discretization on radiomic features (RFs) extraction from 68Ga-DOTA-TOC PET images in patients with neuroendocrine tumors.

METHODS

Forty-nine patients were retrospectively analyzed. Tumor contouring was performed manually by four different operators and with a semi-automatic edge-based segmentation (SAEB) algorithm. Three SUVmax fixed thresholds (20, 30, 40%) were applied. Fifty-one RFs were extracted applying two different intensity rescale factors for gray-level discretization: one absolute (AR60 = SUV from 0 to 60) and one relative (RR = min-max of the VOI SUV). Dice similarity coefficient (DSC) was calculated to quantify segmentation agreement between different segmentation methods. The impact of segmentation and discretization on RFs was assessed by intra-class correlation coefficients (ICC) and the coefficient of variance (COVL). The RFs' correlation with volume and SUVmax was analyzed by calculating Pearson's correlation coefficients.

RESULTS

DSC mean value was 0.75 ± 0.11 (0.45-0.92) between SAEB and operators and 0.78 ± 0.09 (0.36-0.97), among the four manual segmentations. The study showed high robustness (ICC > 0.9): (a) in 64.7% of RFs for segmentation methods using AR60, improved by applying SUVmax threshold of 40% (86.5%); (b) in 50.9% of RFs for different SUVmax thresholds using AR60; and (c) in 37% of RFs for discretization settings using different segmentation methods. Several RFs were not correlated with volume and SUVmax.

CONCLUSIONS

RFs robustness to manual segmentation resulted higher in NET 68Ga-DOTA-TOC images compared to 18F-FDG PET/CT images. Forty percent SUVmax thresholds yield superior RFs stability among operators, however leading to a possible loss of biological information. SAEB segmentation appears to be an optimal alternative to manual segmentation, but further validations are needed. Finally, discretization settings highly impacted on RFs robustness and should always be stated.

A physics-guided modular deep-learning based automated framework for tumor segmentation in PET

An important need exists for reliable positron emission tomography (PET) tumor-segmentation methods for tasks such as PET-based radiation-therapy planning and reliable quantification of volumetric and radiomic features. To address this need, we propose an automated physics-guided deep-learning-based three-module framework to segment PET images on a per-slice basis. The framework is designed to help address the challenges of limited spatial resolution and lack of clinical training data with known ground-truth tumor boundaries in PET. The first module generates PET images containing highly realistic tumors with known ground-truth using a new stochastic and physics-based approach, addressing lack of training data. The second module trains a modified U-net using these images, helping it learn the tumor-segmentation task. The third module fine-tunes this network using a small-sized clinical dataset with radiologist-defined delineations as surrogate ground-truth, helping the framework learn features potentially missed in simulated tumors. The framework was evaluated in the context of segmenting primary tumors in 18F-fluorodeoxyglucose (FDG)-PET images of patients with lung cancer. The framework's accuracy, generalizability to different scanners, sensitivity to partial volume effects (PVEs) and efficacy in reducing the number of training images were quantitatively evaluated using Dice similarity coefficient (DSC) and several other metrics. The framework yielded reliable performance in both simulated (DSC: 0.87 (95% confidence interval (CI): 0.86, 0.88)) and patient images (DSC: 0.73 (95% CI: 0.71, 0.76)), outperformed several widely used semi-automated approaches, accurately segmented relatively small tumors (smallest segmented cross-section was 1.83 cm2), generalized across five PET scanners (DSC: 0.74 (95% CI: 0.71, 0.76)), was relatively unaffected by PVEs, and required low training data (training with data from even 30 patients yielded DSC of 0.70 (95% CI: 0.68, 0.71)). In conclusion, the proposed automated physics-guided deep-learning-based PET-segmentation framework yielded reliable performance in delineating tumors in FDG-PET images of patients with lung cancer.

Optimal method for metabolic tumour volume assessment of cervical cancers with inter-observer agreement on [18F]-fluoro-deoxy-glucose positron emission tomography with computed tomography

PURPOSE

Cervical cancer metabolic tumour volume (MTV) derived from [18F]-FDG PET/CT has a role in prognostication and therapy planning. There is no standard method of outlining MTV on [18F]-FDG PET/CT. The aim of this study was to assess the optimal method to outline primary cervical tumours on [18F]-FDG PET/CT using MRI-derived tumour volumes as the reference standard.

METHODS

81 consecutive cervical cancer patients with pre-treatment staging MRI and [18F]-FDG PET/CT imaging were included. MRI volumes were compared with different PET segmentation methods. Method 1 measured MTVs at different SUV_max thresholds ranging from 20 to 60% (MTV₂₀-MTV₆₀) with bladder masking and manual adjustment when required. Method 2 created an isocontour around the tumour prior to different SUV_max thresholds being applied. Method 3 used an automated gradient method. Inter-observer agreement of MTV, following manual adjustment when required, was recorded.

RESULTS

For method 1, the MTV₂₅ and MTV₃₀ were closest to the MRI volumes for both readers (mean percentage change from MRI volume of 2.9% and 13.4% for MTV₂₅ and - 13.1% and - 2.0% for MTV₃₀ for readers 1 and 2). 70% of lesions required manual adjustment at MTV₂₅ compared with 45% at MTV₃₀. There was excellent inter-observer agreement between MTV₃₀ to MTV₆₀ (ICC ranged from 0.898-0.976 with narrow 95% confidence intervals (CIs)) and moderate agreement at lower thresholds (ICC estimates of 0.534 and 0.617, respectively for the MTV₂₀ and MTV₂₅ with wide 95% CIs). Bladder masking was performed in 86% of cases overall. For method 2, excellent correlation was demonstrated at MTV₂₅ and MTV₃₀ (mean % change from MRI volume of -3.9% and - 8.6% for MTV₂₅ and - 16.9% and 19% for MTV₃₀ for readers 1 and 2, respectively). This method also demonstrated excellent ICC across all thresholds with no manual adjustment. Method 3 demonstrated excellent ICC of 0.96 (95% CI 0.94-0.97) but had a mean percentage difference from the MRI volume of - 19.1 and - 18.2% for readers 1 and 2, respectively. 21% required manual adjustment for both readers.

CONCLUSION

MTV₃₀ provides the optimal correlation with MRI volume taking into consideration the excellent inter-reader agreement and less requirement for manual adjustment.

Intensity threshold based solid tumour segmentation method for Positron Emission Tomography (PET) images: A review

Accurate, robust and reproducible delineation of tumour in Positron Emission Tomography (PET) is essential for diagnosis, treatment planning and response assessment. Since standardized uptake value (SUV) – a normalized semiquantitative parameter used in PET is represented by the intensity of the PET images and related to the radiotracer uptake, a SUV based threshold method is a natural choice to delineate the tumour. However, determination of an optimum threshold value is a challenging task due to low spatial resolution, and signal-to-noise ratio (SNR) along with finite image sampling constraint. The aim of the review is to summarize different fixed and adaptive threshold-based PET image segmentation approaches under a common mathematical framework Advantages and disadvantages of different threshold based methods are also highlighted from the perspectives of diagnosis, treatment planning and response assessment. Several fixed threshold values (30%–70% of the maximum SUV of the tumour (SUV_maxT)) have been investigated. It has been reported that the fixed threshold-based method is very much dependent on the SNR, tumour to background ratio (TBR) and the size of the tumour. Adaptive threshold-based method, an alternative to fixed threshold, can minimize these dependencies by accounting for tumour to background ratio (TBR) and tumour size. However, the parameters for the adaptive methods need to be calibrated for each PET camera system (e.g., scanner geometry, image acquisition protocol, reconstruction algorithm etc.) and it is not straight forward to implement the same procedure to other PET systems to obtain similar results. It has been reported that the performance of the adaptive methods is also not optimum for smaller volumes with lower TBR and SNR. Statistical analysis carried out on the NEMA thorax phantom images also indicates that regions segmented by the fixed threshold method are significantly different for all cases. On the other hand, the adaptive method provides significantly different segmented regions only for low TBR with different SNR. From this viewpoint, a robust threshold based segmentation method that will be less sensitive to SUVmaxT, SNR, TBR and volume needs to be developed. It was really challenging to compare the performance of different threshold-based methods because the performance of each method was tested on dissimilar data set with different data acquisition and reconstruction protocols along with different TBR, SNR and volumes. To avoid such difficulties, it will be desirable to have a common database of clinical PET images acquired with different image acquisition protocols and different PET cameras to compare the performance of automatic segmentation methods. It is also suggested to report the changes in SNR and TBR while reporting the response using threshold based methods.

Borderline Resectable and Locally Advanced Pancreatic Cancer: FDG PET/MRI and CT Tumor Metrics for Assessment of Pathologic Response to Neoadjuvant Therapy and Prediction of Survival

BACKGROUND. Imaging biomarkers of response to neoadjuvant therapy (NAT) for pancreatic ductal adenocarcinoma (PDA) are needed to optimize treatment decisions and long-term outcomes. OBJECTIVE. The purpose of this study was to investigate metrics from PET/MRI and CT to assess pathologic response of PDA to NAT and to predict overall survival (OS). METHODS. This retrospective study included 44 patients with ¹⁸F-FDG-avid borderline resectable or locally advanced PDA on pretreatment PET/MRI who also underwent post-NAT PET/MRI before surgery between August 2016 and February 2019. Carbohydrate antigen 19-9 (CA 19-9) level, metabolic metrics from PET/MRI, and morphologic metrics from CT (n = 34) were compared between pathologic responders (College of American Pathologists scores 0 and 1) and nonresponders (scores 2 and 3). AUCs were measured for metrics significantly associated with pathologic response. Relation to OS was evaluated with Cox proportional hazards models. RESULTS. Among 44 patients (22 men, 22 women; mean age, 62 ± 11.6 years), 19 (43%) were responders, and 25 (57%) were nonresponders. Median OS was 24 months (range, 6-42 months). Before treatment, responders and nonresponders did not differ in CA 19-9 level, metabolic metrics, or CT metrics (p > .05). After treatment, responders and nonresponders differed in complete metabolic response (CMR) (responders, 89% [17/19]; nonresponders, 40% [10/25]; p = .04], mean change in SUV_max (ΔSUV_max; responders, -70% ± 13%; nonresponders, -37% ± 42%; p < .001), mean change in SUV_max corrected to serum glucose level (ΔSUV_gluc) (responders, -74% ± 12%; nonresponders, -30% ± 58%; p < .001), RECIST response on CT (responders, 93% [13/14]; nonresponders, 50% [10/20]; p = .02)], and mean change in tumor volume on CT (ΔTvol) (responders, -85% ± 21%; nonresponders, 57% ± 400%; p < .001). The AUC of CMR for pathologic response was 0.75; ΔSUV_max, 0.83; ΔSUV_gluc, 0.87; RECIST, 0.71; and ΔTvol 0.86. The AUCs of bivariable PET/MRI and CT models were 0.83 (CMR and ΔSUV_max), 0.87 (CMR and ΔSUV_gluc), and 0.87 (RECIST and ΔTvol). OS was associated with CMR (p = .03), ΔSUV_max (p = .003), ΔSUV_gluc (p = .003), and RECIST (p = .046). CONCLUSION. Unlike CA 19-9 level, changes in metabolic metrics from PET/MRI and morphologic metrics from CT after NAT were associated with pathologic response and OS in patients with PDA, warranting prospective validation. CLINICAL IMPACT. Imaging metrics associated with pathologic response and OS in PDA could help guide clinical management and outcomes for patients with PDA who undergo emergency therapeutic interventions.

Sensitivity analysis of FDG PET tumor voxel cluster radiomics and dosimetry for predicting mid-chemoradiation regional response of locally advanced lung cancer

We investigated the sensitivity of regional tumor response prediction to variability in voxel clustering techniques, imaging features, and machine learning algorithms in 25 patients with locally advanced non-small cell lung cancer (LA-NSCLC) enrolled on the FLARE-RT clinical trial. Metabolic tumor volumes (MTV) from pre-chemoradiation (PETpre) and mid-chemoradiation fluorodeoxyglucose-positron emission tomography (FDG PET) images (PETmid) were subdivided into K-means or hierarchical voxel clusters by standardized uptake values (SUV) and 3D-positions. MTV cluster separability was evaluated by CH index, and morphologic changes were captured by Dice similarity and centroid Euclidean distance. PETpre conventional features included SUVmean, MTV/MTV cluster size, and mean radiation dose. PETpre radiomics consisted of 41 intensity histogram and 3D texture features (PET Oncology Radiomics Test Suite) extracted from MTV or MTV clusters. Machine learning models (multiple linear regression, support vector regression, logistic regression, support vector machines) of conventional features or radiomic features were constructed to predict PETmid response. Leave-one-out-cross-validated root-mean-squared-error (RMSE) for continuous response regression (ΔSUVmean) and area-under-receiver-operating-characteristic-curve (AUC) for binary response classification were calculated. K-means MTV 2-clusters (MTVhi, MTVlo) achieved maximum CH index separability (Friedman p < 0.001). Between PETpre and PETmid, MTV cluster pairs overlapped (Dice 0.70-0.87) and migrated 0.6-1.1 cm. PETmid ΔSUVmean response prediction was superior in MTV and MTVlo (RMSE = 0.17-0.21) compared to MTVhi (RMSE = 0.42-0.52, Friedman p < 0.001). PETmid ΔSUVmean response class prediction performance trended higher in MTVlo (AUC = 0.83-0.88) compared to MTVhi (AUC = 0.44-0.58, Friedman p = 0.052). Models were more sensitive to MTV/MTV cluster regions (Friedman p = 0.026) than feature sets/algorithms (Wilcoxon signed-rank p = 0.36). Top-ranked radiomic features included GLZSM-LZHGE (large-zone-high-SUV), GTSDM-CP (cluster-prominence), GTSDM-CS (cluster-shade) and NGTDM-CNT (contrast). Top-ranked features were consistent between MTVhi and MTVlo cluster pairs but varied between MTVhi-MTVlo clusters, reflecting distinct regional radiomic phenotypes. Variability in tumor voxel cluster response prediction can inform robust radiomic target definition for risk-adaptive chemoradiation in patients with LA-NSCLC. FLARE-RT trial: NCT02773238.

The application of artificial intelligence and radiomics in lung cancer

Lung cancer is one of the most leading causes of death throughout the world, and there is an urgent requirement for the precision medical management of it. Artificial intelligence (AI) consisting of numerous advanced techniques has been widely applied in the field of medical care. Meanwhile, radiomics based on traditional machine learning also does a great job in mining information through medical images. With the integration of AI and radiomics, great progress has been made in the early diagnosis, specific characterization, and prognosis of lung cancer, which has aroused attention all over the world. In this study, we give a brief review of the current application of AI and radiomics for precision medical management in lung cancer.

Radiodynamic therapy using 15-MV radiation combined with 5-aminolevulinic acid and carbamide peroxide for prostate cancer in vivo

Photodynamic therapy has been clinically proven to be effective, but its effect is limited to relatively shallow tumors because of its use of visible light. Radiodynamic therapy (RDT) has therefore been investigated as a means to treat deep-seated tumors. In this study, the treatment effect of a novel form of RDT consisting of radiation combined with 5-aminolevulinic acid (5-ALA) and carbamide peroxide was investigated using a mouse model. Male nude mice were injected bilaterally and subcutaneously with human prostate cancer (PC-3) cells and randomized into 8 treatment groups, consisting of various combinations of 15-MV radiotherapy (RT), 5-ALA, and carbamide peroxide. The treatment effect of a single fraction of treatment was measured by calculating tumor growth delay, monitored using weekly MR scans. The ability of the drugs to be delivered to the tumors was qualitatively measured using ¹⁸ F-FDG PET/CT scans. RDT was shown to significantly delay the tumor growth for the mouse model and tumor cell line investigated in this work. Tumors treated with RDT showed a decrease in tumor growth of 24 ± 9% and 21 ± 8% at one and two weeks post-treatment, respectively. Peroxide and 5-ALA did not contribute significantly to tumor growth delay when administered alone or separately with RT. Blood perfusion was shown to be able to deliver agents to the tumors investigated in this work, although uptake of ¹⁸ F-FDG was shown to be non-uniform.

Predictive value of interim 18F-FDG-PET in patients with non-small cell lung cancer treated with definitive radiation therapy

PURPOSE

We evaluated that early metabolic response determined by 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) during radiotherapy (RT), predicts outcomes in non-small cell lung cancer.

MATERIAL AND METHODS

Twenty-eight patients evaluated using pretreatment 18F-FDG-PET/CT (PETpre) and interim 18F-FDG-PET/CT (PETinterim) after 11 fractions of RT were retrospectively reviewed. Maximum standardized uptake value (SUVmax) was calculated for primary lesion. Predictive value of gross tumor volume (ΔGTV) and SUVmax (ΔSUVmax) changes was evaluated for locoregional control (LRC), distant failure (DF), and overall survival (OS). Metabolic responders were patients with ΔSUVmax >40%.

RESULTS

Metabolic responders showed better trends in 1-year LRC (90.9%) than non-responders (47.1%) (p = 0.086). Patients with large GTVpre (≥120 cc) demonstrated poor LRC (hazard ratio 4.14, p = 0.022), while metabolic non-responders with small GTVpre (<120 cc) and metabolic responders with large GTVpre both had 1-year LRC rates of 75.0%. Reduction of 25% in GTV was not associated with LRC; however, metabolic responders without a GTV response showed better 1-year LRC (83.3%) than metabolic non-responders with a reduction in GTV (42.9%). Metabolic responders showed lower 1-year DF (16.7%) than non-responders (50.0%) (p = 0.025). An ΔSUVmax threshold of 40% yielded accuracy of 64% for predicting LRC, 75% for DF, and 54% for OS. However, ΔGTV > 25% demonstrated inferior diagnostic values than metabolic response.

CONCLUSIONS

Changes in tumor metabolism diagnosed using PETinterim during RT better predicted treatment responses, recurrences, and prognosis than other factors historically used.

PET/CT of breast cancer regional nodal recurrences: an evaluation of contouring atlases

BACKGROUND

To validate the Radiation Therapy Oncology Group (RTOG) and European Society for Radiotherapy and Oncology (ESTRO) breast cancer nodal clinical target volumes (CTVs) and to investigate the Radiotherapy Comparative Effectiveness Consortium (RADCOMP) Posterior Neck volume in relation to regional nodal recurrences (RNR).

METHODS

From a population-based database, 69 patients were identified who developed RNR after curative treatment for breast cancer. RNRs were detected with 18-fluorodeoxyglucose-positron emission tomography-computed tomography (PET/CT). All patients were treatment-naïve for RNR when imaged. The RTOG and ESTRO nodal CTVs and RADCOMP Posterior Neck volumes were contoured onto a template patient's CT. RNRs were contoured on each PET/CT and deformed onto the template patient's CT. Each RNR was represented by a 5 mm diameter epicentre, and categorized as 'inside', 'marginal' or 'outside' the CTV boundaries.

RESULTS

Sixty-nine patients with 226 nodes (median 2, range 1-11) were eligible for inclusion. Thirty patients had received adjuvant tangent and regional nodal radiotherapy, 16 tangent-only radiotherapy and 23 no adjuvant radiotherapy. For the RTOG CTVs, the RNR epicentres were 70% (158/226) inside, 4% (8/226) marginal and 27% (60/226) outside. They included the full extent of the RNR epicentres in 38% (26/69) of patients. Addition of the RADCOMP Posterior Neck volume increased complete RNR coverage to 48% (33/69) of patients. For the ESTRO CTVs, the RNR epicentres were 73% (165/226) inside, 2% (4/226) marginal and 25% (57/226) outside. They included the full extent of the RNR epicentres in 57% (39/69) of patients. Addition of the RADCOMP Posterior Neck volume increased complete RNR coverage to 70% (48/69) of patients.

CONCLUSIONS

The RTOG and ESTRO breast cancer nodal CTVs do not fully cover all potential areas of RNR, but the ESTRO nodal CTVs provided full coverage of all RNR epicentres in 19% more patients than the RTOG nodal CTVs. With addition of the RADCOMP Posterior Neck volume to the ESTRO CTVs, 70% of patients had full coverage of all RNR epicentres.

18F-fluorodeoxyglucose positron emission tomography correlates with tumor immunometabolic phenotypes in resected lung cancer

Enhanced tumor glycolytic activity is a mechanism by which tumors induce an immunosuppressive environment to resist adoptive T cell therapy; therefore, methods of assessing intratumoral glycolytic activity are of considerable clinical interest. In this study, we characterized the relationships among tumor ¹⁸F-fluorodeoxyglucose (FDG) retention, tumor metabolic and immune phenotypes, and survival in patients with resected non-small cell lung cancer (NSCLC). We retrospectively analyzed tumor preoperative positron emission tomography (PET) ¹⁸F-FDG uptake in 59 resected NSCLCs and investigated correlations between PET parameters (SUV_Max, SUV_Total, SUV_Mean, TLG), tumor expression of glycolysis- and immune-related genes, and tumor-associated immune cell densities that were quantified by immunohistochemistry. Tumor glycolysis-associated immune gene signatures were analyzed for associations with survival outcomes. We found that each ¹⁸F-FDG PET parameter was positively correlated with tumor expression of glycolysis-related genes. Elevated ¹⁸F-FDG SUV_Max was more discriminatory of glycolysis-associated changes in tumor immune phenotypes than other ¹⁸F-FDG PET parameters. Increased SUV_Max was associated with multiple immune factors characteristic of an immunosuppressive and poorly immune infiltrated tumor microenvironment, including elevated PD-L1 expression, reduced CD57⁺ cell density, and increased T cell exhaustion gene signature. Elevated SUV_Max identified immune-related transcriptomic signatures that were associated with enhanced tumor glycolytic gene expression and poor clinical outcomes. Our results suggest that ¹⁸F-FDG SUV_Max has potential value as a noninvasive, clinical indicator of tumor immunometabolic phenotypes in patients with resectable NSCLC and warrants investigation as a potential predictor of therapeutic response to immune-based treatment strategies.