RPS 1012b - Advanced imaging in paediatric cancers

Purpose:

To assess the effect of time-of-flight (TOF) on image quality at reduced count levels for whole-body [¹⁸F] FDG-PET/CT studies of paediatric oncology patients.

Methods and materials:

29 paediatric oncology patients (12F/17M, (12±5)-y/o, 13≤BMI≤28) who underwent routine whole-body PET/CT examinations on a Siemens Biograph mCT system with TOF capability (500ps) were included. The mean activity concentration was (3.8±08) MBq/kg. Events were randomly removed from the LM data to simulate reduced levels of [¹⁸F] FDG activity. All data was reconstructed using the vendor e7-tools with standard iterative image reconstruction OSEM, OSEM plus resolution recovery (PSF), and with and without TOF information. A 5 mm FWHM Gaussian post-filter was applied to all reconstructions. The reconstructed images were evaluated for noise, signal-to-noise-ratio (SNR) (liver), contrast-to-noise-ratio (CNR) (all lesions), and the SNR and CNR gains with TOF were calculated.

Results:

When using OSEM, the mean noise level was 11% (6%-18%) and when adding the TOF information, the noise was reduced to 9% (6%-15%). For PSF reconstructions, the lowest noise level was obtained with PSF+TOF 8%. At 50% counts, the PSF+TOF mean noise level of 10% was close to 11% for the 100% counts OSEM images. A mean gain in the SNR and CNR of 1.2-1.3 was obtained when the TOF information was included. Assuming that image noise levels below 10% are acceptable for clinical work-up, VOI analysis of the liver indicated this level be achieved at 50% counts or more when using PSF+TOF.

Conclusion:

A 50% dose reduction potential in paediatric PET/CT is possible when using a 10% acceptable threshold on noise levels and iterative reconstruction with PSF+TOF. This quantitative analysis will be complemented by the qualitative grading of image quality by clinical experts.

Limitations:

n/a

Ethics committee approval

Ethics approval 2019/ETH00138.

Funding:

Supported by the Austria-FWF Project I3451-N32.

Purpose:

Digital clinical data (imaging, pathology, genomic analytics, and wearable sensors) and patient electronic records (clinical profiling, treatment, and endpoints) are key enabling factors in clinical practice. This change is promoting clinical innovation models via real-world data-driven inferences. PRIMAGE, a 4-year European Commission-financed project having 16 European partners, is one of the more ambitious medical imaging research projects dealing with artificial intelligence in the paediatric cancer environment: neuroblastoma and diffuse intrinsic pontine glioma (DIPG).

The aim was to construct as an observational in silico study involving high-quality repository with anonymised data (imaging, clinical, molecular, and genetics) for the training of machine learning and multiscale algorithms to finally have a clinical decision support (CDS) tool.

Methods and materials:

The use of computational imaging allows the extraction of multiparametric data, multiscale models, visual analytics, and artificial intelligence, leading to a new era in radiomics, characterised by high-throughput extraction, storage, and analysis of a large amount of quantitative imaging features and parameters (imaging biomarkers).

Results:

The new platform will able to provide quantitative relevant information (virtual biopsies) for early disease diagnosis, disease phenotyping, disease grading, targeting therapies, and the evaluation of disease response to treatment in children with neuroblastoma and DIPG.

Conclusion:

PRIMAGE’s CDS system, offered as an open cloud-based platform, will provide precise clinical assistance for phenotyping, treatment allocation, and patient endpoint estimations based on imaging biomarkers, tumour growth simulation, advanced visualisation, and machine-learning approaches. Final results will be available for the scientific community at the end of the project, ready for translation to other malignant solid tumours.

Limitations:

GDPR limitations on patients recruitment will be commented.

Ethics committee approval

The project has the ethics committee approval of the coordinator hospital and reserach institute.

Funding:

H2020-SC1-DTH-2018-1 GA 826494.

Purpose:

To assess the role of radiomic analyses for characterising metastatic and localised paediatric renal Wilms’ tumours.

Methods and materials:

Paediatric patients affected by Wilms’ tumours referred to our tertiary centre for staging (i.e. treatment naïve) from 2012-2018 and examined by contrast-enhanced computed tomography were included in this retrospective study. Patients matching the inclusion criteria were then subdivided according to the presence of metastases (i.e. mW and lW). One radiologist expert in paediatric imaging drew regions of interest along the margins of all primary tumours covering the entire volume using open-source software (3D Slicer). The same software was applied to extract 33 radiomic features from each patient belonging to three categories: first-order statistics, grey-level co-occurrence matrix (GLCM), and the grey-level run-length matrix (GLRLM). The Student’s t-test was used to compare mW and lW patients for each radiomic feature (p<0.05). The accuracy of the variables showing a statistically significant difference was assessed by receiver operating characteristic curves.

Results:

32 patients (16 females, mean age±SD, 4±2.27 yrs) met the inclusion criteria and were analysed in this study. 10 patients were affected by metastatic disease (i.e. mW) and 22 by localised tumours (i.e. lW). Two features (one FOS, one GLRLM) showed a statistically significant difference between mW and lW: variance (p=0.043) and grey-level non-uniformity normalised (GLNUN; p=0.008). The area under the curve was good for GLNUN (AUC=0.805) and fair for the variance (AUC=0.655)

Conclusion:

GLNUN can be considered as a robust radiomic marker of metastatic spread in Wilms tumours.

Limitations:

Future studies addressing the clinical implications and considering the impact on the therapeutic treatment of our findings should be performed.

Ethics committee approval

Ethics committee approval obtained.

Funding:

No funding was received for this work.

Purpose:

To assess the feasibility and value of semi-automated diffusion-weighted imaging (DWI) volumetry of whole neuroblastic tumours with an apparent diffusion coefficient (ADC) map evaluation after neoadjuvant chemotherapy.

Methods and materials:

Paediatric patients who underwent surgical resection of neuroblastic tumours at our institution from 2013-2019, and who received a preoperative MRI scan with DWI after chemotherapy, were included. Tumour volumetry was assessed with a semi-automated approach in DWI using a dedicated software prototype. Quantitative ADC values were calculated automatically of the total tumour volume after manual exclusion of necrosis. Manual segmentation in T1-weighted and T2-weighted sequences was used as a reference standard for tumour volume comparison.

Results:

27 patients with 28 lesions (neuroblastoma (NB): n=19, ganglioneuroblastoma (GNB): n=7, and ganglioneuroma (GN): n= 2) could be evaluated. The mean patient age was 4.5±3.2 years. The median volume of standard volumetry (T1w or T2w) was 50.2 ml (interquartile range (IQR): 91.9 ml) vs 45.1 ml (IQR: 98.4 ml) of DWI (p=0.145). Mean ADC values (x10^-6 mm²/s) of the total tumour volume (without necrosis) were 1,187±301 in NB vs 1,552±114 in GNB/GN (p=0.037). The 5^th percentile of ADC values of NB (614±275) and GNB/GN (1,053±362) provided the most significant difference (p=0.007) with an area under the curve of 0.848 (p<0.001).

Conclusion:

Quantitative semi-automated DWI volumetry is feasible in neuroblastic tumours with an integrated analysis of tissue characteristics by providing automatically calculated ADC values of the whole tumour as well as an ADC heatmap.

Limitations:

Although the number of subjects is small, it represents one of the largest published cohorts with ADC analysis of neuroblastic tumours due to the low incidence of this disease.

Ethics committee approval

IRB approval obtained. Written informed consent waived.

Funding:

No funding was received for this work.

Purpose:

Neuroblastomas are the most frequent solid extracranial cancer in childhood. Its diagnosis, prognosis, and monitoring are based on the information provided by multiparametric magnetic resonance images. Our focus is to explore the utility of diffusion and perfusion changes in neuroblastomas as an early biomarker of diagnosis, using diffusion-weighted and dynamic contrast-enhanced MRIs.

Methods and materials:

Multiple MR imaging real-word sequences were used from 30 patients. Volumes-of-interest were calculated and transferred to DCE perfusion and apparent diffusion coefficient (ADC) maps. Histogram analysis and clustering unsupervised ML algorithms were used to determine the values of the mean and standard deviation of the initial area under the curve at 60 seconds (IAUC60) and ADC for automatic differentiation of neuroblastic tumours. The resolution was estimated and the data was smeared accordingly to identify and remove the noise and low-quality voxels.

Results:

Significant differences in the mean ADC were found for neuroblastic tumours: 1.0 for ganglioneuroma, 0.82 for ganglioneuroblastoma, and 0.52 for neuroblastoma, with an uncertainty of 0.11%, 42%, and 16%. This result improves tumour differentiation with respect to state-of-the-art voxel-by-voxel methodologies, which were found to be: 1.6 for ganglioneuroma, 1.7 for ganglioneuroblastoma, and 1.3 for neuroblastoma, with an uncertainty of 3.6%, 12%, and 17%. THe mean IAUC60 was found to have a value of 43 (and 14% uncertainty) for neuroblastoma, as opposed to a value of 17 (and 17% uncertainty) with state-of-the-art voxel-by-voxel methodologies.

Conclusion:

The proposed novel technique to determine IAUC60 and ADC parameters holds promise for differentiating benign and malignant neuroblastic tumours.

Limitations:

The dataset size and harmonisation among different machines and data collection techniques.

Ethics committee approval

La Fe Hospital received approval from the hospital's ethics committee.

Funding:

Horizon2020. Topic:SC1-DTH-07-2018-RIA. GA:826494.

Purpose:

To find a correlation between post-transplant thymic volume and thymopoiesis, to associate thymic renewal with early and long-term transplant outcomes, and to expand the role of the radiology team in transplant clinical decisions and the post-transplant management of paediatric patients.

Methods and materials:

Thymic volume assessment and thymocytes analysis were performed before HSCT (baseline volume), at 3, 6, 12 months, and long-term after HSCT.

74 paediatric patients who underwent an allo-HSCT from 2002-2018 were included. The control group consisted of 311 paediatric patients undergoing chest MRI for orthopaedic reasons.

The analysis was performed with HOROS software. Manual thymus tracing was performed for each cut, with the generation of a 3D reconstruction and a volume calculation.

Statistical analyses were performed using WinStat and MedCalc.

Results:

There is a statistically significant correlation between thymus volume and thymopoiesis with p<0.0001 and r=0.5720.

The average thymic volume at all evaluations is significantly greater in the subgroup without GVHD: 16.1 (±10.0) cm³ vs 11.2 (±8.7) cm³; p<0.05.

There is a significant difference in the average thymic volume between patients who survived after the HSCT (68.8 ± 48.46) and patients who did not (2.9 ± 2.11); p<0.0001.

Conclusion:

The restoration of thymic volume is a good indicator of positive outcomes in allogeneic HSCT, while a restoration failure is related to transplant-related mortality.

Thymic MRI assessment is now part of the current clinical management of transplanted patients and improves their integrated care pathways.

The next aim will be to create specific sequences that allow us to analyse the microenvironment of thymic tissue (“virtual biopsy”).

Limitations:

A monocentric and retrospective study with a limited sample of subjects.

Currently, our MRI protocol allows us to study only the thymic morphology but not the microenvironment of the thymic tissue.

Ethics committee approval

n/a

Funding:

No funding was received for this work.

Purpose:

In our hospital, ultrasound-guided fine-needle biopsy (US-guided FNB) is the first choice for tissue diagnosis in the paediatric population. In 2018, we added smear cytology in some of our biopsies, allowing for an immediate primary diagnosis. We retrospectively reviewed our experience with FNB assessing the accuracy rate, safety, and availability of the procedure, as well as the accuracy of the smear cytology and its added value for patient management.

Methods and materials:

Paediatric ultrasound-guided biopsies done in our hospital from 01/2018-09/2019 were studied. Data collection included demographics, clinical and procedural data, and follow-up.

Results:

106 biopsies were performed on 91 patients, 15 of them on known oncologic patients. 96.2% of biopsies were performed within 36 hours. 79 tumours were correctly diagnosed and 1 malignancy was misdiagnosed as a benign lesion. 23 biopsies were correctly diagnosed as non-tumour. Smear cytology was performed in 30 cases; 25 tumours and 5 reactive lymph nodes. The cytologist correctly differentiated the tumour from inflammation in all cases and diagnosed the tumour in 24 of the cases. The sensitivity of ultrasound-guided FNB is 98.7%, specificity 100%, and accuracy 99%. The accuracy of smear cytology for differentiating tumours from non-tumours is 100% and for final diagnosis 97%.

Conclusion:

We find ultrasound-guided FNB for suspected malignancy in the paediatric population highly available, safe, and accurate. In our small cohort, smear cytology was an excellent, real-time tool for differentiating tumour and non-tumour tissue, and in most cases allowed for early correct tumour diagnosis. This procedure may accelerate patient management and improve patient care.

Limitations:

A retrospective study with a small cohort.

Ethics committee approval

All biopsies were done under informed consent. The local Helsinki committee approved the study.

Funding:

No funding was received for this work.