Medical Imaging Biomarker Discovery and Integration Towards AI-Based Personalized Radiotherapy

Intensity-modulated radiation therapy (IMRT) has been used for high-accurate physical dose distribution sculpture and employed to modulate different dose levels into Gross Tumor Volume (GTV), Clinical Target Volume (CTV) and Planning Target Volume (PTV). GTV, CTV and PTV can be prescribed at different dose levels, however, there is an emphasis that their dose distributions need to be uniform, despite the fact that most types of tumour are heterogeneous. With traditional radiomics and artificial intelligence (AI) techniques, we can identify biological target volume from functional images against conventional GTV derived from anatomical imaging. Functional imaging, such as multi parameter MRI and PET can be used to implement dose painting, which allows us to achieve dose escalation by increasing doses in certain areas that are therapy-resistant in the GTV and reducing doses in less aggressive areas. In this review, we firstly discuss several quantitative functional imaging techniques including PET-CT and multi-parameter MRI. Furthermore, theoretical and experimental comparisons for dose painting by contours (DPBC) and dose painting by numbers (DPBN), along with outcome analysis after dose painting are provided. The state-of-the-art AI-based biomarker diagnosis techniques is reviewed. Finally, we conclude major challenges and future directions in AI-based biomarkers to improve cancer diagnosis and radiotherapy treatment.

NIMG-49. A PROSPECTIVE, MULTI-CENTRE TRIAL OF FET-PET IN GLIOBLASTOMA PATIENTS - THE TROG 18.06 FIG STUDY: KEY ASPECTS OF IMAGING AND RADIATION ONCOLOGY CREDENTIALING

The FIG study is a prospective non-randomised study now recruiting up to 210 newly diagnosed GBM participants across ten Australian sites. Study outcomes will address the role of [18F] fluoroethyl-L-tyrosine positron emission tomography (FET-PET) in radiotherapy (RT) planning, evaluation of post-treatment changes versus disease progression and prognostication. We describe here the methodology and preliminary outcomes for site credentialing. Eligible participants with GBM undergo FET-PET imaging at three time-points: FET-PET1-post-operative pre-chemo-RT, FET-PET2 acquired one month post-chemo-RT and FET-PET3 (+/-FDG-PET) triggered when clinical and/or radiological (MRI) progression is suspected. Dynamic and static FET-PET images are analysed qualitatively and quantitatively. Radiotherapy is as per standard care with the treating Radiation Oncologist (RO) blinded to FET-PET1. Site nuclear medicine (NM) physicians are required to delineate a biological target volume (BTV) based on FET-PET1 with hybrid RT volumes derived post-hoc. Pre-trial NM quality assurance comprises certification from the Australasian Radiopharmaceutical Trials Network encompassing FET-PET radiochemistry Quality Control and PET camera calibration. Site and central integrated workflows incorporating multi-modality image registration, target volume/region of interest contouring and analysis have been developed. NM benchmarking involves delineation of FET-PET BTVs in 3 cases with another 3 cases addressing response criteria interpretation harmonized across FET-PET, FDG-PET and MRI. Site ROs complete 3 cases involving standard and hybrid target volume delineation based on pre-derived FET-PET volumes. All NM and RO credentialing cases undergo central expert review. To date, of six sites which have submitted full credentialing data, 19/21 RO and 6/6 planning cases were passed. Of 72 NM cases, 18/72 (25%) required resubmission, primarily related to ensuring standardisation of background regions and time activity curve interpretation. The FIG study will be pivotal in establishing the role of FET-PET in GBM management. The robust NM and RO credentialing program will build capacity and expertise in FET-PET production, acquisition and image interpretation.

Additional Value of 18F-FDOPA Amino Acid Analog Radiotracer to Irradiation Planning Process of Patients With Glioblastoma Multiforme

PurposeTo investigate the added value of 6-(18F]-fluoro-L-3,4-dihydroxyphenylalanine (FDOPA) PET to radiotherapy planning in glioblastoma multiforme (GBM).MethodsFrom September 2017 to December 2020, 17 patients with GBM received external beam radiotherapy up to 60 Gy with concurrent and adjuvant temozolamide. Target volume delineations followed the European guideline with a 2-cm safety margin clinical target volume (CTV) around the contrast-enhanced lesion+resection cavity on MRI gross tumor volume (GTV). All patients had FDOPA hybrid PET/MRI followed by PET/CT before radiotherapy planning. PET segmentation followed international recommendation: T/N 1.7 (BTV1.7) and T/N 2 (BTV2.0) SUV thresholds were used for biological target volume (BTV) delineation. For GTV-BTVs agreements, 95% of the Hausdorff distance (HD95%) from GTV to the BTVs were calculated, additionally, BTV portions outside of the GTV and coverage by the 95% isodose contours were also determined. In case of recurrence, the latest MR images were co-registered to planning CT to evaluate its location relative to BTVs and 95% isodose contours.ResultsAverage (range) GTV, BTV1.7, and BTV2.0 were 46.58 (6–182.5), 68.68 (9.6–204.1), 42.89 (3.8–147.6) cm3, respectively. HD95% from GTV were 15.5 mm (7.9–30.7 mm) and 10.5 mm (4.3–21.4 mm) for BTV1.7 and BTV2.0, respectively. Based on volumetric assessment, 58.8% (28–100%) of BTV1.7 and 45.7% of BTV2.0 (14-100%) were outside of the standard GTV, still all BTVs were encompassed by the 95% dose. All recurrences were confirmed by follow-up imaging, all occurred within PTV, with an additional outfield recurrence in a single case, which was not DOPA-positive at the beginning of treatment. Good correlation was found between the mean and median values of PET/CT and PET/MRI segmented volumes relative to corresponding brain-accumulated enhancement (r = 0.75; r = 0.72).Conclusion18FFDOPA PET resulted in substantial larger tumor volumes compared to MRI; however, its added value is unclear as vast majority of recurrences occurred within the prescribed dose level. Use of PET/CT signals proved to be feasible in the absence of direct segmentation possibilities of PET/MR in TPS. The added value of 18FFDOPA may be better exploited in the context of integrated dose escalation.

Correlation of [18F] FDG-PET/CT with dosimetry data: recurrence pattern after radiotherapy for head and neck carcinoma

ObjectiveTo analyze the pattern of failure in relation to pre-treatment [18F] FDG-PET/CT uptake in head and neck squamous cell carcinoma (HNSCC) patients treated with definitive radio-chemotherapy (RT-CHT).Methods and MaterialsFrom 2012 to 2016, 87 HNSCC patients treated with definitive RT-CHT, with intensity modulated radiation therapy with simultaneous integrated boost (IMRT-SIB), underwent pre-treatment [18F] FDG-PET/CT (PETpre), and MRI/CT for radiotherapy (RT) planning purposes. Patients with local recurrence, received [18F] FDG-PET/CT, (PETrec) at the time of the discovery of recurrence. In these patients, the biological target volume (BTV), BTVpre and BTVrec were segmented on PET images by means of an adaptive thresholding algorithm. The overlapping volume between BTVpre and BTVrec (BTVpre&rec) was generated and the dose coverage of BTVrec and BTVpre&rec was checked on the planning CT using the D99 and D95 dose metrics. The recurrent volume was defined as: ‘‘In-Field (IF)’’, ‘‘Extending Outside the Field (EOF)’’ or ‘‘Out-of-Field (OF)’’ if D95 was respectively equal or higher than 95%, D95 was between 95% and 20% or the D95 was less than 20% of prescribed dose.ResultsWe found 10/87 patients (11.5%) who had recurrence at primary site. Mean BTVpre was 13.1 cc (4.6-37.4 cc), while the mean BTVrec was 4.3 cc (1.1-12.7 cc). Two recurrences resulted 100% inside BTVpre, 4 recurrences were mostly inside (61%-91%) and 4 recurrences were marginal to BTVpre (33-1%). At dosimetric analysis, six recurrences (60%) were IF, 3 (30%) EOF and one (10%) OF. The mean D99 of the overlapping volumes BTVpre&rec was 68.1Gy (66.5-69.2 Gy), considering a prescription dose of 70 Gy to the planning target volume (PTV). ConclusionOur study shows that the recurrence may originate from the volume with the highest FDG-signal. Results support the hypothesis that an intensification of the dose on these volumes could be helpful to prevent local relapse.

A preliminary PET radiomics study of brain metastases using a fully automatic segmentation method

Background Positron Emission Tomography (PET) is increasingly utilized in radiomics studies for treatment evaluation purposes. Nevertheless, lesion volume identification in PET images is a critical and still challenging step in the process of radiomics, due to the low spatial resolution and high noise level of PET images. Currently, the biological target volume (BTV) is manually contoured by nuclear physicians, with a time expensive and operator-dependent procedure. This study aims to obtain BTVs from cerebral metastases in patients who underwent L-[11C]methionine (11C-MET) PET, using a fully automatic procedure and to use these BTVs to extract radiomics features to stratify between patients who respond to treatment or not. For these purposes, 31 brain metastases, for predictive evaluation, and 25 ones, for follow-up evaluation after treatment, were delineated using the proposed method. Successively, 11C-MET PET studies and related volumetric segmentations were used to extract 108 features to investigate the potential application of radiomics analysis in patients with brain metastases. A novel statistical system has been implemented for feature reduction and selection, while discriminant analysis was used as a method for feature classification. Results For predictive evaluation, 3 features (asphericity, low-intensity run emphasis, and complexity) were able to discriminate between responder and non-responder patients, after feature reduction and selection. Best performance in patient discrimination was obtained using the combination of the three selected features (sensitivity 81.23%, specificity 73.97%, and accuracy 78.27%) compared to the use of all features. Secondly, for follow-up evaluation, 8 features (SUVmean, SULpeak, SUVmin, SULpeak prod-surface-area, SUVmean prod-sphericity, surface mean SUV 3, SULpeak prod-sphericity, and second angular moment) were selected with optimal performance in discriminant analysis classification (sensitivity 86.28%, specificity 87.75%, and accuracy 86.57%) outperforming the use of all features. Conclusions The proposed system is able i) to extract 108 features for each automatically segmented lesion and ii) to select a sub-panel of 11C-MET PET features (3 and 8 in the case of predictive and follow-up evaluation), with valuable association with patient outcome. We believe that our model can be useful to improve treatment response and prognosis evaluation, potentially allowing the personalization of cancer treatment plans.