Task 1:Data mining and information fusion for tumor response prediction

This task has started in November, 2012, with the recruitment of a PhD student, Mohamed Majdoub. M. Majdoub has started working on the processing and analysis of PET/CT images to derive new shape and heterogeneity metrics for characterization of functional tumor volumes that would be able to offer some predictive value on prognosis or response to therapy.

Beside technical investigations on these new features (robustness, correlation with volume, comparison of different calculation methods), he also investigated their potential predictive value in various cohorts of patients with breast and lung cancer (using the FDG radiotracer for metabolism) and Head and Neck cancer (with the FLT radiotracer for cellular proliferation).

His goal was to extract pertinent features from images within the context of response to (radio)chemotherapy prediction (breast, H&N), as well as prognosis of recurrence-free and overall survival (H&N, lung).

In addition to the image processing and analysis part of his work, M.Majdoub has also investigated the use of classification tools such as logistic regression, random forest algorithms, and support vector machines in order to incorporate multiple image-derived and other contextual variables (age, sex, histology, etc.) in a predictive model.

In order to extract image-derived features relevant for prognosis or response to therapy from PET images, M. Majdoub has exploited a pipeline of image processing tools such as noise filtering, partial volume effects correction, metabolic tumor volume automatic delineation, shape metrics, and heterogeneity quantification through textural features analysis.

Results from the technical investigations suggested that new calculations of heterogeneity metrics could provide valuable prognostic value in addition to standard clinical variables or usual PET image-derived metrics such as SUVs or metabolic volume. This was demonstrated in a multi-centric cohort of 555 patients, with prognostic value results on 112 esophageal cancer patients and 101 lung cancer patients. It was recently accepted in the J. Nucl Med [1]. We have further investigated the potential complementary prognostic value of PET and CT derived heterogeneity (both FDG radiotracer uptake heterogeneity from PET and tissues density from CT) in the lung cancer cohort and demonstrated the feasibility of developing a multimodal PET/CT heterogeneity based prognostic model with high stratification power (hazard ratio above 4) regarding outcome. Preliminary results for this work were presented at the annual meeting of the AAPM [2] and was recently submitted as a full paper to Radiology [3].

Another study included 171 patients with breast cancer treated with chemotherapy, with two sequential FDG PET/CT scans. An interesting result was that the optimal PET-derived parameter (either SUVmax, volume or TLG) for the prediction of therapy response using the evolution between the baseline scan and the during chemotherapy scan actually depended on the tumor subgroup (HER2+, ER+/HER2- or triple-negative breast cancer). This work has been accepted in Radiology [4]. In addition, we have investigated the prognostic value of PET-derived parameters for disease-free survival and showed that both SUVmax and TLG have prognostic value. This has been submitted to the Journal of Nuclear Medicine and is currently under minor revision [5]. Finally, we have investigated the relationships between clinical and histopathological factors and heterogeneity features in the same cohort. This work is currently in revision in the Journal of Nuclear Medicine [6].

Another original and novel result was obtained on the cohort of H&N cancer patients treated with (radio)chemotherapy that underwent one FLT PET scan before treatment and a second scan during treatment. It was found that the tri-dimensional shape and the intra-tumor radiotracer spatial distribution (heterogeneity) of the proliferative tumor volume (as quantified on FLT PET images) was significantly associated with recurrence-free survival. Tumors’proliferative volumes with more complex 3D shapes and higher tracer uptake heterogeneity led to more and earlier recurrence after treatment that those with less complex shapes and more homogeneous uptake. In addition, the decrease of the complexity of the 3D shape and the heterogeneity of the tumors’ proliferative volumes during treatment was also associated with less or later recurrence. These new features of tumors extracted from PET images were found to be prognostic factors of overall and recurrence-free survival, with higher discriminative power that standard PET measurements such as functional volume and standardize uptake maximum or mean values.

One hypothesis that can explain these results is the fact that the more complex and heterogeneous a tumor volume is (at least according to the cellular proliferation), the more difficult it is to treat efficiently with a homogeneous radiotherapy dose (which is the standard practice and was the case for this cohort of patients) and/or chemotherapy because the drug cannot be efficiently distributed within the tumor. A quasi-spherical shape with homogeneous uptake distribution is easier to cover with adequate dose with radiotherapy, and the drug (chemotherapy) can be distributed within the entire tumor more efficiently.

These results have been presented in an oral presentation at the nuclear medicine meeting in Vancouver (2013) [7], and a complete paper has been recently submitted to the Journal of Nuclear Medicine [8].



1. M. Hatt, M. Majdoub, M. Vallières, F. Tixier, C. Cheze Le Rest, D.Groheux, E.Hindié, A. Martineau, O. Pradier, R. Hustinx, R. Perdrisot, R.Guillevin, I. El Naqa, D. Visvikis. FDG PET uptake characterization through texture analysis: investigating the complementary nature of heterogeneity and functional tumor volume in a multi-cancer site patient cohort. J Nucl Med 2015 56(1):38-44.

2. M-C. Desseroit, D. Visvikis, F. Tixier, M. Majdoub, R. Perdrisot, R. Guillevin, O. Pradier, C. Cheze Le Rest, M. Hatt. Complementary Prognostic Value of CT and 18F-FDG PET Non-Small Cell Lung Cancer Tumor Heterogeneity Features Quantified Trough Texture Analysis. AAMP annual meeting 2014.

3. M-C. Desseroit, D. Visvikis, F. Tixier, M. Majdoub, R. Perdrisot, R. Guillevin, O. Pradier, M. Hatt, C. Cheze Le Rest. Complementary prognostic value of intra-tumor heterogeneity in Non-Small Cell Lung Cancer assessed by combined textural analysis of functional and morphological data using 18F-FDG PET/CT images. Radiology (submitted)

4. D.Groheux, M. Majdoub, A. Martineau, D. Visvikis, M. Espié, M.Hatt, E. Hindié. Early metabolic response to neoadjuvant treatment: 18FDG-PET/CT criteria according to breast cancer subtype. Radiology 2015 (in press).

5. D.Groheux, M. Majdoub, A. Martineau, D. Visvikis, M. Espié, M.Hatt, E. Hindié. 18FDG uptake and total lesion glycolysis measured at baseline and after 2 courses of neoadjuvant chemotherapy are powerful tools to predict relapse in patients with ER+/HER2- breast cancer. J Nucl Med (in minor revision)

6. D. Groheux, M. Majdoub, F. Tixier, C. Cheze Le Rest, A. Martineau, P. Merlet, M. Espié, A. de Roquancourt, E. Hindié, M. Hatt. Do clinical, histological or immunohistochemical primary tumor characteristics translate into different 18FDG-PET/CT image features in stage II-III breast cancer?J Nucl Med (in revision)

7. Majdoub M, Visvikis D, Tixier F, B. Hoeben, E. Visser, Cheze-LeRest C, Hatt M. Proliferative 18F-FLT PET tumorvolumes characterization for prediction of locoregional recurrenceand disease-free survival in head and neck cancer. Society of nuclear medicine and molecular imaging annual meeting, 2013.

8. Majdoub M, Visvikis D, Tixier F, B. Hoeben, E. Visser, Cheze-Le Rest C, Hatt M. Prognostic value of head and neck tumor shape features derived from 18F-FLT PET/CT images . J Nuc Med (submitted)