Radiomics of the primary tumour as a tool to improve ¹⁸F-FDG-PET sensitivity in detecting nodal metastases in endometrial cancer

Endometrial cancer is the most common gynaecological cancer in developed countries [1], with increasing incidence as the global burden of obesity worsens [2]. Prognosis of this malignancy relies upon several factors as depth of myometrium invasion, lympho-vascular space invasion and lymph node (LN) involvement, being LNs the most common site of malignancy extrauterine spread [3–5].

The surgical management for nodal stage is still controversial. Two randomized trials and meta-analysis demonstrated that pelvic lymphadenectomy had no impact on survival for patients with early-stage endometrial cancer [6–11]. To minimize treatment-related morbidity and maintain the benefit of a surgical staging, the sentinel lymph node (SLN) concept has recently received an increasing interest [12].

18F-Fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) has been investigated as a non-invasive staging modality. In a recent prospective multicentre study including 207 patients, it demonstrated a sensitivity of 0.65 (95% confidence interval, 0.57–0.72) [13]. Small metastatic lymph node lesions may indeed remain undetected because of limited spatial resolution and associated partial volume effects [14]. In addition, the introduction of SLN biopsy ultrastaging, able to identify micrometastatic deposits, increased false-negative PET/CT findings [15]. False positive PET/CT findings in nodal detection are instead less frequent and generally due to inflammatory states.

It has been shown in several malignancies that a proper mining of quantitative FDG uptake distribution characteristics inside tumours allows obtaining prognostic information [16–20]. The aim of this study was to apply a radiomic analysis of 18F-FDG distribution inside the primary uterine lesion to help detecting suspicious nodal metastases, for a more personalized patient care of endometrial cancer. The radiomic analysis of gynaecologic primary tumour FDG uptake to predict nodal metastases has been already proposed in a cervical cancer study by Shen et al. [21]. However, in that study, a simple correlation between radiomic-based prediction and PET visual nodal detection was made. Conversely, in our study, histological analysis was considered as gold standard.

On the 86 DB1 patients, the prevalence of nodal metastases was 23%; FDG PET/CT sensitivity and specificity in detecting nodal metastases were respectively 50% and 99%. On the 29 DB2 patients, the prevalence of nodal metastases was 45%; FDG PET nodal metastases detection sensitivity and specificity were respectively 33% and 95%. Results are reported in Table 1.

Radiomic analysis of primary uterine lesion 18F-FDG uptake

Univariate analysis

In the univariate analysis, the widely used SUVmax was not significant (Fig. 3). Twelve features (TLG, two geometrical features, two GLCM features, two NGTDM features, two GLSZM features, one NGLCM feature, one NGLDM feature) were instead able to differentiate patients with LN metastases with a P value ≤ 0.01. The lowest P value and the highest AUC were obtained by GLSZM ZP (zone percentage of GLSZM). GLSZM in each sample (n, s) contains the number of 3D zones of size s and grey level n in VOI. Features computed on GLSZM provide a characterization of regional tumour heterogeneity, i.e. describe variations of intensity between regions and variations of homogeneous area size [35]. Zone percentage of GLSZM is the ratio between the total number of zones and the number of voxels in VOI. Tumours with nodal metastases show a reduced GLSZM ZP if compared with tumours without nodal metastases, with a P value of 2.8 × 10⁻⁴ and an AUC of 0.79.

Multivariate analysis: feature reduction and model selection

Feature reduction for multivariate analysis was applied to the 12 features with P ≤ 0.01, and 3 further features were selected: TLG, solidity (ratio between VOI volume and VOI convex hull volume, smaller for VOIs presenting concavities or surface irregularities) and NGLCM ENTROPY (Entropy of NGLCM). NGLCM is a N × N matrix, where N is the number of grey levels in VOI after quantization (N = 64 in our implementation). NGLCM (i, j) counts the relative frequency of grey levels i and j at a one voxel distance. NGLCM features therefore characterize tumour local heterogeneity on the basis of grey levels variations between neighbouring voxels. Entropy in particular measures the randomness of grey level distributions [28]. Spearman rank correlation vs GLSZM ZP of TLG, solidity and NGLCM ENTROPY is − 0.65, 0.83 and − 0.49, respectively. In Fig. 3, the distribution of the four selected features in DB1 patients with and without LN metastases is displayed, together with corresponding univariate analysis P values and AUC values. For GLSZM ZP, the optimal cut-off defined on DB1 (0.2755) is also represented. As to model selection, the best results were obtained with a 13 parameter neural network with three hidden layer neurons and 2 inputs (GLSZM ZP and SOLIDITY).

In Fig. 4, two examples of endometrium tumours (the left one with LN metastases, the right one without LN metastases) are represented, together with the relevant feature values and SUV max values. GLSZM ZP practically quantifies the number of uniform uptake regions inside the lesion after 64 level quantization, i.e. after the subdivision of the whole range of lesion uptake values into 64 equally spaced intervals. Lesions with lower GLSZM ZP, like the left one in Fig. 4, are the ones characterized by a wider range of uptake values before quantization (e.g. by the co-presence of very high-uptake voxels and very low-uptake regions, like necrotic areas) so that, after quantization, the number of uniform areas inside the lesion results small. Conversely, lesions with higher GLSZM ZP, like the right one in Fig. 4, are those characterized by a more uniform uptake and so by a narrower uptake value range, resulting in a higher number of uniform areas after quantization.

Radiomic model testing

Results of univariate and multivariate model testing on DB1 (LOO) and DB2 are shown in Table 2, together with the results of the nodal status visual assessment and the results of the unified prognostic framework combining nodal status visual assessments and primary tumour radiomic analysis (described in Fig. 2).

DB1-LOO (#86)		LN visual detection	Univariate model (GLSZM ZP)	LN visual detection + univariate model	Multivariate model (GLSZM ZP + Solidity)	LN visual detection + multivariate model
	Sensitivity	50%	75%	94%	67% ± 8%	86% ± 6%
	Specificity	99%	81%	67%	68% ± 3%	66% ± 3%
DB2 (#29)		LN visual detection	Univariate model (GLSZM ZP)	LN visual detection + univariate model	Multivariate model (GLSZM ZP + Solidity)	LN visual detection + multivariate model
	Sensitivity	33%	89%	89%	89%	89%
	Specificity	95%	80%	75%	80%	75%

In Fig. 5, results obtained on all DB1 (left panel) and DB2 patients (right panel) are shown.

The analysis of Fig. 5 and Table 2 shows that LN visual detection and primary tumour radiomic analysis are complementary. In particular, there are patients with negative LN at visual detection which have very low GLSZM ZP values and so are classified as positive by the radiomic analysis. The performance of visual analysis is worse on DB2 than on DB1, while radiomic analysis (trained on DB1) performs better on DB2. This could be explained by the increased incidence of micrometastases in DB2 (24% vs 13% in DB1). The combination of LN visual detection and radiomic analysis increases sensitivity compared to LN visual detection alone; however, since patients classified as LN positives by one of the two approaches are globally classified as positive, false positive findings augment, thus reducing specificity. The neural network multivariate model performs equally (on DB2) or even worse (on DB1) than the univariate model relying on GLSZM ZP only.

American College of Radiology Appropriateness Criteria suggest 18F-FDG PET/CT as the best technique for endometrial cancer nodal staging (score = 9), in particular for high-risk histologies. However, PET spatial resolution limits make visually detectable only LN metastatic deposits larger than 5 mm, resulting in FN findings. The PET/CT FN rate has recently further increased, due to sentinel node biopsy and ultrastaging improvement, able to identify micrometastases (< 2 mm) and isolated tumour cells, not detectable at PET/CT scans [15]. The aim of this study was therefore to assess if a radiomic approach on the primary uterine lesion could improve 18F-FDG PET sensitivity for nodal metastases. Standard imaging features like SUV, MTV and TLG were taken into account, together with histogram-based features, texture features and geometrical shape features. Data acquired and reconstructed on three different scanners were considered, with the specific aim of finding a nodal involvement predicting model with a certain robustness degree. It is worth noticing that the reconstruction voxel size was maintained identical in the three scanners, since it is known to be the factor most influencing texture feature absolute and prognostic values.

SUVmax, the most widely used PET feature, considered as an important indicator reflecting tumour aggressiveness, such as myometrial invasion or tumour grade, was not significantly correlated to lymph node status according to previous studies [36, 37]. Tumours with LN metastases were conversely generally characterized by higher MTV and higher TLG, as already observed in our previous report [23], and even more by higher heterogeneity and irregular borders, thus confirming the poorer prognosis of tumours presenting these characteristics, as already observed in PET radiomic literature.

An univariate prediction model relying on a unique heterogeneity feature (GLSZM ZP) and a neural network multivariate model considering GLSZM ZP and a geometrical shape feature (SOLIDITY) were defined on a database of 86 patients (DB1) and successively assessed on DB1 with a LOO approach and on a second independent database of 29 patients (DB2). On DB1, where sensitivity and specificity of LN visual detection were 50% and 99%, the univariate model obtained a sensitivity of 75% and a specificity of 81%, while the multivariate model a sensitivity of 67% ± 8% and a specificity of 68% ± 3% on 20 LOO sessions. On DB2, where sensitivity and specificity of LN visual detection were instead 33% and 95%, univariate and multivariate models performed identically, achieving a sensitivity of 89% and a specificity of 80%.

Our results show that, for nodal staging, GLSZM ZP alone performs better than any other feature or multivariate model. GLSZM ZP is a regional texture feature whose ability in differentiating patients with different prognosis has been already observed in various tumours [38]. Tumours characterized by the co-presence of high-uptake and low-uptake areas (and so by heterogeneous content) have a lower GLSZM ZP value and a poorer prognosis. In DB1, GLSZM ZP correlates with LN metastases presence with P = 2.8 × 10⁻⁴. GLSZM ZP has already been shown to be reproducible in test-retest studies [35], robust vs. segmentation algorithms, reconstruction parameters and algorithms [17, 19, 39]. The robustness of GLSZM ZP is here confirmed. Models were indeed defined on DB1 patients, which were studied on Discovery 600 and Discovery STE GE scanners, and validated on DB2 patients, which were instead for the most part (21/29) studied on a Discovery IQ GE scanner with a different acquisition/reconstruction protocol. In particular, Discovery 600 and Discovery ST patients were reconstructed without PSF modelling, while Discovery IQ patients with PSF modelling, which is known to influence image texture appearance. GLSZM ZP appears therefore particularly robust vs this aspect. The independency of GLSZM ZP from the scanner (together with that of Solidity, MTV, TLG, SUVmax and SUVmean) was also a-posteriori verified by means of a Kruskal-Wallis test.

In this study, we propose to combine a radiomic prediction model and the results of LN metastases visual detection into a unified framework to improve PET technique sensitivity. On DB1, the unified framework obtained a sensitivity of 94% and a specificity of 67%, while on DB2 a sensitivity of 89% and a specificity of 75%. A joint model of this kind maximizes the exploitation of the PET technique information for a more personalized and effective surgical treatment selection. The combination of PET/CT and SLN mapping has been proposed to minimize complications and to maximize LN status and cure rate definition [15]. The high PPV of FDG PET allows to direct patients positive at LN visual detection to lymphadenectomy, with debulking aim. However, in case of negative PET, microdisease cannot be excluded. Radiomics can help in further stratifying the risk of nodal metastases, to better select women who can benefit from SLN procedure and ultrastaging, for resource optimization. Negative PET patients with low GLSZM ZP seem in fact more likely to have micrometastases and should therefore be referred to SLN biopsy and ultrastaging in third level specialized hospitals.

Our preliminary results are promising in order to gather as much information as possible from an examination that is necessary in clinical setting of endometrial cancer patients. GLSZM ZP can be easily extracted from PET images by means of many freely available radiomic tools. Future perspectives will include the further assessment of the predictive values of CT/MRI features and eventually the construction and validation of multimodal models to further exploit PET/CT technique potentialities.

The main limitations of the present study are the small number of patients, in particular in DB2. In addition, DB2 is more recent and all patients underwent to SLN biopsy and ultrastaging, able to identify micrometastatic deposits not detectable by older histological techniques and largely under the PET spatial resolution limit [40]. This may justify the higher rate of micrometastases (24% vs 13%), the lower sensitivity of LN visual detection (33% vs 50%) and the better performance of the radiomic analysis observed in DB2 with respect to DB1. We plan to collect new data for training and new independent data for testing to confirm trends observed on DB2. Furthermore, we hope that these preliminary data could encourage cooperative efforts to confirm or to reject results on a wider and therefore more significant patient cohort of endometrial cancer patients.

The objective of this work was to try to improve PET sensitivity for nodal staging. A further step for treatment definition improvement may concern the insertion of PET features and laboratory/histological parameters into a global multivariate staging model. On the 115 patients we have, we have verified that, in accordance with literature, myometrium invasion and lympho-vascular space invasion are significantly correlated with LN metastases presence. These two variables may therefore be significant covariates in a global staging model. Grade and histology were instead not correlated. It is worth noticing however that most of patients in our database have endometrioid histology; thus, the correlation with histology may be assessed on a larger and more heterogeneous sample.

Endometrial cancers with LN metastases were generally characterized by higher heterogeneity at PET scan, and imaging features computed on the primary tumour were able to improve PET sensitivity in LN metastases detection.