Texture Analysis as Imaging Biomarker for recurrence in advanced cervical cancer treated with CCRT

This prospective study explored the application of texture features extracted from T2WI and apparent diffusion coefficient (ADC) maps in predicting recurrence of advanced cervical cancer patients treated with concurrent chemoradiotherapy (CCRT). We included 34 patients with advanced cervical cancer who underwent pelvic MR imaging before, during and after CCRT. Radiomic feature extraction was performed by using software at T2WI and ADC maps. The performance of texture parameters in predicting recurrence was evaluated. After a median follow-up of 31 months, eleven patients (32.4%) had recurrence. At four weeks after CCRT initiated, the most textural parameters (four T2 textural parameters and two ADC textural parameters) showed significant difference between the recurrence and nonrecurrence group (P values range, 0.002~0.046). Among them, RunLengthNonuniformity (RLN) from T2 and energy from ADC maps were the best selected predictors and together yield an AUC of 0.885. The support vector machine (SVM) classifier using ADC textural parameters performed best in predicting recurrence, while combining T2 textural parameters may add little value in prognosis. T2 and ADC textural parameters have potential as non-invasive imaging biomarkers in early predicting recurrence in advanced cervical cancer treated with CCRT.

tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) studies. It was reported that some pretreatment texture features as well as changes of texture features were associated with treatment outcome in various tumors [10][11][12][13] . To date, there have been a few reports on cervical cancer prognosis using texture analysis. Sylvain et al. and Ho et al. found texture features extracted from PET images could predict recurrence of cervical cancer better than SUV max while PET is less clinically used than MRI with radiation exposure 14,15 . Jeffrey et al. reported that texture features based on dynamic contrast-enhanced MRI (DCE-MRI) performed well in prediction recurrence but with a very limited sample size of 23 16 . Most studies on MR texture analysis used functional imaging such as DCE and DWI to obtain texture features, attentions have also been paid to the application to routine T1-and T2-weighted images (T2WI) 17,18 .
It is known that the ADC values are affected by perfusion, diffusion factors and artifacts. Furthermore, DCE MR imaging uses contrast media containing gadolinium which could induce nephrogenic systemic fibrosis in patients. Thus, recently, texture analysis based on T2WI have been investigated in oncologic imaging by several groups. Vignati et al. found that some texture features calculated on T2WI outperform ADC parameters in predicting prostate cancer aggressiveness 19 . A recent study reported that texture features were associated with pathologic complete response only at T2WI but not at DCE in breast cancer treated with neoadjuvant chemotherapy 20 . Carlo et al. demonstrated the efficacy of using texture analysis based on T2WI to predict tumor response to neoadjuvant chemoradiotherapy in rectal cancer 21 . To the best of our knowledge, there have been no previous reports examining texture analysis based on routine T2WI or DWI sequences for the prognosis of cervical cancer.
In the present study, we aimed to explore more promising texture features extracted from pre-and post-treatment T2WI and ADC maps to non-invasively predict recurrence of advanced cervical cancer patients treated with CCRT.
Texture parameters between different prognosis groups. At timepoint 1, Only several T2 textual parameters including 5 Percentile, range and RLN showed significant difference between the recurrence and nonrecurrence groups. (P values: 0.004, 0.008, 0.015).
At timepoint 2, T2 textual parameter RLN and ADC textural parameter correlation-GLCM25 were significant different between groups. (P values: 0.023; 0.011). (B,F) 2 weeks after CCRT initiated, tumor shows a significant decrease in size in both cases; (C,G) 4 weeks after CCRT initiated, tumor continues to shrink in both cases; (D,H) one month after CCRT completion, no obvious residual lesions could be seen on T2w images of both cases. Those two representative cases illustrate the difficulty of predicting cervical cancer recurrence for clinicians. Logistic regression models for predicting recurrence. At timepoint 2, with the best selected predictor as RLN from T2WI, the AUC for predicting recurrence was 0.739.
At timepoint 3, for T2 texture analysis, RLN was the best selected predictor and yield an AUC as 0.787. For ADC texture analysis, energy was the best selected predictor and yield an AUC as 0.775. After combining all parameters from T2WI and ADC maps, with the best selected predictors as RLN from T2WI and energy from ADC maps, the AUC could be improved to 0.885, though there were no significant differences between any two AUCs of the features (all P values > 0.05). The ROC curves of each regression model in predicting recurrence are displayed in Fig. 3. The detailed performances of each regression model are shown in Table 1.
At timepoint 1 and timepoint 4, no parameter was selected in the regression model.
Results of supervised classification. Since at timepoint 3, the number of parameters that could differentiate different groups were the most, and the regression models at timepoint 3 show relatively high AUCs for predicting recurrence, we considered timepoint 3 as the best timepoint for early predicting recurrence. The SVM classification results obtained by cross-validation on T2, ADC and T2 + ADC textural parameters at timepoint 3 are shown in Table 2. Textural parameters extracted from ADC maps had higher accuracy, sensitivity and specificity than those extracted from T2WI. T2 + ADC textural parameters also performed well in predicting recurrence, but did not show obviously better results than the sole ADC textual parameters.

Discussion
The results of our study demonstrate the potential use of texture analysis based on T2WI and ADC maps to predict recurrence of advanced cervical cancer treated with CCRT. We also found that four weeks after CCRT initiated was the optimal timepoint for early predicting cervical cancer recurrence. ADC textural parameters at four weeks after CCRT initiated performed best in predicting recurrence, while combining T2 textural parameters may add little value in prognosis. Preliminary reports have hinted at the potential use of texture analysis in cervical cancer imaging. Becker et al. reported that ADC textural parameter LRHGE correlated with the differentiation of cervical cancer 22 . Ho et al. applied PET texture analysis to cervical cancer prognosis and found RLN as one of good predictors of post-CCRT complete metabolic response with an AUC of 0.75 15 . Our study had several important differences compared with the existing literature. Most previous studies applying texture analysis to tumor prognosis only focused on one or two timepoints 15,21,23 . In the current work, intratumoral heterogeneity depicted by texture parameters was evaluated at four different timepoints including baseline, 2nd week and 4th week during therapy and after the completion of treatment. Hence, we can not only explore the temporal behaviors of tumor heterogeneity, but also find which timepoint was optimal for predicting recurrence in advanced cervical cancer treated with CCRT. Our study found timepoint 3 was the best timepoint for two main reasons: firstly, at timepoint 3, the number of texture parameters that could differentiate different groups were the most, and the regression models at timepoint 3 show relatively high AUCs for predicting recurrence. Secondly, pre-treatment texture parameters at timepoint 1 can only represent the inherent heterogeneity characteristics of the tumor, while mid-treatment texture parameters also reflect tumor microenvironment change information caused by anti-cancer treatment. At timepoint 3, the tumor microenvironment changes are greater than that at timepoint 2, thus may better predict tumor response to the treatment and long-term prognosis.   During CCRT, textural parameters 5 Percentile, RLN, contrast-NIDM and LRHGE showed ascending temporal trends while range, correlation-GLCM25 and energy showed descending temporal trends. These changing trends indicated that tumor heterogeneity reduced after treatment. However, no obvious difference between the general variety trends of the recurrence and nonrecurrence groups was observed in this study. Logistic regression analysis selected the most discriminatory two features textural parameters, namely RLN derived from T2WI and energy derived from ADC maps at timepoint 3. The SVM classification results also indicated timepoint 3 as a good timepoint for early predicting recurrence. However, some previous studies demonstrated that baseline MR textual parameters could have potential in predicting treatment response in breast cancer 20 , rectal cancer 21 and glioblastoma 24 . Although we found several baseline T2 textural parameters including 5 Percentile, range and RLN in the recurrence group were significantly lower than those in the nonrecurrence group, none of baseline textural parameters was selected in the regression model. The possible explanation may be that pre-treatment MRI data can only reflect inherent intratumoral heterogeneity information, while post-treatment MRI data represent the current status of the tumor after chemoradiotherapy.
The prognostic model in our study combined both T2WI and DWI data of patients with advanced cervical cancer. This combination method has been used in the diagnosis and grading of prostate cancer 19,25 as well as the prognosis of rectal cancer 26 , but so far it has not been reported for assessing cervical cancer. We found combination of T2 textural parameter RLN and ADC textural parameter energy yield a little higher AUC (0.885) than either of them alone (AUC = 0.787, 0.775, respectively). And the SVM classification showed that combining T2 textural parameters may add little value in prognosis. We speculated that morphological features from T2WI reflect only limited information about residual tumor posttreatment, DWI may provide more valuable details regarding the response to CCRT in advanced cervical cancer. A recent study by Liu et al. also confirmed this. They constructed a radiomics signature for pCR assessment after chemoradiotherapy in rectal cancer, and found that only 1 T2WI feature was selected in the model while the others were all ADC features 26 .
Another advantage of our study was that our measurement for heterogeneity on T2WI and ADC maps was done at all slices covering the whole tumor. The whole-tumor texture analysis is more representative of tumor heterogeneity than some previous studies using the single largest cross-sectional area analysis 23 . What's more, to strengthen our study, the MR imaging technique was standardized and uniform across the study population.
Our study also had some limitations. Firstly, the number of patients in this preliminary study was still limited. As a result, when performing SVM classification, the training and testing were performed on the same set of patient data. To minimize the bias, the patient data were stratified sampled with 70% of them used for training while the remaining 30% of them used for testing purpose. All the tests were run 100 times with the average value reported as the cross-validated performance. Study with larger sample size as well as an external validation are required to confirm the prognostic performance of these textural parameters. Secondly, we visually showed changing trends of some representative textural parameters, but did not investigate their change rates, which may also be related to prognosis. Studies on correlation between textural parameters change rates and tumor recurrence is needful in the future. Thirdly, the follow-up was not long enough to assess the predictive value on patient survival. A longitudinal study is needed to further understand the long-term prognostic value of MR texture analysis in cervical cancer.

Conclusion
Our study suggests the potential of T2 and ADC textural parameters as non-invasive imaging biomarkers in early predicting recurrence in advanced cervical cancer treated with CCRT, which may provide an opportunity for clinicians to adjust therapeutic strategies in time to develop a more individualized anti-cancer treatment.

Materials and Methods
Patient cohort. This study was approved by the ethics committee of the Institutional Review Board of Nanjing Drum Tower Hospital, and written informed consent was obtained from all patients. The methods were carried out in accordance with the relevant guidelines and regulations. We prospectively enrolled 34 consecutive patients between October, 2013 and August, 2016. The inclusion criteria were as follows: (i) histologically confirmed cervical cancer, (ii) locally advanced tumor stages IB2 to IVA according to the International Federation of Gynecology and Obstetrics (FIGO) classification, (iii) undergoing CCRT in our institution and no treatment performed before, (iv) complete acquisition of MR examination for 4 times in the same 3.0-T MR scanner, (v) minimum follow-up period of 15 months after CCRT in patients without recurrence. Patients who quit or suspended therapy (n = 3) or insufficient image quality (n = 2) were considered not eligible for the study. Flowchart of the study population was shown in Fig. 4. Patient characteristics are detailed in Table 3.
All the patients were scheduled to undergo 5-weeks external beam radiation therapy (EBRT) followed by 3-weeks intracavitary brachytherapy (ICBT). EBRT was delivered to the whole pelvis at 1.8-2.0 Gy daily, 5 days a week, with a total dose of 45-50 Gy. From the last week of EBRT, ICBT was given to point A (2 cm above the distal end of the lowest cervix and 2 cm lateral to the midline) at a fraction dose of 5 Gy, twice a week, with a total dose of 30-40 Gy. The total radiation time was within 8 weeks. Six cycles of weekly nedaplatin or four cycles of bi-weekly nedaplatin plus paclitaxel/docetaxel was given concomitantly. Adjustment of the therapeutic regimens was varied according to the health condition of individual patient.
Clinical follow-up. Patients were evaluated posttherapy 1 month, 3 months and afterwards every 6 months until recurrence or last contact. Recurrence was defined as presence of histologically proven recurrence or progression of the primary tumor in the cervix, uterus or pelvis. Death from cervical cancer was also classified as recurrence group. Median follow-up of nonrecurrence patients were 31 months (range, 16-43 months). Radiomic pipeline. The entire radiomic feature extraction was performed using the Imaging Biomarker Explorer (IBEX) software 27 . Pre-, mid-and post-treatment MRIs were analyzed by 2 radiologists (J.H. and Z.Z.), with 7 and 9 years' experience in gynecological imaging, respectively), both were blinded to the results of patients' outcomes. The regions of interest (ROIs) were drawn manually using the T2WI and DWI images on each slice covering the whole tumor. ROIs were placed on the slightly high signal intensity region on T2WI images and the high signal intensity region on DWI (b-value of 800 s/mm 2 ) images and then copied to ADC maps. If no tumor signals were noted on post-CCRT images, then the ROIs were placed on the latest former tumor region. Next, 7 categories of different texture feature sets were extracted from the pre-, mid-and post-treatment T2WI and ADC data with manually delineated ROIs:

Feature selection methods.
Before establishing a prognostic model, feature filter is required mainly for three reasons: reducing the model's training time, improving the robustness of the model and enhancing the model's reliability and behavior. The chosen parameters should be reproducible, show high degree of differentiation and low redundancy. To analyze the reproducibility, the parameters were repeatedly measured at an interval of 6 weeks using pretreatment T2WI images. The concordance correlation coefficient (CCC) were used to evaluate the consistency of texture parameters extracted from two different measurements. We found the vast majority of features can meet high enough reproducibility with CCC value not lower than 0.9. A metric named dynamic range (DR) not lower than 0.9 implied that the feature had a large dynamic range 32 . Texture parameters with a CCC value ≥ 0.9 and a DR value ≥ 0.9 were extracted. Redundancy was assessed by computing interfeature correlation coefficient using R package corrgram. The features were grouped on the basis of the Pearson correlation coefficient between them, we chose 0.8 as cutoff value for the Pearson correlation coefficient. In this subset, one representative that had the highest DR was picked. Using the above methods, 20 texture parameters were selected for further processing of the study (see details in Table 4). . The support vector machine (SVM) classifier was used for supervised learning on T2, ADC and T2 + ADC textural parameters, respectively. The stratified k-fold cross-validation (CV) was then used as the internal validation to evaluate the accuracy, sensitivity and specificity of the classification. The stratified approach was chosen in order to ensure that both the recurrence and nonrecurrence types were represented in the validation folds. This process was repeated 100 times to include all the possible ways of obtaining such a partition in our dataset, and the results were then averaged.

Statistical analyses.
Data availability. The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.