Diffusion Kurtosis Imaging as a Prognostic Marker in Osteosarcoma Patients with Preoperative Chemotherapy

Abstract

Background. The accurate prediction of prognosis is key to prompt therapy adjustment. The purpose of our study was to investigate the efficacy of diffusion kurtosis imaging (DKI) in predicting progression-free survival (PFS) and overall survival (OS) in osteosarcoma patients with preoperative chemotherapy. Methods. Thirty patients who underwent DKI before and after chemotherapy, followed by tumor resection, were retrospectively enrolled. The patients were grouped into good responders (GRs) and poor responders (PRs). The Kaplan-Meier and log-rank test were used for survival analysis. The association between the DKI parameters and OS and PFS was performed by univariate and multivariate Cox proportional hazards models. Results. Significantly worse OS and PFS were associated with a lower mean diffusivity (MD) after chemotherapy (HR, 5.8; 95% CI, 1.5-23.1; $P=0.012$ and HR, 3.5; 95% CI, 1.2-10.1: $P=0.028$, respectively) and a higher mean kurtosis (MK) after chemotherapy (HR, 0.3; 95% CI, 0.1-0.9; $P=0.041$ and HR, 0.3; 95% CI, 0.1-0.8; $P=0.049$, respectively). Likewise, shorter OS and PFS were also significantly associated with a change rate in MD (CR MD) of less than 13.53% (HR, 8.6; 95% CI, 1.8-41.8; $P=0.007$ and HR, 2.9; 95% CI, 1.0-8.2; $P=0.045$, respectively). Compared to GRs, PRs had an approximately 9- and 4-fold increased risk of death (HR, 9.4; 95% CI, 1.2-75; $P=0.034$) and progression (HR, 4.2; 95% CI, 1.2-15; $P=0.026$), respectively. Conclusions. DKI has a potential to be a prognostic tool in osteosarcoma. Low MK and high MD after chemotherapy or high CR MD indicates favorite outcome, while prospective studies with large sample sizes are warranted.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Introduction

Osteosarcoma is one of the most common primary malignant bone tumors in children and adolescents, and approximately 75-80% of osteosarcoma involves appendicular bone [1]. The importance of preoperative neoadjuvant chemotherapy in the treatment of osteosarcoma has been confirmed, and effective chemotherapy has dramatically improved patient survival rates, contributing to a 5-year survival rate increase from 20% to 70% [2]. Currently, increasing evidence has shown that chemotherapy-induced tumor necrosis is the strongest known predictive indicator for survival [3, 4]. In clinical practice, the early identification of chemotherapy response is key to prompting treatment regimen adjustments, as ineffective chemotherapy has the potential to increase the risk of complications and mortality or form resistant clones [5]. However, the crucial issue is the lack of imaging features for monitoring chemotherapy response in vivo. Suboptimal histologic response to neoadjuvant chemotherapy can be assessed only from the postoperative specimen after the completion of neoadjuvant chemotherapy. Therefore, there is an urgent need to discover imaging surrogates that are reliable preoperative prognostic indictors.

Currently, several imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and PET/CT, play a crucial role in monitoring the neoadjuvant chemotherapy response of osteosarcoma [6, 7]. Conventional diffusion-weighted imaging (DWI), a type of functional MR imaging, has been used to reflect chemotherapy-induced tumor necrosis. A few studies have demonstrated that apparent diffusion coefficient (ADC) values were significantly associated with tumor necrosis after surgery [8–16]. However, this imaging modality is based on the assumption of a Gaussian distribution of water diffusion in tissue. In reality, water diffusion deviates from this pattern due to the presence of intracellular and extracellular complexes in the tissue microstructure. Thus, DWI may result in an inaccurate reflection of tissue microstructure heterogeneity and complexity.

In contrast to DWI, diffusion kurtosis imaging (DKI) provides a method which might reflect more precisely the diffusional heterogeneity of the tumor by quantifying the non-Gaussian diffusion of water molecules in bone tumors. To date, few studies have evaluated chemotherapeutic response using DKI [17, 18]. Recently, DKI has been shown to have the potential to predict survival outcomes in high-grade glioma patients [19]. However, to the best of our knowledge, there is little information regarding whether DKI has the ability to evaluate prognosis in osteosarcoma patients with preoperative chemotherapy. Therefore, our purpose was to investigate the utilization of DKI in prediction of the survival outcome in high-grade osteosarcoma patients.

2. Materials and Methods

2.1. Study Subjects

Our retrospective study was approved by the Ethics Committee of Shanghai Sixth Hospital, and written informed consent was obtained from each patient. Thirty-three patients were recruited between March 2016 and December 2016. The patients were eligible for inclusion with the following criteria, if they (1) had pathologically proven osteosarcoma and received preoperative chemotherapy followed by surgery, (2) had complete and interpreted data in the MRI database including routine MRI and DKI before and after chemotherapy, (3) had a time interval between biopsy and first MR imaging of more than 7 days and between preoperative MRI and surgery within 2 weeks, (4) had not underwent other preoperative therapy simultaneously, and (5) had available contact information during follow-up. Three patients, for whom DKI had serious motion artifacts, were excluded. Finally, 30 subjects were enrolled, and the detailed clinical information is summarized in Table 1.

Table 1

Clinical characteristics of the study patients.

Variable	$n$(%)
Age, mean (range)	17.5 (7-34)
Sex
Male	21 (70.0)
Female	9 (30.0)
Tumor location
Distal femur	21 (70.0)
Others	9 (30.0)
AJCC stage
IIA	4 (13.3)
IIB	22 (73.4)
III	4 (13.3)
Surgical approach
Limb salvage	28 (93.3)
Amputation	2 (6.7)
Pathological subtype
Osteoblastic	27 (90.0)
Others	3 (7.0)
Huvos grade
I and II	17 (56.7)
III and IV	13 (43.3)
Progression rate
Free	15 (50.0)
Positive	15 (50.0)

AJCC: American Joint Committee on Cancer.

2.2. MR Imaging Acquisition

The preoperative MR imaging of all patients was performed using a 3.0T MR scanner (MAGETOM, Verio, Siemens Healthcare, Erlangen, Germany), and an eight-channel body array coil was used for signal reception.

DKI was performed using a single shot echo planar imaging sequence with fat suppression. The scan protocol was as follows: axial plane with six $b$ values (0, 500, 1000, 1500, 2000, and 2500 s/mm², respectively); $\text{TR}=3400\text{ms}$; $\text{TE}=72\text{ms}$; $\text{field}\text{of}\text{view}\text{FOV}=380\times 380\text{mm}$; $\text{matrix}=384\times 384$; $\text{slice}\text{thickness}=5\text{mm}$; $\text{flip}\text{angle}={180}^{°}$; $\text{voxel}\text{size}=2.0\times 2.0\times 5\text{m}{\text{m}}^3$; and $\text{scan}\text{duration}=4\min \text{and}37\text{s}$. The diffusion gradient of DKI was encoded in three orthogonal directions.

The following scan protocols for routine and contrast-enhanced sequences were as follows: $\text{TR}=4000\text{ms}$; $\text{TE}=104\text{ms}$; $\text{FOV}=380\times 380\text{mm}$; $\text{matrix}=384\times 384$; and $\text{slice}\text{thickness}=5\text{mm}$ for coronal, respectively; sagittal or transverse fat-saturated T2-weighted sequences; and $\text{TR}=600\text{ms}$; $\text{TE}=20\text{ms}$; $\text{FOV}=380\times 380\text{mm}$; $\text{matrix}=384\times 384$; and $\text{slice}\text{thickness}=5\text{mm}\text{for}\text{coronal}$, respectively; sagittal or transverse fat-saturated T1-weighted sequences. Fat-saturated T1-weighted contrast-enhanced MRI was performed after injection of 0.1 mmol/kg Gd-DTPA into the cubital vein. The scan protocol was the same as those described above.

2.3. MR Image Interpretation

Based on the DKI data, diffusivity and kurtosis maps were automatically generated according to the postprocessing software “Body Diffusion Toolbox” (MathWorks, Natick, MA) [20]. The postprocessing procedure for DKI data was previously described in more details [21]. In brief, a Gaussian filter was first used in the software with a full width at half maximum of 3 mm to increase the signal-noise ratio. Then, a voxel-by-voxel fitting of DKI data was performed according to DKI nonlinear equation [22]. The equation is described as follows: $S=S_0·\exp -bD+1/6b^2{D}^{2}K$, where $S$ refers to DWI signal at a particular $b$ value, $S_0$ is the baseline signal without diffusion weighting, $D$ is diffusivity, and $K$ is kurtosis. Afterwards, two experienced radiologists who were blinded to the tumor necrosis manually drew the regions of interest (ROIs) on the $b_0$ image and avoided tumor necrotic, cystic, and hemorrhagic areas. Moreover, we used the T2W and enhanced T1W images as a reference [10]. The ROIs were automatically matched on the diffusivity map and kurtosis map. Subsequently, the values of MD and MK for the ROIs were calculated by the software (Figure 1). We defined the change rates in MK and MD before and after neoadjuvant chemotherapy according to the following formulas [14]: $\text{CR}\text{MK}=\text{pre}\text{MK}-\text{post}\text{MK}/\text{pre}\text{MK}\ast 100\%\text{and}\text{CR}\text{MD}=\text{pre}\text{MD}-\text{post}\text{MD}/\text{pre}\text{MD}\ast 100\%$.

[figure omitted; refer to PDF]

However, there were any such significant associations were found between DKI and OS and PFS among the patients with GRs (Table 4, Figure 3).

4. Discussion

In the present study, our findings suggested that post MD, post MK, and CR MD were associated with PFS and OS, which suggested that DKI has potential as a prognostic tool in patients with osteosarcoma. Patients with lower post MK and higher post MD or higher CR MD have an improved outcome.

Histological response induced by neoadjuvant chemotherapy is considered the most reliable prognostic factor for the survival of patients with osteosarcoma [7]. Until now, few studies have investigated the usefulness of diffusion-weighted imaging (DWI) in monitoring the tumor response to chemotherapy in osteosarcoma patients. Thus, there was still no agreement regarding the evaluation of tumor necrosis using DWI-related parameters. Several previous studies showed increased ADC values in response to chemotherapy, and the average ADC change rate before and after chemotherapy could distinguish tumor necrosis based on small sample sizes [14, 24]. However, Oka et al. and Bajpai et al. suggested that the ADC value and change rate in ADC were not associated with tumor necrosis [12, 13]. The wide range of ADC values might be attributed to the heterogeneity of tumor tissue-induced chemotherapy.

Osteosarcoma, especially after neoadjuvant chemotherapy, is characterized by a complex microstructure and heterogeneity. Chemotherapy-induced mitochondrial or organelle swelling, increase in the nuclear-cytoplasmic ratio, or formation of new colonies results in compartmentalization and restricts the free displacement of water molecules, which is attributed to the non-Gaussian diffusion of water molecules [25]. In contrast to DWI, DKI has the potential to illustrate non-Gaussian water diffusion behavior and more accurately reflect and quantify tumor microenvironment complexity [26]. The DKI-related MK and MD are defined as the mean kurtosis and the average diffusivity of all diffusion gradient directions, respectively. Our results suggested that post MD, post MK, and CR MD were predictors of PFS and OS.

Unfortunately, DKI-related parameters before chemotherapy were not associated with PFS or OS, which is in contrast to the results of Wang et al., who suggested that preoperative MK was significantly associated with clinical outcome in patients with high-grade gliomas [19]. This result may be attributed to the intrinsic nature of osteosarcoma. Osteosarcoma may exhibit a faster signal decay than other tissues when $b$ factors increase [27]. Moreover, for reducing patient’s discomfort and imaging motion artifact, we applied the body DKI model, which required acquisition of only 3 DWIs along the main ($x$, $y$, $z$) orthogonal directions. Although this body DKI model has been used in various types of cancer, it could not reflect the full kurtosis tensor in the tissue, which may affect our result. Thus, further verification was needed in the advanced DKI model. In additional, in this single-center study, although an identical scanner, imaging protocol, and receiver coil were used for a given patient, we did not carry out the correction for nonlinearity of diffusion gradient for DKI, which may potentially affect accuracy of DKI-related quantitative measurement, especially in longitudinal study [28, 29]. In the future, we will employ the DWI quality assurance protocol that Fedeli et al. recommended to further improve DKI accuracy and standardization [30].

Likewise, in contrast to CR MD, CR MK was not significantly associated with OS or PFS. The possible reasons were as follows. On the one hand, this finding may be attributed to the limited sample size. On the other hand, the MD value was decreased, whereas the MK value was not increased in some tumor viscous areas. Furthermore, the area of tumor necrosis with extensive interstitial fibrosis, inflammatory infiltration, and granuloma formation was found in patients who were good responders, which increased the heterogeneity of residual tumors. Last but not least, in this study, we estimated DKI-derived index by fitting DKI model to trace-weighted (TW) images. Although this empirical and straightforward method has been employed in several previous extracranial DKI studies, it could introduce bias and error in the estimation of DKI-derived indices. Accordingly, Giannelli et al. [31] have shown that, for kurtosis values of about 1 (as typically observed in human tissue) and low diffusion anisotropy (<0.2), the absolute percentage error in $K$ can range up to 35% or more. Recently, Marzi et al. [32] have found that, in head and neck cancer, the fit of the DKI model to TW images can introduce nonnegligible bias and error in the estimation of $K$ and $D$ for single lesion. In particular, the median (95% confidence interval) errors in $K$ and $D$ were 5.1% (0.8%, 32.6%) and 1.7% (-2.5%, 5.3%), respectively. However, it is not clear how TW images affect DKI-derived indices in the estimation of $D$ and $K$ in the osteosarcoma. In the future, we will further investigate this potential bias. These above findings may partially explain our present results.

Our findings suggested that patients with lower post MK and higher post MD had longer PFS and OS, and this finding was also found when we evaluated the effect of these two combined parameters on survival, with highly increased risk of OS or PFS for patients in group 2 or group 3 compared with those in group 1, respectively. Increased MK indicates a higher degree of complexity of the microstructure within the tumor, which can represent tumors with higher cellular pleomorphism and more microvascular proliferation [19]. However, reduced MD reflects a higher degree of proliferation and cellularity. In good responders, reduced post MK and elevated post MD may result from alterations in cell and intracellular membrane integrity and permeability to water, cell death, or a decrease in cellularity after chemotherapy, which change the degree of restricted diffusion and heterogeneity of tumor tissue [11]. Prior studies confirmed that ¹⁸F–FDG PET could accurately reflect the response of osteosarcoma to chemotherapy and outcome [3, 10]. Costelloe et al. suggested that survival was associated with SUV_max after chemotherapy [3]. Compared to PET, DKI not only has a higher resolution but also is free from ionizing radiation, which may be well suited for dynamically evaluating osteosarcoma.

Previous studies showed that the response to chemotherapy influences on survival [33]. In our study, a weak association was observed between post MD and CR MD and OS and between post MD and PFS among the patients with PRs, while no any significant associations were found between DKI and survival among patients with GRs. Such very preliminary findings may help generate some novel hypotheses for further investigation in future larger studies.

Several potential limitations were also present in this study. Firstly, the single-center, retrospective design and small sample size may result in biased conclusions. Further validation in larger sample sizes is needed. Secondly, DKI data acquisition requires a long time, which causes patent discomfort and increases the risk of motion artifacts. Third, the design including the $b_0$ value for DKI may increase the intravoxel incoherent motion effects. Fourth, DKI was performed by using only 3 orthogonal diffusion weighting directions. This straightforward approach does not allow to estimate the diffusion and kurtosis tensors, which are needed to adequately characterize diffusion processes and obtain rotational invariant estimates of $D$ and $K$. Fifth, the use of trace-weighted images can introduce both bias and error in the estimation of DKI-derived indices of $K$ and $D$. Lastly, the time course of change in DKI parameters during chemotherapy needs further exploration in the future.

In summary, DKI is a promising prognostic tool for OS and PFS in patients with osteosarcoma. Post MD, post MK, and CR MD might serve as potential invasive surrogate prognostic markers and could contribute to regimen management of individualized treatment for better survival and improved quality of life for this rare disease.

Disclosure

Chenglei Liu and Dongmin Wei are co-first authors.

Authors’ Contributions

Chenglei Liu and Dongmin Wei contributed equally to this work.

Acknowledgments

This study was sponsored by the National Natural Science Foundation of China (No. 81771790) and the Shanghai Scientific Research Plan Project (No. 16511101101).

Glossary

Abbreviations

DKI:Diffusion kurtosis imaging

PFS:Progression-free survival

OS:Overall survival

GRs:Good responders

PRs:Poor responders

MK:Mean kurtosis

MD:Mean diffusivity

CR MD:Change rate in mean diffusivity before and after chemotherapy

CR MK:Change rate in mean kurtosis before and after chemotherapy

DWI:Diffusion-weighted imaging

ADC:Apparent diffusion coefficient

AJCC:American Joint Committee on Cancer.

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