Skip to main content

Application of first-order feature analysis of DWI-ADC in rare malignant mesenchymal tumours of the maxillofacial region



To research the first-order features of apparent diffusion coefficient (ADC) values on diffusion-weighted magnetic resonance imaging (DWI) in maxillofacial malignant mesenchymal tumours.


The clinical data of 12 patients with rare malignant mesenchymal tumours of the maxillofacial region (6 cases of sarcoma and 6 cases of lymphoma) treated in the hospital from May 2018 to June 2020 and were confirmed by postoperative pathology were retrospectively analyzed. The patients were all examined by 1.5T magnetic resonance imaging. PyRadiomics were used to extract radiomics imaging first-order features. Group differences in quantitative variables were examined using independent-samples t-tests.


The voxels number of ADCmean and ADCmedian of sarcoma tissues were 44.9124 and 44.2064, respectively, significantly higher than those in lymphoma tissues (ADCmean (− 68.8379) and ADCmedian (− 74.0045)), the difference considered statistically significant, so do the ADCkurt and ADCskew.


The statistical difference of ADCmean and ADCmedian is significant, it is consistent with the outcome of the manual measurement of the ADC mean value of the most significant cross-section of twelve cases of lymphoma. Development of tumour volume based on the ADC parameter map of DWI demonstrates that the first-order ADC radiomics features analysis can provide new imaging markers for the differentiation of maxillofacial sarcoma and lymphoma. Therefore, first-order ADC features of ADCkurt combined ADCskew may improve the diagnosis level.

Peer Review reports


The rapid development of MRI in the maxillofacial region has improved the recognition and diagnosis of maxillofacial tumours significantly. The vast majority of malignant maxillofacial tumours originate from epithelial tissue, and mesenchymal tumours are rare. Therefore, the imaging manifestations of these diseases are not well understood, and the rate of clinical misdiagnosis is high. Different types of tumours have different clinical treatment methods. Sarcoma is generally entirely removed by surgery. Chemotherapy is the standard lymphoma treatment. Therefore, it is vital to determine the type of tumour before operating. DWI could provide essential biomarkers in several kinds of tumours [1, 2].

Radiomics is a new technology whereby algorithms automatically extract and transform a large amount of representative imaging data into exploitable feature spaces that reflect the microscopic characteristics of tumours [3, 4]. MRI could detect and locate the focus and monitor the disease progression that a biopsy cannot [5, 6]. Recently, radiomics has played an important role in the identification of imaging biomarkers and clinical management [7,8,9,10,11]. However, there are few reports on the application and related literature of maxillofacial mesenchymal tumours. In this paper, several rare cases of mesenchymal tumours were analysed retrospectively to provide more reliable clinical evidence for the differential diagnosis of sarcoma and lymphoma. First-order features radiomics analysis, as an emerging tool in MRI analysis [12, 13], could provide insight into tumour heterogeneity and was valuable for differentiating tumour type [14,15,16,17,18,19].

In this study, the first-order features of the volume of interests (VOI) were extracted from DWI-ADC parameters. These results, together with the mean ADC were analysed for characterisation of rare malignant mesenchymal tumours in the maxillofacial region.


Patients and MRI acquisition parameters


Twelve patients were retrospectively reviewed (seven men, five women; mean age was 54 [range 13–85] years) with a biopsy-proven malignant mesenchymal tumour. The sample patients had preoperative MRI scans between May 2018 and June 2020, and the pathological differentiation could be determined. The sample included six sarcomas and six lymphomas, including one low-grade central osteosarcoma of the left zygoma, three well-differentiated chondrosarcomas of the jaw, two leiomyosarcoma of neck and jaw, two well-differentiated diffuse large B-cell tumour of the buccal region, one B-cell lymphoma related marginal area of tongue mucosa and three non-Hodgkin's follicular lymphoma of the parotid gland. Only patients with sarcomas showed varying degrees of pain and limited facet joint movement, which had no specific clinical symptoms.

The inclusion criteria: biopsy-proven malignant mesenchymal tumour without the concomitant disease. The exclusion criteria were as follows: without definitive post-operative information on pathological characteristics, a minimum tumour diameter < 5 mm, poor MRI quality.

This study was conducted in accordance with the Helsinki Declaration of 1975, as revised in 2013 and approved by the ethics committee of China-Japan Union Hospital of Jilin University in May 2018. All patients signed an informed consent form for inclusion in the study.

MRI acquisition parameters

1.5-T Siemens Avanto with an eight-channel phased-array neck coil was used in this study. The patient's head was secured. Non-contrast axial, sagittal and coronal FS-T2WI sequences acquired in multiple breath-holds were obtained by the following parameters: a repetition/echo time of 5080/87 ms, a slice thickness/interslice gap of 4.0/0.4 mm, 20 slices and a matrix of 256 × 320. Axial T1-weighted images were also acquired in multiple breath-holds. Diffusion-weighted images were obtained in the coronal plane. Following the image acquisition, a pixel-wise ADC map was generated by the inbuilt software using b values of 800 s/mm2. All patients received a 15-ml intravenous bolus injection of gadodiamide (GE Healthcare Ireland Limited, County Cork, Republic of Ireland). The contrast imaging was performed using a fat-suppressed three-dimensional (3D) T1-weighted volumetric interpolated breath-hold examination sequence after the injection.

The shape, size, signal, bone destruction, adjacent tissue relationship on MRI were evaluated. Besides, the ADC map was generated based on DWI, and the sampling was selected to measure the ADC value at the maximum level of the lesion. The lesions were resected surgically in all eight patients. Histopathological and immunohistochemical staining (IHC) was performed postoperatively.

MRI and radiomics analysis

Dr Wise Multimodal Research Platform was used for radiomics analysis. An open-source python package called PyRadiomics (2.2.0) was used for extraction of features. The platform supports feature extraction used to calculate single values per feature for a region of interest (ROI) (‘segment-based’) or generate feature maps (‘voxel-based’) (Fig. 1).

Fig. 1

First-order ADC radiomics analysis scheme used in this study

Delineation of tumour ROI

The tumour regions in the primary dataset were labeled manually by two experts. In the case of disagreement, a third opinion was requested. The DWI-ADC parameter diagram scan was selected as the labeling image, then tumour tissue was classified.

Extracting features from MRI scans

A B-spline interpolation resampling was used and the anisotropic voxels were resampled to form isotropic voxels of 2.0 mm × 2.0 mm × 2.0 mm. The MRI images were then normalised by centring it at the mean with standard deviation.

$$f(x) = \frac{s(x - \mu x)}{{\sigma x}}$$

(s = 100; μχ represents mean value; σ represents standard deviation).

Eighteen first-order features were obtained from the original images based on the pixel value extracted from each ROI: Energy, Total Energy, Mean Absolute Deviation, Robust Mean Absolute Deviation, Entropy, 10Percentile, 90Percentile, Minimum, Maximum, Mean, Median, Interquartile Range, Range, Root Mean Squared, Skewness, Kurtosis, Variance and Uniformity.

Statistical analysis

The research data was normally distributed with homogeneity of variance. All data analyses were carried out using SPSS 16.0 (IBM Corp., Armonk, NY, USA). Group differences in quantitative variables were compared using student’s t-test. A P-value < 0.05 was considered statistically significant.


Imaging and radiomics features

The general characteristics of the twelve study participants were listed in Table 1. The sarcomas showed slightly higher signal intensity with b = 800 s/mm2, while ADC images showed a slightly lower signal intensity on DWI images. The mean ADC for all patients with sarcoma was approximately 1.40 × 10−3mm2/s, while the mean ADC for patients with lymphoma was approximately 0.49 × 10−3mm2/s. A total of 18 first-order features were obtained. ADC first-order radiomics features were as follows: The mean voxels number of ADCkurt and ADCskew values of sarcomas were 4.1834 and 0.4956 respectively, the values of lymphomas were 9.6219 and 1.8514 respectively. The ADCkurt and ADCskew differed significantly between sarcoma and lymphoma (P < 0.05). The voxels number of ADCmean and ADCmedian of sarcomas were 44.9124 and 44.2064 respectively, which is significantly higher than that of lymphoma (ADCmean = − 68.8379 and ADCmedian = − 74.0045), the difference was statistically significant, shown in Table 2.

Table 1 The clinical and MRI characteristics of patients (n = 12)
Table 2 Results of the two-sample t-test (ADCkurt, ADCskew, ADCmean and ADCmedian)

Figure 2a shows a low-grade central osteosarcoma of left zygoma with slightly higher ADC value; Fig. 2b shows a non-Hodgkin's follicular lymphoma of the right parotid gland with low value. Figure 2c shows that lymphoma correlates with an ADCmean in the lower range, positive skew (i.e. high skewness) and a steep curve (i.e. high kurtosis). Sarcoma correlates with a higher ADCmean value, a negative skew (i.e. low skewness), and a flatter shape (i.e. low kurtosis).

Fig. 2

DWI-ADC images of a scarcoma and b lymphoma. a A 28 years old female patient with a sarcoma within the left zygoma, DWI-ADC image the lesion appears relatively inhomogeneous slightly higher signal compared to the adjacent muscle. ADC value is about 1.12 mm2/s; b a 67 years old male patient with a lymphoma within the right parotid gland, DWI-ADC image the lesion appears relatively homogeneous lower signal compared to the adjacent muscle. ADC value is about 0.54 mm2/s; c lymphoma correlating with an ADCmean in the lower range, positive skew (high skewness), and a steep curve (high kurtosis). Sarcoma correlating with a higher ADCmean value, a negative skew (low skewness), and a flatter shape (low kurtosis)


In this study, the ADCkurt, and ADCskew were significant predictive factors. The ADCmean and ADCmedian were significantly different with statistical significance, the result is consistent with the mean of ADC value. The average ADC value of sarcomas is approximately 1.40 × 10–3 mm2/s, and the average ADC value of lymphomas is approximately 0.49 × 10–3 mm2/s. Maeda et al. [20] and Wang et al. [21] found that the average ADC value of lymphoma was lower than head and neck malignant tumours. Furthermore, Sumi et al. [22] reported that an ADC smaller than 0.560 × 10−3 mm2/s could differentiate pharyngeal lymphomas from pharyngeal carcinomas. These results are consistent with our study. Also, the mean voxels number of ADCmean and ADCmedian of sarcomas were 44.9124 and 44.2064, respectively, which is significantly higher than that of lymphoma (ADCmean = − 68.8379 and ADCmedian = − 74.0045), the difference was statistically significant. These results are consistent with the consequences of manual measurement of the maximum cross-section ADC of twelve cases. However, the previous study is limited to the review of ADC; there is no distinction between single lymphoma and sarcoma. The cases in this study are rare, which may enrich the clinical data and further improve the understanding of the radiomics features of mesenchymal tumours.

This study found that ADCkurt and ADCskew differed significantly between sarcoma and lymphoma. Lymphoma correlated with an ADCmean in the lower range. Sarcoma correlated with a higher ADCmean value. This study demonstrates that first-order ADC radiomics analysis, used with the average ADC value to improve diagnostic accuracy, could provide new imaging markers for the differentiation of maxillofacial sarcoma and lymphoma. However, more cases need to be studied. Furthermore, there is theoretical support for studying the difference between the first-order characteristics of sarcoma and lymphoma. Besides, Lisson et al. [23] identified several first-order parameters in order to differentiate enchondroma and low-grade osteosarcoma. Meyer et al. [24] reported that texture analysis parameters derived from MRIs could reflect the Ki67 index in soft tissue sarcoma. So, radiomics analysis can reflect microstructure differences between these tumour entities.

DWI has been used in head and neck tumor imaging and is considered as a sensitive marker for monitoring treatment response in head and neck cancer [25]. While many researchers focused on the interpretation of conventional CT and MRI, the MRI's imaging extraction feature presents an intriguing way to differentiate sarcoma and lymphoma [26, 27]. Suo et al. [28] reported that ADCskew, ADCkurt and ADCmean differed between benign bladder lesions and bladder carcinoma. Wang et al. [29] applied first-order ADC texture analysis in order to differentiate lymphoma from metastatic nodes in the head and neck region.

DWI could detect non-invasively the diffusion of water molecules in living tissues [30]. The characteristics of the dense distribution of lymphoma cells and small extracellular space can decrease the ADC value [22]. However, cystic degeneration and necrosis are common in sarcomas, and ADC value is slightly higher [31]. Although ADC value is helpful for clinical identification of maxillofacial lymphoma and other diseases, the rate of clinical misdiagnosis is high because of a low incidence rate and typical clinical manifestations. Therefore, more diagnostic criteria are needed, thus increasing the accuracy of clinical diagnosis. Conventional MRI examinations mainly reflect the shape, composition and water molecule diffusion of a tumour. Texture analysis can extract and quantify the grey level, roughness and homogeneity of lesions that cannot be distinguished by the naked eye [32]. That allows the practitioner to reflect on the characteristics of lesions more comprehensively and carefully [33]. Recent studies found that histogram-based parameters reflected the different histopathological features in several tumour entities [13, 34]. This relationship maybe help to better characterise tumours through radiological imaging and aid in differentiating between tumour types [35].

Based on the number of cases and features selected in this paper, 18 kinds of first-order features of lesions were extracted for analysis. First-order statistics describe the distribution of voxel intensities. Skewness measures the asymmetry of the distribution of values. A higher kurtosis reflected that distribution mass is concentrated towards the tail(s) rather than towards the mean. Lower kurtosis implies the reverse. Fine texture usually appears in healthy tissue, while rough texture highlights the heterogeneity of the tumour. In this study, the first-order feature analysis of rare cases showed that sarcoma and lymphoma were different in malignant mesenchymal tumours of the maxillofacial region. Therefore, radiomics analysis can provide quantitative parameters in the tumour ROI.


First, the number of patients was limited. Analysing a more significant number of patients’ textural parameters may have shown vital information related to tumour characteristics. Second, the differences relating to the imaging parameters and image viewer remain unknown. Future studies of this nature should include a more significant number of cases.


In conclusion, the development of tumour volume based on the ADC parameter map of DWI first-order ADC radiomics analysis makes it possible to provide new imaging markers for the differentiation of maxillofacial sarcoma and lymphoma. Primarily, the feature parameters ADCkurt and ADCskew, combined with the mean ADC value, effectively improves the diagnostic level. However, due to the low incidence rate and a limited number of cases, this study explored only first-order features that were closely related to malignant mesenchymal tumours. It can be concluded from the results of the study that tumour classification can be predicted based on radiomics features, and further study is recommended.

Availability of data and materials

All data generated or analyzed during this study are included in this published article.



Magnetic resonance imaging


Diffusion-weighted imaging


Apparent diffusion coefficient


Volume of interests


Region of interest




  1. 1.

    Surov A, Meyer HJ, Wienke A. Correlation between apparent diffusion coefficient (ADC) and cellularity is different in several tumors: a meta-analysis. Oncotarget. 2017;8(35):59492–9.

    Article  Google Scholar 

  2. 2.

    Ravanelli M, Grammatica A, Tononcelli E, Morello R, Leali M, Battocchio S, Agazzi GM, Buglione di Monale E, Bastia M, Maroldi R, Nicolai P, Farina D. Correlation between human papillomavirus status and quantitative MR imaging parameters including diffusion-weighted imaging and texture features in oropharyngeal carcinoma. AJNR Am J Neuroradiol. 2018;39(10):1878–83.

    Article  Google Scholar 

  3. 3.

    Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278(2):563–77.

    Article  Google Scholar 

  4. 4.

    Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ. CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics. 2017;37(5):1483–503.

    Article  Google Scholar 

  5. 5.

    Soufi M, Arimura H, Nakamoto T, Hirose TA, Ohga S, Umezu Y, Honda H, Sasaki T. Exploration of temporal stability and prognostic power of radiomic features based on electronic portal imaging device images. Phys Med. 2018;46:32–44.

    Article  Google Scholar 

  6. 6.

    Li Q, Kim J, Balagurunathan Y, Liu Y, Latifi K, Stringfield O, Garcia A, Moros EG, Dilling TJ, Schabath MB, Ye Z, Gillies RJ. Imaging features from pretreatment CT scans are associated with clinical outcomes in nonsmall-cell lung cancer patients treated with stereotactic body radiotherapy. Med Phys. 2017;44(8):4341–9.

    Article  Google Scholar 

  7. 7.

    Zhou W, Zhang L, Wang K, Chen S, Wang G, Liu Z, Liang C. Malignancy characterization of hepatocellular carcinomas based on texture analysis of contrast-enhanced MR images. J Magn Reson Imaging. 2017;45(5):1476–84.

    Article  Google Scholar 

  8. 8.

    Li Z, Mao Y, Huang W, Li H, Zhu J, Li W, Li B. Texture-based classification of different single liver lesion based on SPAIR T2W MRI images. BMC Med Imaging. 2017;17(1):42.

    Article  Google Scholar 

  9. 9.

    Kim AY, Kim YK, Lee MW, Park MJ, Hwang J, Lee MH, Lee JW. Detection of hepatocellular carcinoma in gadoxetic acid-enhanced MRI and diffusion-weighted MRI with respect to the severity of liver cirrhosis. Acta Radiol. 2012;53(8):830–8.

    Article  Google Scholar 

  10. 10.

    Makanyanga J, Ganeshan B, Rodriguez-Justo M, Bhatnagar G, Groves A, Halligan S, Miles K, Taylor SA. MRI texture analysis (MRTA) of T2-weighted images in Crohn’s disease may provide information on histological and MRI disease activity in patients undergoing ileal resection. Eur Radiol. 2017;27(2):589–97.

    Article  Google Scholar 

  11. 11.

    Li M, Fu S, Zhu Y, Liu Z, Chen S, Lu L, Liang C. Computed tomography texture analysis to facilitate therapeutic decision making in hepatocellular carcinoma. Oncotarget. 2016;7(11):13248–59.

    Article  Google Scholar 

  12. 12.

    Davnall F, Yip CS, Ljungqvist G, Selmi M, Ng F, Sanghera B, Ganeshan B, Miles KA, Cook GJ, Goh V. Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging. 2012;3(6):573–89.

    Article  Google Scholar 

  13. 13.

    Just N. Improving tumour heterogeneity MRI assessment with histograms. Br J Cancer. 2014;111(12):2205–13.

    Article  Google Scholar 

  14. 14.

    Downey K, Riches SF, Morgan VA, Giles SL, Attygalle AD, Ind TE, Barton DP, Shepherd JH, deSouza NM. Relationship between imaging biomarkers of stage I cervical cancer and poor-prognosis histologic features: quantitative histogram analysis of diffusion-weighted MR images. AJR Am J Roentgenol. 2013;200(2):314–20.

    Article  Google Scholar 

  15. 15.

    Woo S, Cho JY, Kim SY, Kim SH. Histogram analysis of apparent diffusion coefficient map of diffusion-weighted MRI in endometrial cancer: a preliminary correlation study with histological grade. Acta Radiol. 2014;55(10):1270–7.

    Article  Google Scholar 

  16. 16.

    Takahashi M, Kozawa E, Tanisaka M, Hasegawa K, Yasuda M, Sakai F. Utility of histogram analysis of apparent diffusion coefficient maps obtained using 3.0T MRI for distinguishing uterine carcinosarcoma from endometrial carcinoma. J Magn Reson Imaging. 2016;43(6):1301–7.

    Article  Google Scholar 

  17. 17.

    Hao Y, Pan C, Chen W, Li T, Zhu W, Qi J. Differentiation between malignant and benign thyroid nodules and stratification of papillary thyroid cancer with aggressive histological features: Whole-lesion diffusion-weighted imaging histogram analysis. J Magn Reson Imaging. 2016;44(6):1546–55.

    Article  Google Scholar 

  18. 18.

    Liang H-Y, Huang Y-Q, Yang Z-X, Zeng M-S, Rao S-X. Potential of MR histogram analyses for prediction of response to chemotherapy in patients with colorectal hepatic metastases. Eur Radiol. 2016;26(7):2009–18.

    Article  Google Scholar 

  19. 19.

    Kyriazi S, Collins DJ, Messiou C, Pennert K, Davidson RL, Giles SL, Kaye SB, Desouza NM. Metastatic ovarian and primary peritoneal cancer: assessing chemotherapy response with diffusion-weighted MR imaging-value of histogram analysis of apparent diffusion coefficients. Radiology. 2011;261(1):182–92.

    Article  Google Scholar 

  20. 20.

    Maeda M, Kato H, Sakuma H, Maier SE, Takeda K. Usefulness of the apparent diffusion coefficient in line scan diffusion-weighted imaging for distinguishing between squamous cell carcinomas and malignant lymphomas of the head and neck. AJNR Am J Neuroradiol. 2005;26(5):1186–92.

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    Wang J, Takashima S, Takayama F, Kawakami S, Saito A, Matsushita T, Momose M, Ishiyama T. Head and neck lesions: characterization with diffusion-weighted echo-planar MR imaging. Radiology. 2001;220(3):621–30.

    Article  Google Scholar 

  22. 22.

    Sumi M, Ichikawa Y, Nakamura T. Diagnostic ability of apparent diffusion coefficients for lymphomas and carcinomas in the pharynx. Eur Radiol. 2007;17(10):2631–7.

    Article  Google Scholar 

  23. 23.

    Lisson CS, Lisson CG, Flosdorf K, Mayer-Steinacker R, Schultheiss M, von Baer A, Barth TFE, Beer AJ, Baumhauer M, Meier R, Beer M, Schmidt SA. Diagnostic value of MRI-based 3D texture analysis for tissue characterisation and discrimination of low-grade chondrosarcoma from enchondroma: a pilot study. Eur Radiol. 2018;28(2):468–77.

    Article  Google Scholar 

  24. 24.

    Meyer HJ, Renatus K, Höhn AK, Hamerla G, Schopow N, Fakler J, Josten C, Surov A. Texture analysis parameters derived from T1-and T2-weighted magnetic resonance images can reflect Ki67 index in soft tissue sarcoma. Surg Oncol. 2019;30:92–7.

    Article  Google Scholar 

  25. 25.

    Kim S, Loevner L, Quon H, Sherman E, Weinstein G, Kilger A, Poptani H. Diffusion-weighted magnetic resonance imaging for predicting and detecting early response to chemoradiation therapy of squamous cell carcinomas of the head and neck. Clin Cancer Res. 2009;15(3):986–94.

    Article  Google Scholar 

  26. 26.

    Kierans AS, Doshi AM, Dunst D, Popiolek D, Blank SV, Rosenkrantz AB. Retrospective assessment of histogram-based diffusion metrics for differentiating benign and malignant endometrial lesions. J Comput Assist Tomogr. 2016;40(5):723–9.

    Article  Google Scholar 

  27. 27.

    Suo S, Zhang K, Cao M, Suo X, Hua J, Geng X, Chen J, Zhuang Z, Ji X, Lu Q, Wang H, Xu J. Characterization of breast masses as benign or malignant at 3.0T MRI with whole-lesion histogram analysis of the apparent diffusion coefficient. J Magn Reson Imaging. 2016;43(4):894–902.

    Article  Google Scholar 

  28. 28.

    Suo ST, Chen XX, Fan Y, Wu LM, Yao QY, Cao MQ, Liu Q, Xu JR. Histogram analysis of apparent diffusion coefficient at 3.0 T in urinary bladder lesions: correlation with pathologic findings. Acad Radiol. 2014;21(8):1027–34.

    Article  Google Scholar 

  29. 29.

    Wang YJ, Xu XQ, Hu H, Su GY, Shen J, Shi HB, Wu FY. Histogram analysis of apparent diffusion coefficient maps for the differentiation between lymphoma and metastatic lymph nodes of squamous cell carcinoma in head and neck region. Acta Radiol. 2018;59(6):672–80.

    Article  Google Scholar 

  30. 30.

    Inoue T, Kozawa E, Ishikawa M, Okada H. Application of magnetic resonance imaging in the evaluation of nutritional status: a literature review with focus on dialysis patients. Nutrients. 2021;13(6):2037.

    Article  Google Scholar 

  31. 31.

    Shen Y, Zhong Y, Wang H, et al. MR imaging features of benign retroperitoneal extra-adrenal paragangliomas. Sci Rep. 2017;7(1):4517.

    Article  Google Scholar 

  32. 32.

    Incoronato M, Aiello M, Infante T, Cavaliere C, Grimaldi AM, Mirabelli P, Monti S, Salvatore M. Radiogenomic analysis of oncological data: a technical survey. Int J Mol Sci. 2017;18(4):805.

    Article  Google Scholar 

  33. 33.

    Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, Sanduleanu S, Larue RTHM, Even AJG, Jochems A, van Wijk Y, Woodruff H, van Soest J, Lustberg T, Roelofs E, van Elmpt W, Dekker A, Mottaghy FM, Wildberger JE, Walsh S. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749–62.

    Article  Google Scholar 

  34. 34.

    Schob S, Meyer HJ, Pazaitis N, Schramm D, Bremicker K, Exner M, Höhn AK, Garnov N, Surov A. ADC histogram analysis of cervical cancer aids detecting lymphatic metastases-a preliminary study. Mol Imaging Biol. 2017;19(6):953–62.

    Article  Google Scholar 

  35. 35.

    Liu S, Zhang Y, Chen L, Guan W, Guan Y, Ge Y, He J, Zhou Z. Whole-lesion apparent diffusion coefficient histogram analysis: significance in T and N staging of gastric cancers. BMC Cancer. 2017;17(7):665.

    Article  Google Scholar 

Download references


We would like to express our gratitude to all those who helped us during the writing of this manuscript.


Health and Health Technology Innovation Project of Jilin Province (2018J078); Discipline Layout Project of Natural Science Foundation of Technology Department of Jilin Province (20200201373JC).

Author information




YBT, HCC, LS, LT and GYY conceived of the study, and YBT, ZXW and DJ participated in its design and coordination and YBT helped to draft the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Jun Ding.

Ethics declarations

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Yu, B., Huang, C., Liu, S. et al. Application of first-order feature analysis of DWI-ADC in rare malignant mesenchymal tumours of the maxillofacial region. BMC Oral Health 21, 463 (2021).

Download citation


  • Maxillofacial
  • Mesenchymal tumours
  • MRI
  • DWI
  • Radiomics feature
  • First-order feature