354

ORIGINAL
Multiparametric Analysis of Tumor Morphological andFunctional MR Parameters Potentially Predicts Local Failure
in Pharynx Squamous Cell Carcinoma Patients
Noriyuki Fujima1, 2, Yukie Shimizu1, Daisuke Yoshida1, Satoshi Kano3, Takatsugu Mizumachi3, Akihiro Homma3,
Koichi Yasuda2, 4, Rikiya Onimaru4, Osamu Sakai5, Kohsuke Kudo1, 2, and Hiroki Shirato2, 4
1
Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan, 2The Global Station for Quantum
Medical Science and Engineering, Global Institution for collaborative research and education, Sapporo, Japan, 3Department of Otolaryngology-Head and Neck Surgery, Hokkaido University Graduate School of Medicine, Sapporo, Japan, 4Department of Radiation Medicine, Hokkaido
University Graduate School of Medicine, Sapporo, Japan, 5Departments of Radiology, Otolaryngology-Head and Neck Surgery, and Radiation
Oncology, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA

Abstract : Purpose : To predict local control / failure by a multiparametric approach using magnetic resonance(MR)-derived tumor morphological and functional parameters in pharynx squamous cell carcinoma (SCC) patients. Materials and Methods : Twenty-eight patients with oropharyngeal and hypopharyngeal SCCs were included in this study. Quantitative morphological parameters and intratumoral characteristics on T2-weighted images, tumor blood flow from pseudo-continuous arterial spin labeling, and tumor diffusion parameters of
three diffusion models from multi-b-value diffusion-weighted imaging as well as patients’ characteristics wereanalyzed. The patients were divided into local control / failure groups. Univariate and multiparametric analysis
were performed for the patient group division. Results : The value of morphological parameter of ‘sphericity’and intratumoral characteristic of ‘homogeneity’ was revealed respectively significant for the prediction of thelocal control status in univariate analysis. Higher diagnostic performance was obtained with the sensitivity of
0.8, specificity of 0.75, positive predictive value of 0.89, negative predictive value of 0.6 and accuracy of 0.79 bymultiparametric diagnostic model compared to results in the univariate analysis. Conclusion : A multiparametric analysis with MR-derived quantitative parameters may be useful to predict local control in pharynx SCC
patients. J. Med. Invest. 68 : 354-361, August, 2021
Keywords : Magnetic Resonance Imaging, Multiparametric Magnetic Resonance Imaging, Squamous Cell Carcinoma of Head and Neck,
Treatment Outcome

INTRODUCTION
Achieving local control with the appropriate methods aretreatment goals for patients with head and neck squamous cell
carcinomas (SCCs). It would be very helpful if we could predict
the treatment outcomes such as local control or failure whentreatment decisions are being made, such as type of surgery,selection of chemotherapy (e.g., whether to perform induction
chemotherapy), and radiation therapy planning, and also to
determine the best post-treatment imaging follow-up strategy.
The optimization of patient assessment will lead to truly personalized precision medicine for the best prognosis.
To predict prognoses of patients with SCCs, which are closely
related to treatment outcomes, conventionally the TNM stage,
tumor size, and histological grade have been used (1-3). In addition to such traditional tumor information, characteristics oftumor morphology and intratumoral heterogeneity have been
evaluated by an advanced mathematical approach using conventional imaging by CT, MRI and FDG-PET ; advanced morphological shape characteristics of tumors such as sphericity wereindicated to be useful for the prediction of treatment outcomesReceived for publication March 12, 2021 ; accepted August 19, 2021.
Address correspondence and reprint requests to Noriyuki Fujima,
Department of Diagnostic and Interventional Radiology, Hokkaido
University Hospital, N15, W7, Kita-Ku, Sapporo 060-8638, Japan andFax : +81-11-706-7876.

in head and neck SCC compared to simple tumor size information, whereas intratumoral heterogeneity was also indicated asa prognostic factor in head and neck SCC patients and tumors
with high degree of intratumoral heterogeneity demonstrated atendency for poor prognoses (4, 5). Tumor functional informationsuch as tumor perfusion values was described as an important
predictor of the treatment outcome ; lower tumor perfusion was
related to poor treatment outcome in these reports, probably
because of the relation between tumor hypoxia and lower tumor
perfusion (6, 7). In patients with head and neck SCCs, tumor diffusion information was reported to be useful to predict the treatment outcome ; diffusion parameters were suggested to reflecttumor microenvironment such as tumor cell density, and thus
enable to evaluate tumor characteristics in greater detail (8, 9).
In addition, a non-Gaussian diffusion model such as diffusion
kurtosis imaging (DKI) was considered in order to visualize themicrostructural complexity (10, 11), which could have a possible
relation to the presence of intratumoral necrosis (12). This information was useful for outcome prediction.
These tumor characteristics may provide higher diagnostic
accuracy when several of the above-described information iseffectively combined rather than using each parameter independently. Multiparametric model, such as machine learning approach, were introduced for the effective selection ofseveral parameters as a type of analysis technique including thefield of head and neck oncology (13-15). Effectively combining
several tumor characteristics may result in a better diagnostic
performance.

The Journal of Medical Investigation Vol. 68 2021

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The Journal of Medical Investigation Vol. 68 August 2021
We conducted the present study to determine the diagnostic
accuracy of magnetic resonance (MR)-derived parameters oftumor morphology, intratumoral characteristics, tumor perfusion, diffusion information, and the multiparametric approachby integrating several of those MR-derived parameters efficiently to predict treatment outcomes in patients with oropharyngeal
and hypopharyngeal SCCs. We targeted patients treated withsuper-selective arterial infusions of cisplatin with concomitantradiotherapy which have become popular for head and neck SCC
because of its higher local control rate in advanced cases, including pharynx SCC (16).

PATIENTS AND METHODSPatients

This retrospective study was approved by our institutional
review board, and the requirement to obtain written informed
consent was waived. From March 2010 to June 2017, 28 patientswith oropharyngeal and hypopharyngeal SCCs referred to our
hospital met all of the following inclusion criteria and were included in this study ; (1) had a histopathological diagnosis of SCC
in the oropharynx or hypopharynx, (2) received a specific treatment regimen (super-selective arterial infusion of cisplatin withconcomitant radiotherapy of total 70 Gy) with curative intent,(3) had undergone an MRI examination including conventional
anatomical imaging of T1-weighted images (T1WI), fat-suppressed T2-weighted images (Fs-T2WI), arterial spin labeling(ASL), and multiple b-value diffusion-weighted imaging (DWI)
before treatment. The patients’ characteristics are summarizedin Table 1.
To avoid a large variation of treatment regimens that can affect the interpretation of the analysis results, we examined thecases of only the patients who underwent a specific treatment
regimen (which did not differ among the patients). The detailsof this regimen are as follows : an arterial infusion of cisplatin(100–120 mg / m2 per week for 4 weeks) to the primary tumor’s
dominant blood supply via a microcatheter, with concurrent
radiotherapy of a total of 70 Gy in 35 fractions (16). For the management of any residual mass of neck metastatic lymph nodes
after chemoradiation therapy, the patients with resectable necknodes underwent additional surgical dissection.

imaging (EPI) with three orthogonal motion-probing gradients
following a reported acquisition protocol with 12 b-values : 0,
10, 20, 30, 50, 80, 100, 200, 400, 800, 1000, and 2000 s / mm2(18, 19) to analyze the tumor microstructural information using
DWI-derived parameters. All images were acquired on the basisof the axial plane. The imaging parameters used for the T1WI,
T2WI, pCASL, and DWI are summarized in Table 2.

Data analysis
ROI delineation
Each tumor was delineated by a board-certified neuroradiologist with 10 years’ experience with a polygonal region-of-interest(ROI) on Fs-T2WI, ASL perfusion images, and DWI b0 images.
The ROI of the axial Fs-T2WI was first placed, and then thesame ROI was delineated with reference to the Fs-T2WI ROI
so that the same region was delineated in all images. Manual

Table 1. Patient characteristics
Age
Median62 years old

Mean

61.3 years old

Range

41-77 years old

GenderMale

24

Female4

Primary tumor site
Oropharynx

24

Hypopharynx

4

T-stage
T1

0

T2

6

T3

4

T4a

15

T4b

3

N-stage
N0

6

MR imaging protocol

N1

6

All MR scanning was performed using a 3.0-Tesla unit (Achieva TX ; Philips Healthcare, Best, Netherlands) with a 16-channel neurovascular coil. All axial slices were placed in parallelwith the anterior commissure‒posterior commissure line. Conventional MRI sequences of axial FsT2WI were obtained to analyze the tumor morphological and intratumoral heterogeneity
parameters. As supportive information, axial T1WI was also
used for the evaluation. Pseudo-continuous ASL (pCASL) was
conducted for the analysis of the tumor perfusion information.
The acquisition of pCASL was performed using multi-shot spinecho echo-planar imaging to obtain control and labeled images
following a previously described protocol (17). In this protocol,
the post-labeling delay (PLD) time was set at 1280 ms ; this PLD
was a bit shorter compared to the conventional time (typicallyaround 1500-2000 ms) ; however, it was not considered inappropriate when evaluating tumor blood flow because a marked
transit delay is frequently not present when assessing malignant
tumors. The labeling slab was placed just under the bifurcationof the internal and external carotid arteries. Control images
were obtained without the labeling of arterial water, using thesame imaging scheme as the labeled images. Multi-b-point DWI
was also conducted using single-shot spin-echo echo-planar

N2

16

N3

0

Smoking status
Tabacco smokers

24

Packs-years
Median29 packs-years

Mean

29.5 packs-years

Range

8-94 packs-years

Alcohol use
Occasional or non-drinker

7

Moderate use (< 14 units / week)

3

Heavy use (≥ 14 units / week)18

HPV status
Positive

8

Negative

6

Unknown

14

Table 1 footnote : one unit of alcohol use means 58 g / 10 mL
ethanol.

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356

correction of the ROI was performed as necessary dependingon the degree of image distortion observed in ASL and DWI b0
images. For the DWI analysis, the tumor ROI on the b0 image
was copied on the respective b-value images. Any areas of suspected necrosis or cystic lesions were excluded from the ROI. If
the tumor extended into two or more slices, all slices were delin-

eated respectively. In addition, the sternocleidomastoid muscleof the healthy side was also delineated with a polygonal ROI on
Fs-T2WI to calculate the relative T2WI signal intensity. The example of tumor ROI delineation on Fs-T2WI, ASL and DWI waspresented in Figure 1.

Table 2. Imaging parameters
Basic
Sequence

TR (ms)TE (ms)FOV (cm)

matrixslice
thickness
(mm)

slice
gap
(mm)

scanning
time

T1WI

SE

450

10

24 × 24512 × 512

5

1.5

2'12

Fs-T2WI

TSE

4500

70

24 × 24512 × 512

5

1.5

2'06

SE-EPI

3619

18

23 × 2380 × 80

5

1.5

5'11

pCASL
DWI

Other specific parameters ; labeling duration 1650 ms, PLD 1280 ms, SENSE factor 2.5
SE-EPI

4500

64

23 × 2364 × 64

5

1.5

4'37

Other specific parameters ; EPI factor 32, SENSE factor 2.0

Table 2 footnote : TR : repetition time, TE : echo time, FOV : field of view, T1WI : T1 weighted image, SE : spin echo, Fs-T2WI : fat-suppressed T2 weighted image, TSE : turbo spin echo, pCASL : pseudo-continuous arterial spin labeling, SE-EPI : spin-echo echo-planar
imaging, PLD : post labeling delay, SENSE : sensitivity encode, DWI : diffusion weighted imaging, EPI : echo planar imaging.

ａ

ｂ

ｃ

Fig 1. Tumor ROI delineation. Each primary tumor was firstly outlined by a polygonal ROI on Fs-T2WI (a ; arrow). Subsequently, primarytumor on ASL perfusion images (b ; arrow) and b0 images of DWI (c ; arrow) were delineated with reference to the Fs-T2WI ROI so that thesame region was outlined among Fs-T2WI, ASL and DWI. For the DWI analysis, the tumor ROI on the b0 image was then copied on the EPI of
the respective b-values (c ; arrowheads).

The Journal of Medical Investigation Vol. 68 August 2021
Analysis of the Fs-T2WI for morphological and intratumoral data
For the analysis of morphological characteristics, the tumor
volume and tumor surface area were directly calculated fromthe morphological shape, which consisted of all selected tumor
voxels in the delineated tumor ROI on Fs-T2WI. The sphericity
of each tumor was calculated by a previously reported method(4). For the intra-tumoral characteristics analysis, the relative
Fs-T2WI mean signal intensity (= the mean signal on the tumor
ROI / the medial pterygoid muscle ROI), the coefficient of variance (CV) in the tumor ROI, and the textural parameters within
the ROI were calculated. In the analysis of textural features, thegray-level co-occurrence matrix (GLCM) features were analyzed
because GLCM features were reported as the most common andsensitive texture descriptor to calculate lesion heterogeneity ingreater detail from the texture data (20). Texture parameters ofcontrast, correlation, energy and homogeneity were calculated
based on a reported method (21). In addition, for the calculationof texture parameters, intensity normalization was performedby remapping the brightness to an 8-bit (from 1 to 256) scale
between the minimum and maximum values or within the meanvalue ± 3 standard deviations of an image (22).
ASL analysis
The tumor blood flow (TBF) was calculated using the pCASL
technique from the signal data in ASL subtraction images using
a previously described following equation (17) ;where f is the value of TBF, ΔM is the signal difference between pCASL labeled and control images, R1a is the longitudinalrelaxation rate of blood (0.67 s−1) (14), γ is the labeling time (1.65s), ω is the post-labeling delay time (1.28 s), α is the labelingefficiency (0.85), and λ is the blood / tumor-tissue water partition
coefficient (1.0 g / mL) (17). M0 is the equilibrium magnetizationof the tumor tissue, which was estimated from the signal intensity in control images. TBF values in each tumor were calculated
as the mean TBF value in each tumor’s delineated tumor ROI.
DWI analysis
From the diffusion-weighted imaging data, each parameterof mono-exponential function (the apparent diffusion coefficient ; ADC), the bi-exponential function, i.e., the so-called intravoxel incoherent motion (IVIM) (the perfusion fraction f, the fastdiffusion coefficient D*, and the slow diffusion coefficient D), andthe DKI (kurtosis value K and kurtosis-corrected diffusion coefficient Dk) were calculated. Using the signal intensities of all 12
b-values, we calculated the bi-exponential function parameters.
Assessments of the ADC and DKI usually target tissue diffusion(except for the perfusion-related signal), and we therefore used
the signal intensity of six b-values (0, 200, 400, 800, 1000, and2000 s / mm2) for the parameter calculations of the ADC and
DKI. To perform these parameter calculations, we used the following equations (19) ;357

where S(b) is the signal intensity at the b-value denoted by
the subscript, S0 is the signal intensity at the b-value of 0, andb is b-factor in Eqs. [1] to [3]. We fitted the signal intensity ofb-values in Eqs. [1] to [3] with least square fitting using theLevenberg-Marquardt algorithm. We used the average signal
intensity value in the ROI on each image for the calculationof these diffusion parameters. The self-developed program by
MATLAB ver. 2015a (MathWorks, Natick, MA) was used for theabove-described calculations.

Clinical assessment for the final diagnosis
In all patients, clinical and radiological follow-ups were performed after the treatment to determine the final diagnosis
related to the local control / failure at least 2 years. The follow-up
duration was measured from the end of treatment. We assessed
whether each patient experience local failure during its whole
follow up time. Local failure was determined by the histopathological confirmation of SCC in biopsied or surgically resected
tissue, or development of a new mass in the post-treatment site orresidual tumor enlargement on follow-up imaging studies.

Statistical analysis based on the univariate and multiparametricapproach
For the multiparametric approach, a linear support vector machine (SVM) was used to obtain the best combination of features
for the differentiation of local control versus failure groups. For
the SVM analysis, a linear kernel and cost parameter (C) of 1 (=default setting) were used. We simply used this default setting
to avoid overfitting of the created model. In creating the multiparametric diagnostic model and in evaluating the model accuracy, a leave-one-out cross-validation scheme was used. In thisprocess, the data were divided into 27 training cohorts and one
validation cohort ; 28 repetitions of the training and validation
analyses with different validation cohorts were performed in aleave-one-out cross-validation scheme. In each training session,feature extraction and SVM model creation were performed. All
MR-derived parameters (three morphological characteristics,
six intratumoral characteristics, one perfusion parameter, andsix diffusion parameters) were first included. Before the feature
selection, multicollinearity between respective parameters wasassessed in advance with the variance inflation factors (VIFs)(reference value of 10). Parameters that showed multicollinearity to other parameters were subsequently removed. In thefeature-selection process, a backward sequential selection toeliminate features was performed. This method was based on
an iterative technique in which the starting condition included
all of the features. With each iteration, the feature with theleast impact on the contribution to the diagnostic performancefor the differentiation was eliminated. Features were removed
one by one, and the process was repeated until the number offinally selected features was less than or equal to three ; thiselimination process was performed to avoid overfitting of the
finally established diagnostic model. In each validation session,
one validation cohort was diagnosed using the created multiparametric model in the training session. The mean diagnostic
accuracy over 28 repetitions was calculated to determine thediagnostic performance that could predict patients with local
control / failure.
We also conducted a univariate analysis to compare all MR
derived parameters and patients’ characteristics (T-stage,
N-stage, smoking status, alcohol use and human papillomavirus status) respectively between the patients with and without
local control using the Mann-Whitney U-test. In assessing
the patients‘ characteristics, we converted each characteristic
into a score as follows : T-stage (T1 = 1, T2 = 2, T3 = 3, T4 = 4),
N-stage (N0 = 0, N1 = 1, N2 = 2, N3 = 3), alcohol use (occasional

N. Fujima, et al. Multiparametric analysis for pharynx SCC

358

or non-drinker = 0, moderate use = 1, heavy use = 2) and HPVstatus (positive = 0, negative or unknown = 1), whereas the packyears value was directly used in assessing the smoking status.
If a significant difference was observed, such parameters were
further analyzed by the receiver operating characteristic (ROC)
curve analysis, after that, the diagnostic power was determinedusing the optimal cut-off value determined by the Youden Index.

RESULTS
Eight patients were revealed to have local failure by a clinicalassessment. Of these eight patients, local failure was proven in
six by a surgical biopsy with the histological demonstration ofresidual SCC. In the other two patients, a large new lesion wasobserved by CT images during the follow-up period. The remaining 20 patients were revealed to have achieved local control byclinical observation and imaging studies throughout their follow-up (mean 73.2 months ; range 27–98 months).
In all 28 SCC patients, the analyses of the primary sites weresuccessfully performed. Table 3 summarizes the details of all
MR derived parameters (tumor morphological, intratumoral
characteristics, the perfusion and diffusion parameters) and patients’ characteristics (T-stage, N-stage, smoking status, alcohol

Table 3. Patient characteristics and parameters of the patients with local control and failure
Treatment outcome
Local control

Local failure

T1

0

0

T2

6

0

T3

2

2

T4

12

6

N0

4

2

N1

3

3

N2

13

3

N3

0

0

Packs-years

32.9±23.8

20.8±16.7

Occasional or
non-drinker

52Moderate use

30

Heavy use

126
1

T-stageN-stage

Patients’characteristics

Smoking status

Alcohol use

Positive

6

Negative

30

Unknown

11

7

HPV status

MorphologicalParameters

Intratumoral
Characteristics
Parameters

Perfusion
Parameters

Diffusion
Parameters

P value

0.11

0.36

0.15

0.87

0.39

Tumor volume (ml)15.9 ± 7.6

15.3 ± 7.2

0.81

Surface area (cm2)40.8 ± 1350.1 ± 16.3

0.24
0.008*

Sphericity

0.59 ± 0.1

0.45 ± 0.08

Relative mean signal

3.1 ± 0.4

3.1 ± 0.6

0.76

Coefficient of variance0.12 ± 0.02

0.13 ± 0.03

0.78

Contrast

34.8 ± 6.8

37.3 ± 6.5

0.32
0.34

Correlation

0.84 ± 0.05

0.81 ± 0.04

Energy (×10-3)

1.7 ± 0.2

1.5 ± 0.5

0.24

Homogeneity

0.29 ± 0.03

0.27 ± 0.01

0.03*

TBF (ml / 100g / min)125.6 ± 17.6

108.7 ± 26.6

0.09

ADC (×10-3 mm2 / s)

0.92 ± 0.09

0.89 ± 0.1

0.44

f (×102 %)0.14 ± 0.04

0.12 ± 0.03

0.18

D* (×10-3 mm2 / s)

18.9 ± 6.2

15.2 ± 5.2

0.21

D (×10-3 mm2 / s)

0.73 ± 0.09

0.74 ± 0.11

0.65

K

0.78 ± 0.18

0.82 ± 0.09

0.51

Dk (×10-3 mm2 / s)

0.97 ± 0.11

0.94 ± 0.16

0.46

Table 3 footnote : Smoking status and data of all parameters are mean ± standard deviation. TBF : tumor blood
flow, ADC : apparent diffusion coefficient, D : slow diffusion coefficient, f : perfusion fraction, D* : fast diffusion coefficient, K : kurtosis value, Dk : kurtosis corrected diffusion coefficient.

The Journal of Medical Investigation Vol. 68 August 2021
use and human papillomavirus status) between the local control
and failure groups.
In the multiparametric analysis of the prediction of the local
control status, the diagnostic performance (= average of 28 repetitive evaluations in a leave-one-out cross validation scheme) in
the training session was as follows : sensitivity, 0.86 ; specificity,
0.82 ; positive predictive value (PPV), 0.92 ; negative predictive
value (NPV), 0.7 ; and accuracy, 0.85. Table 4 summarizes thedetails of selected features and the diagnostic performance ineach training session. The diagnostic performance in the validation session was as follows : sensitivity, 0.8 ; specificity, 0.75 ;positive predictive value (PPV), 0.89 ; negative predictive value(NPV), 0.6 ; and accuracy, 0.79.
In a univariate analysis, results in each variable between thepatients with and without local control using the Mann-Whitney
U-test were also provided in Table 3. The value of sphericity(p < 0.01) and homogeneity (p < 0.05) was revealed respectively
significant. Respective diagnostic performance of these parameters was determined by ROC curve analysis as follows ; [sphericity] sensitivity of 0.65, specificity of 0.75, PPV of 0.87, NPV
of 0.46, and accuracy of 0.68, [homogeneity] sensitivity of 0.7,
specificity of 0.63, PPV of 0.82, NPV of 0.45, and accuracy of359

0.68. The diagnostic accuracy of these parameters were lower
compared to the multiparametric analysis based diagnostic accuracy (i.e. diagnostic accuracy obtained from validation session
in the multiparametric analysis).

DISCUSSION
In the current study, several significant differences in univariate and multiparametric analyses using MR-derived quantitative parameters were obtained between local control andfailure in patients with oropharyngeal or hypopharyngeal SCC
treated by super-selective arterial infusions of cisplatin withconcomitant radiotherapy. Our results in univariate analysis indicated that tumor sphericity and homogeneity were respectively
revealed significant difference between local control and failure,
whereas the value of TBF was revealed nearly significant although statistical significance was not obtained. Some studies
previously investigated morphological (4, 23), intratumoral
characteristics (4, 5), perfusion and diffusion information (9,24) using medical imaging dataset in patients with pharyngeal
SCC for predicting the treatment outcome, however, no study

Table 4. Detail of the training session
No. ofsessionSensitivity

Specificity

PPV

NPV

Acuracy

Finally selected features

1

0.84

0.87

0.94

0.7

0.85

Sphericity, Homogeneity, TBF

2

0.89

0.75

0.89

0.75

0.85

Sphericity, Homogeneity, Surface area

3

0.84

0.87

0.94

0.7

0.85

Sphericity, Homogeneity, TBF

4

0.85

0.71

0.89

0.62

0.81

Sphericity, Homogeneity, TBF

5

0.84

0.87

0.94

0.7

0.85

Sphericity, Homogeneity, TBF

6

0.85

0.71

0.89

0.62

0.81

Sphericity, Homogeneity, TBF

7

0.84

0.75

0.88

0.66

0.81

Sphericity, Homogeneity, TBF

8

0.89

0.75

0.89

0.75

0.85

Sphericity, Homogeneity, TBF

9

0.84

0.87

0.94

0.7

0.85

Sphericity, Homogeneity, ADC

10

0.89

0.75

0.89

0.75

0.85

Sphericity, Homogeneity, TBF

11

0.84

0.87

0.94

0.7

0.85

Sphericity, Homogeneity, TBF

12

0.85

0.85

0.94

0.66

0.85

Sphericity, Homogeneity, TBF

13

0.84

0.87

0.94

0.7

0.85

Sphericity, Homogeneity, ADC

14

0.85

0.85

0.94

0.66

0.85

Sphericity, Homogeneity, TBF

15

0.84

0.87

0.94

0.7

0.85

Sphericity, Homogeneity, TBF

16

0.89

0.75

0.89

0.75

0.85

Sphericity, Homogeneity, ADC

17

0.89

0.75

0.89

0.75

0.85

Sphericity, Homogeneity, TBF

18

0.85

0.85

0.94

0.66

0.85

Sphericity, Homogeneity, ADC

19

0.84

0.87

0.94

0.7

0.85

Sphericity, Homogeneity, TBF

20

0.84

0.87

0.94

0.7

0.85

Sphericity, Homogeneity, Surface area

21

0.85

0.85

0.94

0.66

0.85

Sphericity, Homogeneity, TBF

22

0.84

0.75

0.88

0.66

0.81

Sphericity, Homogeneity, TBF

23

0.85

0.85

0.94

0.66

0.85

Sphericity, Homogeneity, TBF

24

0.84

0.87

0.94

0.7

0.85

Sphericity, Homogeneity, TBF

25

0.84

0.87

0.94

0.7

0.85

Sphericity, Homogeneity, TBF

26

0.89

0.75

0.89

0.75

0.85

Sphericity, Homogeneity, ADC

27

0.85

0.85

0.94

0.66

0.85

Sphericity, Homogeneity, TBF

28

0.84

0.87

0.94

0.7

0.85

Sphericity, Homogeneity, TBF

Table 4 footnote : PPV, positive predictive value : NPV, negative predictive value : tumor blood flow : ADC, apparent diffusion coefficient.

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N. Fujima, et al. Multiparametric analysis for pharynx SCC

simultaneously evaluated all of these information using thesame patient cohorts. In addition, our results indicated that a
multiparametric approach-based diagnosis have a potential topredict the treatment outcomes more accurately than univariate
approach. Especially, high sensitivity and positive predictive
value to detect patients with the local control was obtained. The
knowledge during the pretreatment period would be of great
help to optimize the treatment regimen, for example regarding
the need for additional chemotherapy such as induction therapy
or possible earlier salvage surgery after current chemoradiotherapy. It would be also helpful to optimize the follow-up imaging
strategy depending on the risk in each patient. Particularly,
investigation related to imaging biomarkers for the outcome prediction in patients who received intraarterial chemotherapy
was very limited ; our results may be useful information in theassessment for such patient population.
Related to the prediction of treatment outcomes using medical imaging dataset, as mentioned above, several studies havedescribed the utility of tumor morphological information, tumor
heterogeneity, and perfusion and diffusion information. The
conventional morphological parameters of tumor volume andT-stage were reported to be predictive factors for the assessmentof local control (25). In addition, several other studies investigated more advanced tumor morphological features compared toabovementioned conventional tumor volume information ; thesestudies reported that tumors with low ‘sphericity’ (i.e. high‘asphericity’) were related to poor treatment outcome (4, 23). In
addition, a recent analysis focused on intratumoral heterogeneity in the tumor depicted on medical imaging by calculating thesecond-order texture parameters reported that these parameters
are independent indicators for the prediction of local control, anddescribed tumors with highly heterogeneous intratumoral characteristics tended to result in poor treatment outcome (5). Tumor
perfusion and diffusion have also been described as useful for theprediction of treatment outcomes as tumor functional information-reflecting parameters (9, 24), although pCASL and multiple
b-value based DWI derived parameters were not statisticallysignificant between treatment control and failure by univariate
analysis in the current study. Our present study might accomplish the efficient selection of several features from various MR
derived parameters and integrate them effectively to obtain the
more powerful diagnostic algorithm. Multiparametric approachis an important diagnostic method with the selection of parameters efficiently and will continue to provide clinically usefulness.
Several studies with this analytical approach providing clinicalusefulness including the area of head and neck imaging werereported in recent years (14, 26, 27). However, the number ofreports have exclusively focused on intratumoral characteristics
such as second order texture parameters ; the utility of combined
use including tumor shape information (e.g. tumor sphericity)and tumor functional information (e.g. tumor perfusion and diffusion) is still very limited. The analysis with initially assessing
all of these information and efficient parameters selection may
achieve higher diagnostic performance. In the current study, we
preformed the multiparametric approach with the small numberof finally selected parameters to avoid overfitting because ourstudy cohorts in training dataset consisted of the small numberof patients ; the sample size for training in multiparametricapproach was generally considered at least 5-10 times the number of parameters used for the final diagnostic model (28). In
addition, we initially used a relatively small number of imaging
features (i.e., a total 16 MR-derived parameters) in creating
the diagnostic model compared to conventional so-called radiomics studies which typically include hundreds of imaging features. As described above, the current study population was notlarge ; therefore, we analyzed a small number of imaging features

to avoid inappropriate model creation due to overfitting andmulticollinearity between imaging features ; such problems tend
to be present in analyses with a small number of subjects and alarge number of imaging features. Further study will be needed
to reveal the presence of elevation in diagnostic performance byusing all of tumor morphological, intratumoral characteristics,
tumor perfusion and diffusion information with a larger numberof study cohorts. Moreover, the validity of the current modelneeds to be assessed with the different cohort in the future study.
The present study has several limitations. First, the number ofpatients was small. We selected patient cohorts with availabilityof entirely MR-derived information, in addition, only patients
who received the specific treatment regimen (i.e. super-selective
arterial infusions of cisplatin with concomitant radiotherapy)
were selected to avoid bias caused by different treatment methods ; these causes resulted in limited number of patients. As
mentioned above, several studies suggested that tumor perfusion and diffusion can be useful in the prediction of treatment
outcomes. However, pCASL- and DWI-derived parameters in
the current study were not statistically significant in univariate
analysis, although TBF obtained from pCASL and f from IVIM
might have shown a tendency to predict local control / failure. We
speculate that this was due to the small number of patients inthe current study. Additionally, the specific treatment regimen
used might have affected the results. Therefore, the results ofthe current study should be treated as preliminary, and further
analysis with a larger number of patients is needed as describedabove. Second, the HPV status of some of the patients was notavailable. This status has a significant influence on prognosis
and should be added to the multiparametric approach as animportant parameter.
In conclusion, the multiparametric approach with the integration of several MR-derived tumor characteristics might
have a possibility to become one of the supporting tools for theprediction of treatment outcomes. This diagnostic technique may
contribute to the determination of the appropriate assessment
for patients with oropharyngeal and hypopharyngeal SCCs.

CONFLICT OF INTERESTS-DISCLOSURE
None

ACKNOWLEDGEMENTNone

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