Diffusion Weighted Imaging in Predicting Progression Free Survival in Patients with Squamous Cell Carcinomas of the Head and Neck Treated with Induction Chemotherapy

Rationale and Objectives

The aim of this study was to assess the role of diffusion-weighted imaging in predicting progression-free survival in patients with head and neck squamous cell carcinoma (HNSCC) treated with induction chemotherapy.

Materials and Methods

Eighteen patients with HNSCC underwent diffusion-weighted imaging studies prior to treatment and within 3 weeks after completion of induction chemotherapy. Median apparent diffusion coefficient (ADC) values were computed from the largest cervical metastatic lymph node. Percentage changes in ADC values from pretreatment to posttreatment time points were compared between alive and dead patients using the Mann-Whitney U test. P values < .05 were considered statistically significant.

Results

A 22% increase in ADC was observed after induction chemotherapy in alive patients ( n = 15), while patients who died from HNSCC ( n = 3) demonstrated a 33% decrease in ADC. The difference in percentage change in ADC between alive and dead patients was significant ( P = .039).

Conclusions

ADC may be a useful marker in predicting progression-free survival in patients with HNSCC undergoing induction chemotherapy.

Neoadjuvant induction chemotherapy in patients with head and neck squamous cell carcinoma (HNSCC) has resulted in a decline in the risk for distant metastasis and an upsurge in organ preservation . In general, patients responsive to induction chemotherapy also demonstrate positive responses to subsequent radiotherapy . However, given the economic burden and toxic side effects associated with induction chemotherapy in patients with HNSCC , imaging biomarkers that can evaluate treatment and survival outcomes in assessing the efficacy of induction chemotherapy are desirable.

Several physiologic imaging modalities, such as 2-[ 18 F]- fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET) , computed tomographic perfusion imaging , proton and phosphorous magnetic resonance spectroscopy , and dynamic contrast-enhanced magnetic resonance imaging (MRI) , have been proposed for predicting and monitoring treatment response as well as predicting survival outcomes in patients with HNSCC undergoing concurrent chemoradiation therapy and surgery. However, lower spatial resolution and specificity associated with FDG-PET, technical limitations associated with performing magnetic resonance spectroscopy, and rigorous steps involved in the analysis of computed tomographic perfusion and dynamic contrast-enhanced MRI data limit the use of these modalities in the routine clinical settings. On the contrary, diffusion-weighted imaging (DWI), which provides maps of microscopic water motion within biologic tissues, offers a more simplistic approach to ascertain physiologic changes within the tumor after treatment. Moreover, relative ease of data acquisition and availability of data processing tools for computing the magnitude of microscopic motion on routine clinical scanners make DWI a more suitable choice.

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Materials and methods

Patient Population and Treatment

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Table 1

Characteristics of the Patients

Patient Induction Chemotherapy Protocol Age (y) Gender T N M Dead vs Alive 1 CDDP + TXT + 5-FU 64 M Tx N3 M0 Died of disease 2 CDDP + TXT + 5-FU 55 F T3 N2c M0 Died of disease 3 CDDP + TXT + 5-FU 48 F T4 N2b M0 Died of disease 4 CDDP + TXT + 5-FU 51 M Tx N2b M0 Alive 5 CDBCA + TAX 54 M T3 N2b M0 Alive 6 CDBCA + TAX 63 M T2 N2b M0 Alive 7 CDBCA + TAX + 5-FU 72 M T4 N2c M0 Alive 8 CDDP + TXT + 5-FU 67 M T0 N2b M0 Alive 9 CDDP + TXT + 5-FU 57 M T4 N2c M0 Alive 10 CDDP + TXT + 5-FU 52 M T3 N2c M0 Alive 11 CDDP + TXT + 5-FU 49 M T2 N2c M0 Alive 12 CDDP + TXT + 5-FU 54 M T4 N2c M0 Alive 13 CDDP + TXT + 5-FU 47 M T4 N2b M0 Alive 14 CDDP + TXT + cetuximab 71 M T2 N2a M0 Alive 15 CDDP + TXT + cetuximab 45 M T2 N2b M0 Alive 16 CDDP + TXT + cetuximab 62 M T3 N2b M0 Alive 17 CDDP + TXT + cetuximab 51 M T2 N2b M0 Alive 18 CDDP + TXT + cetuximab 62 M T2 N2c M0 Alive

CDBCA, carboplatin; CDDP, cisplatin; F, female; 5-FU, 5-fluorouracil; M, male; TAX, paclitaxel; TXT, docetaxel.

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Data Acquisition

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Image Processing

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SI(TE)=SI0×exp(−TE/T2). SI

(

)

exp

(

−

)

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SIi=SI0×exp(bi×ADC), SI

exp

(

ADC

)

where SI i is the SI of the diffusion-weighted image at b i .

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(∑numberofpixelsencompassingaregionofinterestfromeachslice)×(slicethickness). (

∑

number

pixels

encompassing

region

interest

from

each

slice

)

(

slice

thickness

)

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Clinical Follow-Up and Data Analysis

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percentagechangeinparameter=[(posttreatmentvalueofparameter−pretreatmentvalueofparameter)/pretreatmentvalueofparameter]×100. percentage

change

parameter

[

(

posttreatment

value

parameter

−

pretreatment

value

parameter

)

pretreatment

value

parameter

]

100

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Results

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Table 2

Percentage Changes (Between Pre- and Post Induction Chemotherapy) in Parameters Between Complete Responders and Partial Responders Assessed by Using Different Clinical Endpoints

Clinical Endpoint Percentage Change_P_ Complete Responders Partial Responders ADC Based on 50% reduction in nodal volume 13.7 (−41, 115) 21.2 (−47, 133) .86 At 6 months from the end of all treatment 21.9 (−47, 133) −15.4 (−41, −6) .164 At last follow-up date (December 30, 2010) 23.7 (−35, 115) −15.4 (−47, 133) .113 T2 Based on 50% reduction in nodal volume −27.1 (−60, 32) −37.5 (−58, 14) .126 At 6 months from the end of all treatment −17.4 (−60, 32) −9.1 (−59, −1) .824 At last follow-up date (December 30, 2010) −17.4 (−40, 32) −9.1 (−60, 14) .546 Volume Based on 50% reduction in nodal volume — — — At 6 months from the end of all treatment −77.1 (−96, −24) −51.1 (−77, −41) .498 At last follow-up date (December 30, 2010) −81.1 (−96, −30) −49.6 (−95, −24) .113

Data are expressed as median (minimum, maximum).

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Discussion

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Acknowledgments

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Diffusion Weighted Imaging in Predicting Progression Free Survival in Patients with Squamous Cell Carcinomas of the Head and Neck Treated with Induction Chemotherapy

Rationale and Objectives

Materials and Methods

Results

Conclusions

Materials and methods

Patient Population and Treatment

Data Acquisition

Image Processing

Clinical Follow-Up and Data Analysis

Results

Discussion

Acknowledgments

References

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