INVESTIGATION OF THE RELATIONSHIP OF COMPUTED TOMOGRAPHY HISTOGRAM ANALYSIS WITH SURVIVAL TIME AND LOCAL CONTROL TIME IN HEAD AND NECK SQUAMOUS CELL CARCINOMA TREATED WITH CHEMORADIOTHERAPY

Yıl 2023, , 133 - 140, 05.04.2023

Sevtap Arslan , Yasin Sarıkaya

https://doi.org/10.18229/kocatepetip.1034928

Öz

OBJECTIVE: This study aimed to evaluate the association between computed tomography (CT) histogram analysis and overall survival and local control in head and neck squamous cell carcinoma (HNSCC) treated with chemoradiotherapy.
MATERIAL AND METHODS: Data archive and CT images from the ‘HNSCC’ study, which is publicly available on ‘The Cancer Imaging Archive’ website, were used in this study. Patients with known Human papilloma virus (HPV) status of the tumor who were treated with concurrent chemoradiotherapy and had pretreatment contrast-enhanced neck CT examination with a slice thickness of 1.3 mm were included. Histogram analysis was performed on 112 tumors and 98 lymphadenopathies. Tumor and lymphadenopathy boundaries, including cystic and necrotic areas, were manually drawn from a single axial CT slice where the lesion size was the largest. Then, histogram parameters [mean, variance, skewness, kurtosis, 1st percentile (P), 10th P, 50th P, 90th P, 99th P] were calculated from the corresponding areas. Kaplan Meier method and univariate and multivariate Cox proportional hazard models were used to examine the association between CT histogram parameters and overall survival and local control.
RESULTS: 95 males and 17 females were included in this study (mean age 59±9.54 years). Mean overall survival was 69.3 months, local control duration was 68.4 months, and the five-year survival rate was 84%. Multivariate Cox proportional hazard model adjusted for age, sex, smoking, HPV status, and primary tumor T (tumor), N (lymph node), and TNM (tumor-lymph node-metastasis) stages showed that mean, 50th P, 90th P, 99th P values of the lymphadenopathy were independent predictors of overall survival, and mean, 1st P, 10th P values of the tumor were independent predictors of local control.
CONCLUSIONS: CT histogram analysis could serve as a pretreatment noninvasive biomarker for predicting overall survival and local control in HNSCC treated with chemoradiotherapy.

Anahtar Kelimeler

Head and neck squamous cell carcinoma, computed tomography, histogram analysis, overall survival, local control

KEMORADYOTERAPİ İLE TEDAVİ EDİLEN BAŞ VE BOYUN SKUAMÖZ HÜCRELİ KANSERİNDE BİLGİSAYARLI TOMOGRAFİ HİSTOGRAM ANALİZİNİN SAĞKALIM SÜRESİ VE LOKAL KONTROL SÜRESİ İLE İLİŞKİSİNİN ARAŞTIRILMASI

Öz

AMAÇ: Bu çalışmada kemoradyoterapi ile tedavi edilen baş ve boyun skuamöz hücreli kanserinde (BBSHK) bilgisayarlı tomografi (BT) histogram analizi ile sağkalım süresi ve lokal kontrol süresi arasındaki ilişkisinin değerlendirilmesi hedeflenmiştir.
GEREÇ VE YÖNTEM: Çalışmamızda ‘Kanser Görüntüleme Arşivi’ veri tabanında kayıtlı ‘Baş ve Boyun Skuamöz Hücreli Kanserleri’ isimli çalışmaya ait veri seti ve bu çalışmaya kayıtlı olguların BT görüntüleri kullanılmıştır. Tümöre ait Human papilloma virüs (HPV) durumu bilinen, konkomitan kemoradyoterapi ile tedavi edilmiş ve tedavi öncesi 1.3 mm kesit kalınlığında kontrastlı boyun BT incelemesi bulunan olgular çalışmaya dahil edilmiştir. 112 tümör ve 98 lenfadenopatiden BT histogram analizi gerçekleştirilmiştir. Lezyonların en geniş boyuta ulaştığı aksiyel kesit belirlenerek bu kesitte lezyon sınırları nekrotik-kistik alanları da içerecek şekilde çizilmiş ve bu alan üzerinden histogram parametreleri [ortalama, varyans, çarpıklık, kurtozis, 1.persentil (P), 10.P, 50.P, 90.P ve 99.P] hesaplanmıştır. Histogram parametrelerinin sağkalım süresi ve lokal kontrol süresi ile ilişkisi Kaplan Meier yöntemi ve tek değişkenli ve çok değişkenli Cox regresyon analizleri ile değerlendirilmiştir.
BULGULAR: Çalışmaya 95 erkek, 17 kadın olgu dahil edilmiştir (ortalama yaş 59.12±9.54 yıl). Ortalama sağkalım süresi 69.3 ay, ortalama lokal kontrol süresi 68.4 ay ve 5 yıllık sağkalım oranı %84’tür. Yaş, cinsiyet, sigara öyküsü, kanser orijini, T (tümör) evresi, N (lenf nodu) evresi, TNM (tümör-lenf nodu-metastaz) evresi ve HPV durumuna göre düzeltme yapılarak çok değişkenli Cox regresyon analizi yapıldığında lenfadenopati histogram parametrelerinden ortalama değer, 50.P, 90.P ve 99.P değerlerinin sağkalım süresini; tümör histogram parametrelerinden ortalama değer, 1.P ve 10.P değerlerinin lokal kontrol süresini tahmin etmede bağımsız belirteçler olduğu bulunmuştur.
SONUÇ: Tedavi öncesi evreleme amaçlı sıklıkla kullanılan BT’den gerçekleştirilecek histogram analizi kemoradyoterapi ile tedavi edilen BBSHK’de sağkalım ve lokal kontrol sürelerinin öngörülmesinde klinik faktörlere ek katkı sağlayabilir.

Anahtar Kelimeler

Baş ve boyun skuamöz hücre karsinomu, Bilgisayarlı tomografi, Histogram analizi, Sağkalım, Lokal kontrol

Kaynakça

• 1. Machiels JP, René Leemans C, Golusinski W, et al. Squamous cell carcinoma of the oral cavity, larynx, oropharynx and hypopharynx: EHNS-ESMO-ESTRO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2020;31(11):1462-75.
• 2. Braakhuis BJ, Brakenhoff RH, Leemans CR. Treatment choice for locally advanced head and neck cancers on the basis of risk factors: biological risk factors. Ann Oncol. 2012;23(10):173-7.
• 3. Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015;65(2):87-108.
• 4. Sacco AG, Cohen EE. Current Treatment Options for Recurrent or Metastatic Head and Neck Squamous Cell Carcinoma. J Clin Oncol. 2015;33(29):3305-13.
• 5. Zanoni DK, Patel SG, Shah JP. Changes in the 8th Edition of the American Joint Committee on Cancer (AJCC) Staging of Head and Neck Cancer: Rationale and Implications. Curr Oncol Rep. 2019;21(6):52.
• 6. Kuno H, Qureshi MM, Chapman MN, et al. CT Texture Analysis Potentially Predicts Local Failure in Head and Neck Squamous Cell Carcinoma Treated with Chemoradiotherapy. AJNR Am J Neuroradiol. 2017;38(12):2334-40 .
• 7. Lassen P, Primdahl H, Johansen J, et al. Impact of HPV-associated p16-expression on radiotherapy outcome in advanced oropharynx and non-oropharynx cancer. Radiother Oncol. 2014;113(3):310-6.
• 8. Zhang H, Graham CM, Elci O, et al. Locally advanced squamous cell carcinoma of the head and neck: CT texture and histogram analysis allow independent prediction of overall survival in patients treated with induction chemotherapy. Radiology. 2013;269(3):801-9.
• 9. Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441-6.
• 10. Bogowicz M, Tanadini-Lang S, Guckenberger M, Riesterer O. Combined CT radiomics of primary tumor and metastatic lymph nodes improves prediction of loco-regional control in head and neck cancer. Sci Rep 2019;9(1):15198.
• 11. Cozzi L, Franzese C, Fogliata A, et al. Predicting survival and local control after radiochemotherapy in locally advanced head and neck cancer by means of computed tomography based radiomics. Strahlenther Onkol. 2019;195(9):805-18.
• 12. Keek SA, Wesseling FWR, Woodruff HC, et al. A Prospectively Validated Prognostic Model for Patients with Locally Advanced Squamous Cell Carcinoma of the Head and Neck Based on Radiomics of Computed Tomography Images. Cancers (Basel). 2021;13(13):3271.
• 13. Ou D, Blanchard P, Rosellini S, et al. Predictive and prognostic value of CT based radiomics signature in locally advanced head and neck cancers patients treated with concurrent chemoradiotherapy or bioradiotherapy and its added value to Human Papillomavirus status. Oral Oncol. 2017;71:150-55.
• 14. Head MACC, Group NQIW. Investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients. Scientific reports. 2018;8(1):1524.
• 15. Li W, Wei D, Wushouer A, et al. Discovery and Validation of a CT-Based Radiomic Signature for Preoperative Prediction of Early Recurrence in Hypopharyngeal Carcinoma. Biomed Res Int. 2020;2020:4340521.
• 16. Zhai TT, Langendijk JA, van Dijk LV, et al. The prognostic value of CT-based image-biomarkers for head and neck cancer patients treated with definitive (chemo-)radiation. Oral Oncol. 2019;95:178-86.
• 17. Leger S, Zwanenburg A, Leger K, et al. Comprehensive Analysis of Tumour Sub-Volumes for Radiomic Risk Modelling in Locally Advanced HNSCC. Cancers (Basel). 2020;12(10):3047.
• 18. Wu W, Ye J, Wang Q, Luo J, Xu S. CT-Based Radiomics Signature for the Preoperative Discrimination Between Head and Neck Squamous Cell Carcinoma Grades. Front Oncol. 2019;9:821.
• 19. Zhang MH, Cao D, Ginat DT. Radiomic Model Predicts Lymph Node Response to Induction Chemotherapy in Locally Advanced Head and Neck Cancer. Diagnostics (Basel). 2021;11(4):588.
• 20. Zhai TT, Wesseling F, Langendijk JA, et al. External validation of nodal failure prediction models including radiomics in head and neck cancer. Oral Oncol. 2021;112:105083.
• 21. Huang C, Cintra M, Brennan K, et al. Development and validation of radiomic signatures of head and neck squamous cell carcinoma molecular features and subtypes. EBioMedicine. 2019;45:70-80.
• 22. Zhu Y, Mohamed ASR, Lai SY, et al. Imaging-Genomic Study of Head and Neck Squamous Cell Carcinoma: Associations Between Radiomic Phenotypes and Genomic Mechanisms via Integration of The Cancer Genome Atlas and The Cancer Imaging Archive. JCO Clin Cancer Inform. 2019;3:1-9.
• 23. Leijenaar RT, Carvalho S, Hoebers FJ, et al. External validation of a prognostic CT-based radiomic signature in oropharyngeal squamous cell carcinoma. Acta Oncol. 2015;54(9):1423-9.
• 24. Vallières M, Kay-Rivest E, Perrin LJ, et al. Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer. Sci Rep. 2017;7(1):10117.
• 25. Bogowicz M, Riesterer O, Ikenberg K, et al. Computed Tomography Radiomics Predicts HPV Status and Local Tumor Control After Definitive Radiochemotherapy in Head and Neck Squamous Cell Carcinoma. Int J Radiat Oncol Biol Phys. 2017;99(4):921-28.
• 26. Parmar C, Leijenaar RT, Grossmann P, et al. Radiomic feature clusters and prognostic signatures specific for Lung and Head & Neck cancer. Sci Rep. 2015;5:11044.
• 27. Grossberg A EH, Mohamed A, et al. M.D. Anderson Cancer Center Head and Neck Quantitative Imaging Working Group. HNSCC (Dataset). The Cancer Imaging Archive. 2020.
• 28. Grossberg AJ, Mohamed ASR, Elhalawani H, et al. Imaging and clinical data archive for head and neck squamous cell carcinoma patients treated with radiotherapy. Sci Data. 2018;5:180173.
• 29. Clark K, Vendt B, Smith K, et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging. 2013;26(6):1045-57.
• 30. Teicher BA. Hypoxia and drug resistance. Cancer Metastasis Rev. 1994;13(2):139-68.
• 31. Janssen HL, Haustermans KM, Balm AJ, Begg AC. Hypoxia in head and neck cancer: how much, how important? Head Neck. 2005;27(7):622-38.
• 32. Dua B, Chufal KS, Bhatnagar A, Thakwani A. Nodal volume as a prognostic factor in locally advanced head and neck cancer: Identifying candidates for elective neck dissection after chemoradiation with IGRT from a single institutional prospective series from the Indian subcontinent. Oral Oncol. 2018;87:179-85.
• 33. Mukherjee P, Cintra M, Huang C, et al. CT-based Radiomic Signatures for Predicting Histopathologic Features in Head and Neck Squamous Cell Carcinoma. Radiol Imaging Cancer. 2020;2(3):e190039.
• 34. Seidler M, Forghani B, Reinhold C, et al. Dual-Energy CT Texture Analysis With Machine Learning for the Evaluation and Characterization of Cervical Lymphadenopathy. Comput Struct Biotechnol J. 2019;17:1009- 15.
• 35. Kuno H, Garg N, Qureshi MM, et al. CT Texture Analysis of Cervical Lymph Nodes on Contrast-Enhanced [(18)F] FDG-PET/CT Images to Differentiate Nodal Metastases from Reactive Lymphadenopathy in HIV- Positive Patients with Head and Neck Squamous Cell Carcinoma. AJNR Am J Neuroradiol. 2019;40(3):543-50.
• 36. Forghani R, Chatterjee A, Reinhold C, et al. Head and neck squamous cell carcinoma: prediction of cervical lymph node metastasis by dual-energy CT texture analysis with machine learning. Eur Radiol. 2019;29(11):6172-81.