Diagnostic efficacy of computed tomography histogram analysis for the differentiation of histopathological low- and high-grade tumors in colocteral carcinoma
Öz
Aim: Colorectal adenocarcinoma (CA) is the most common type of cancer worldwide and the third leading cause of cancer-related deaths. Primary pathological grade bears importance in the course of the disease. The possibility of non-invasive grading through radiology modalities is still an important issue. The present study aims to reveal whether a non-invasive grading similar to pathological grading can be performed using histogram analysis on computed tomography (CT) scan images.
Material and Method: 58 patients operated and diagnosed with CA pathologically were included in the present study. As for medical protocol, abdominal intravenous contrast CT scan images obtained from TOSHIBA Alexion and TOSHIBA Aquilion ONE (Toshiba Medical Systems, Nasu, Japan) devices with 120 kVp tube voltage were set to a window width of 400 and a window level of 40. Patient images from retrospective scanning were evaluated on a workstation. For the evalution of mass, intraluminal air, necrotic areas, pericolonic fat tissue or intra-mass large feeding vessels were not included in the measurement range. Mass size was measured on the largest axis according to the longest axis. For histogram analysis, regions of interest were positioned. Parameters included in the histogram analysis were pixels, mean, standard deviation, minimum, maximum, median, variance, entropy, size L%, size U%, size M%, kurtosis, skewness, uniformity, percent01, percent03, percent05, percent10, percent25, percent75, percent90, percent95, percent97 and percentile 99.
Results: Histogram analysis results obtained from three different measurements for each of 58 patients were not found to be statistically significant in the differentation of pathologically defined histological grading system.
Conclusion: Although the use of a non-invasive method instead of an invasive one may offer an advantage, was not statistically significant in the prediction of histological grade.
Anahtar Kelimeler
Colorectal adenocarcinoma Histogram analysis Computed tomography Histological grade Texture analysis
Kaynakça
- Angius A, Uva P, Pira G, et al. Integrated Analysis of miRNA and mRNA Endorses a Twenty miRNAs Signature for Colorectal Carcinoma. Int J Mol Sci. 2019;20:4067.
- Athanasakis E, Xenaki S, Venianaki M, Chalkiadakis G, Chrysos E. Newly recognized extratumoral features of colorectal cancer challenge the current tumor-node-metastasis staging system. Ann Gastroenterol. 2018;31:525-34.
- Compton CC, Fielding LP, Burgart LJ, et al. Prognostic factors in colorectal cancer. College of American Pathologists Consensus Statement 1999. Arch Pathol Lab Med. 2000;124:979-94.
- Sagaert X, Vanstapel A, Verbeek S. Tumor heterogeneity in colorectal cancer: what do we know so far? Pathobiology. 2018;85:72-4.
- Huang Y, Liu Z, He L, et al. Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer. 2016.
- Miles KA, Ganeshan B, Hayball MP. CT texture analysis using the filtration-histogram method: what do the measurements mean? Cancer Imaging. 2013;13:400.
- Huang YQ, Liang CH, He L, et al. Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer. J Clin Oncol. 2016;34:2157-64.
- Andersen MB, Bodtger U, Andersen IR, Thorup KS, Ganeshan B, Rasmussen F. Metastases or benign adrenal lesions in patients with histopathological verification of lung cancer: Can CT texture analysis distinguish? European Journal of Radiology. 2021;138:109664.
- Ng F, Ganeshan B, Kozarski R, Miles KA, Goh V. Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology. 2013;266:177-84.
- Lubner MG, Stabo N, Lubner SJ, et al. CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes. Abdom Imaging. 2015;40:2331-37.
- Cao J, Wang GR, Wang ZW, Jin ZY. CT Texture Analysis: A Potential Biomarker for Evaluating KRAS Mutational Status in Colorectal Cancer. Chin Med Sci J. 2020;35:306-14.
- Liang P, Xu C, Tan F, et al. Prediction of the World Health Organization Grade of rectal neuroendocrine tumors based on CT histogram analysis. Cancer Med. 2021;10:595-04.
- Semenza GL. HIF-1 and tumor progression: pathophysiology and therapeutics. Trends Mol Med. 2002;8:62-7.
- Lunt SJ, Chaudary N, Hill RP. The tumor microenvironment and metastatic disease. Clin Exp Metastasis. 2009;26:19-34.
- Nelson DA, Tan TT, Rabson AB, Anderson D, Degenhardt K, White E. Hypoxia and defective apoptosis drive genomic instability and tumorigenesis. Genes Dev. 2004;18:2095-107.
- Ganeshan B, Abaleke S, Young RC, Chatwin CR, Miles KA. Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage. Cancer Imaging. 2010;10:137-43.
Kolokteral karsinomda histopatolojik düşük ve yüksek dereceli tümörlerin ayrımı için bilgisayarlı tomografi histogram analizinin tanısal etkinliği
Öz
Giriş ve amaç: Kolokteral adenokarsinom (KA), dünya çapında en yaygın kanser türüdür ve kansere bağlı ölümlerin üçüncü önde gelen nedenidir. Hastalığın seyrinde primer patolojik derece önem taşır. Radyoloji modaliteleri aracılığıyla non-invaziv derecelendirme olasılığı hala önemli bir konudur. Bu çalışma, bilgisayarlı tomografi (BT) tarama görüntülerinde histogram analizi kullanılarak patolojik derecelendirmeye benzer non-invaziv bir derecelendirmenin yapılıp yapılamayacağını ortaya koymayı amaçlamaktadır.
Gereç ve Yöntem: Ameliyat edilen ve patolojik olarak KA tanısı alan 58 hasta bu çalışmaya dahil edildi. Medikal protokol olarak TOSHIBA Alexion ve TOSHIBA Aquilion ONE (Toshiba Medical Systems, Nasu, Japan) cihazlarından alınan 120 kVp tüp voltajı ile abdominal intravenöz kontrastlı BT görüntüleri pencere genişliği 400 ve pencere seviyesi 40 olarak ayarlandı. Retrospektif taramadan alınan görüntüler bir iş istasyonunda değerlendirildi. Kitle değerlendirmesi için intraluminal hava, nekrotik alanlar, perikolonik yağ dokusu veya kitle içi büyük besleyici damarlar ölçüm aralığına dahil edilmedi. Kütle boyutu en uzun eksene göre en büyük eksende ölçülmüştür. Histogram analizi için ilgi bölgeleri konumlandırıldı. Histogram analizine dahil edilen parametreler piksel, ortalama, standart sapma, minimum, maksimum, medyan, varyans, entropi, %L boyutu, %U boyutu, %M boyutu, basıklık, çarpıklık, tekdüzelik, yüzde01, yüzde03, yüzde05, yüzde10, yüzde25, yüzde75, yüzde90, yüzde95, yüzde97 ve yüzdelik dilim 99.
Bulgular: 58 hastanın her biri için üç farklı ölçümden elde edilen histogram analiz sonuçları, patolojik olarak tanımlanan histolojik derecelendirme sisteminin farklılaşmasında istatistiksel olarak anlamlı bulunmadı.
Sonuç: İnvaziv yerine invaziv olmayan bir yöntemin kullanılması avantaj sağlasa da histolojik derecenin tahmininde istatistiksel olarak anlamlı değildi.
Anahtar Kelimeler
Kolokteral adenokarsinom histogram analizi bilgisayarlı tomografi histolojik derece doku analizi
Kaynakça
- Angius A, Uva P, Pira G, et al. Integrated Analysis of miRNA and mRNA Endorses a Twenty miRNAs Signature for Colorectal Carcinoma. Int J Mol Sci. 2019;20:4067.
- Athanasakis E, Xenaki S, Venianaki M, Chalkiadakis G, Chrysos E. Newly recognized extratumoral features of colorectal cancer challenge the current tumor-node-metastasis staging system. Ann Gastroenterol. 2018;31:525-34.
- Compton CC, Fielding LP, Burgart LJ, et al. Prognostic factors in colorectal cancer. College of American Pathologists Consensus Statement 1999. Arch Pathol Lab Med. 2000;124:979-94.
- Sagaert X, Vanstapel A, Verbeek S. Tumor heterogeneity in colorectal cancer: what do we know so far? Pathobiology. 2018;85:72-4.
- Huang Y, Liu Z, He L, et al. Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer. 2016.
- Miles KA, Ganeshan B, Hayball MP. CT texture analysis using the filtration-histogram method: what do the measurements mean? Cancer Imaging. 2013;13:400.
- Huang YQ, Liang CH, He L, et al. Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer. J Clin Oncol. 2016;34:2157-64.
- Andersen MB, Bodtger U, Andersen IR, Thorup KS, Ganeshan B, Rasmussen F. Metastases or benign adrenal lesions in patients with histopathological verification of lung cancer: Can CT texture analysis distinguish? European Journal of Radiology. 2021;138:109664.
- Ng F, Ganeshan B, Kozarski R, Miles KA, Goh V. Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology. 2013;266:177-84.
- Lubner MG, Stabo N, Lubner SJ, et al. CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes. Abdom Imaging. 2015;40:2331-37.
- Cao J, Wang GR, Wang ZW, Jin ZY. CT Texture Analysis: A Potential Biomarker for Evaluating KRAS Mutational Status in Colorectal Cancer. Chin Med Sci J. 2020;35:306-14.
- Liang P, Xu C, Tan F, et al. Prediction of the World Health Organization Grade of rectal neuroendocrine tumors based on CT histogram analysis. Cancer Med. 2021;10:595-04.
- Semenza GL. HIF-1 and tumor progression: pathophysiology and therapeutics. Trends Mol Med. 2002;8:62-7.
- Lunt SJ, Chaudary N, Hill RP. The tumor microenvironment and metastatic disease. Clin Exp Metastasis. 2009;26:19-34.
- Nelson DA, Tan TT, Rabson AB, Anderson D, Degenhardt K, White E. Hypoxia and defective apoptosis drive genomic instability and tumorigenesis. Genes Dev. 2004;18:2095-107.
- Ganeshan B, Abaleke S, Young RC, Chatwin CR, Miles KA. Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage. Cancer Imaging. 2010;10:137-43.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Sağlık Kurumları Yönetimi |
| Bölüm | Research Articles [en] Araştırma Makaleleri [tr] |
| Yazarlar | |
| Yayımlanma Tarihi | 27 Mart 2023 |
| Yayımlandığı Sayı | Yıl 2023 Cilt: 4 Sayı: 2 |
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