nach oben

Erschienen in:

01.09.2020 | Short Communication

Cone-beam computed tomography-based radiomics in prostate cancer: a mono-institutional study

verfasst von: Davide Giovanni Bosetti, MD, Lorenzo Ruinelli, Maria Antonietta Piliero, PhD, Prof Linda Christina van der Gaag, Gianfranco Angelo Pesce, MD, Mariacarla Valli, MD, Marco Bosetti, Stefano Presilla, MP, Antonella Richetti, MD, Letizia Deantonio, MD

Erschienen in: Strahlentherapie und Onkologie | Ausgabe 10/2020

Einloggen, um Zugang zu erhalten

Abstract

Purpose

The purpose of the reported study was to investigate the value of cone-beam computed tomography (CBCT)-based radiomics for risk stratification and prediction of biochemical relapse in prostate cancer.

Methods

The study population consisted of 31 prostate cancer patients. Radiomics features were extracted from weekly CBCT scans performed for verifying treatment position. From the data, logistic-regression models were learned for establishing tumor stage, Gleason score, level of prostate-specific antigen, and risk stratification, and for predicting biochemical recurrence. Performance of the learned models was assessed using the area under the receiver operating characteristic curve (AUC-ROC) or the area under the precision-recall curve (AUC-PRC).

Results

Results suggest that the histogram-based Energy and Kurtosis features and the shape-based feature representing the standard deviation of the maximum diameter of the prostate gland during treatment are predictive of biochemical relapse and indicative of patients at high risk.

Conclusion

Our results suggest the usefulness of CBCT-based radiomics for treatment definition in prostate cancer.

D’Amico AV, Moul J, Carroll PR et al (2003) Cancer-specific mortality after surgery or radiation for patients with clinically localized prostate cancer managed during the prostate-specific antigen era. J Clin Oncol 21:2163–2172CrossRef

Traverso A, Wee L, Dekker A, Gillies R (2018) Repeatability and reproducibility of radiomic features: a systematic review. Int J Radiat Oncol Biol Phys 102:1143–1158CrossRef

Gardin I, Gregoire V, Gibon D et al (2019) Radiomics: principles and radiotherapy applications. Crit Rev Oncol Hematol 138:44–50CrossRef

Coroller TP, Agrawal V, Huynh E et al (2017) Radiomic-based pathological response prediction from primary tumors and lymph nodes in NSCLC. J Thorac Oncol 12:467–476CrossRef

Scalco E, Rizzo G (2017) Texture analysis of medical images for radiotherapy applications. Br J Radiol 90:1070CrossRef

Gnep K, Fargeas A, Gutierrez-Carvajal RE et al (2017) Haralick textural features on T2-weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer. J Magn Reson Imaging 45:103–117CrossRef

Liu J, Mao Y, Li Z et al (2016) Use of texture analysis based on contrast-enhanced MRI to predict treatment response to chemoradiotherapy in nasopharyngeal carcinoma. J Magn Reson Imaging 44:445–455CrossRef

Yip SS, Aerts HJ (2016) Applications and limitations of radiomics. Phys Med Biol 61:R150–R166CrossRef

Baeßler B, Weiss K, Santos DDP (2019) Robustness and reproducibility of radiomics in magnetic resonance imaging: a phantom study. Invest Radiol 54:221–228CrossRef

10.

Tanadini-Lang S, Bogowicz M, Veit-Haibach P et al (2018) Exploratory radiomics in computed tomography perfusion of prostate cancer. Anticancer Res 38:685–690PubMed

11.

Coates J, El Naqa I (2016) Outcome modeling techniques for prostate cancer radiotherapy: Data, models, and validation. Phys Med 32:512–520CrossRef

12.

Van Timmeren JE, Leijenaar RTH, van Elmpt W et al (2017) Feature selection methodology for longitudinal cone-beam CT radiomics. Acta Oncol 56:1537–1543CrossRef

13.

Amin MB, Edge S, Greene F et al (2017) AJCC Cancer Staging Manual, 8th edn. Springer, BerlinCrossRef

14.

Mohler JL, Antonarakis ES, Armstrong AJ et al (2019) Prostate cancer, version 2.2019, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw 17:479–505CrossRef

15.

Roach M 3rd, Hanks G, Thames H Jr, Schellhammer P, Shipley WU, Sokol GH, Sandler H (2006) Defining biochemical failure following radiotherapy with or without hormonal therapy in men with clinically localized prostate cancer: recommendations of the RTOG-ASTRO Phoenix Consensus Conference. Int J Radiat Oncol Biol Phys 65:965–974CrossRef

16.

Wei L, Rosen B, Vallières M et al (2019) Automatic recognition and analysis of metal streak artifacts in head and neck computed tomography for radiomics modeling. Phys Imaging Radiat Oncol 10:49–54CrossRef

17.

Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107CrossRef

18.

McBride GB (2005) A proposal for strength-of-agreement criteria for Lin’s concordance correlation coefficient. NIWA Client Report No. HAM2005–062 1‑14

19.

Fraser CG, Fogarty Y (1989) Interpreting laboratory results. BMJ 298(6689):1659–1660CrossRef

20.

Liu H, Hussain F, Lim C, Dash M (2002) Discretization: an enabling technique. Data Min Knowl Discov 6:393–423CrossRef

21.

Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830

22.

Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. Proc 14th Intl J Conf Artif Intell 2:1137–1143

23.

Salto T, Rehmsmeier M (2015) The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE 10:e118432CrossRef

24.

Nadeau C, Bengio Y (2003) Inference for the generalization error. Mach Learn 52:239–281CrossRef

25.

Fave X, Mackin D, Yang J et al (2015) Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer? Med Phys 42:6784–6797CrossRef

26.

van Timmeren JE, Leijenaar RT, van Elmpt W et al (2017) Survival prediction of non-small cell lung cancer patients using radiomics analyses of cone-beam CT images. Radiother Oncol 123:363–369CrossRef

27.

Osman SOS, Leijenaar RTH, Cole AJ et al (2019) Computed tomography-based radiomics for risk stratification in prostate cancer. Int J Radiat Oncol Biol Phys 105:448–456CrossRef

28.

Fehr D, Veeraraghavan H, Wibmer A et al (2015) Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images. Proc Natl Acad Sci 112:E6265–E6273CrossRef

29.

Peeken JC, Nüsslin F, Combs SE (2017) “Radio-oncomics” : The potential of radiomics in radiation oncology. Strahlenther Onkol 193:767–779CrossRef

30.

Zhong QZ, Long LH, Liu A et al (2020) Radiomics of multiparametric MRI to predict biochemical recurrence of localized prostate cancer after radiation therapy. Front Oncol 10:731CrossRef

31.

Jacobs BL, Zhang Y, Skolarus TA et al (2014) Comparative effectiveness of external-beam radiation approaches for prostate cancer. Eur Urol 65:162–168CrossRef

32.

Turkbey B, Mani H, Shah V et al (2011) Multiparametric 3T prostate magnetic resonance imaging to detect cancer: histopathological correlation using prostatectomy specimens processed in customized magnetic resonance imaging-based molds. J Urol 186:1818–1824CrossRef

33.

Kuban DA, Tucker SL, Dong L et al (2008) Long-term results of the M. D. Anderson randomized dose-escalation trial for prostate cancer. Int J Radiat Oncol Biol Phys 70:67–74CrossRef

Titel: Cone-beam computed tomography-based radiomics in prostate cancer: a mono-institutional study
verfasst von: Davide Giovanni Bosetti, MD
Lorenzo Ruinelli
Maria Antonietta Piliero, PhD
Prof Linda Christina van der Gaag
Gianfranco Angelo Pesce, MD
Mariacarla Valli, MD
Marco Bosetti
Stefano Presilla, MP
Antonella Richetti, MD
Letizia Deantonio, MD
Publikationsdatum: 01.09.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: Strahlentherapie und Onkologie / Ausgabe 10/2020
Print ISSN: 0179-7158
Elektronische ISSN: 1439-099X
DOI: https://doi.org/10.1007/s00066-020-01677-x

Neu im Fachgebiet Onkologie

18.04.2024 | Wirbelsäulenmetastase | Nachrichten

Live-Webinar: Aktuelle Leitlinien bei Herz-Kreislauf-Erkrankungen

Springer Medizin

Cone-beam computed tomography-based radiomics in prostate cancer: a mono-institutional study

Abstract

Purpose

Methods

Results

Conclusion

Neu im Fachgebiet Onkologie

Stereotaktische Radiatio schlägt konventionelle Bestrahlung

Adjuvante Brustkrebstherapie: Doppelt hält besser

Manche Gestagene können das Risiko für Meningeome erhöhen

Einladung zum PSA-Screening senkt die Krebssterblichkeit

Update Onkologie

Live-Webinar: Aktuelle Leitlinien bei Herz-Kreislauf-Erkrankungen

Springer Medizin

Abstract

Purpose

Methods

Results

Conclusion

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Weitere Artikel der Ausgabe 10/2020

A radiomic approach to predicting nodal relapse and disease-specific survival in patients treated with stereotactic body radiation therapy for early-stage non-small cell lung cancer.

Applications of radiomics and machine learning for radiotherapy of malignant brain tumors

Segmentation of prostate and prostate zones using deep learning

Die Herzmortalität nach Strahlentherapie, Chemotherapie und endokriner Therapie des Mammakarzinoms

Magnetic resonance imaging-based radiomic features for extrapolating infiltration levels of immune cells in lower-grade gliomas

Radiomics in radiation oncology—basics, methods, and limitations

Neu im Fachgebiet Onkologie

Stereotaktische Radiatio schlägt konventionelle Bestrahlung

Adjuvante Brustkrebstherapie: Doppelt hält besser

Manche Gestagene können das Risiko für Meningeome erhöhen

Einladung zum PSA-Screening senkt die Krebssterblichkeit

Update Onkologie