Elisa Scalco1, Simona Marzi2, Giuseppe Sanguineti3, Antonello Vidiri4, Giovanna Rizzo5. 1. Institute of Molecular Bioimaging and Physiology (IBFM), CNR, Segrate (MI), Italy. Electronic address: elisa.scalco@ibfm.cnr.it. 2. Medical Physics Laboratory, Regina Elena National Cancer Institute, Rome, Italy. 3. Department of Radiotherapy, Regina Elena National Cancer Institute, Rome, Italy. 4. Radiology and Diagnostic Imaging Department, Regina Elena National Cancer Institute, Rome, Italy. 5. Institute of Molecular Bioimaging and Physiology (IBFM), CNR, Segrate (MI), Italy.
Abstract
PURPOSE: In the treatment of Head-and-Neck Squamous Cell Carcinoma (HNSCC), the early prediction of residual malignant lymph nodes (LNs) is currently required. Here, we investigated the potential of a multi-modal characterization (combination of CT, T2w-MRI and DW-MRI) at baseline and at mid-treatment, based on texture analysis (TA), for the early prediction of LNs response to chemo-radiotherapy (CRT). METHODS: 30 patients with pathologically confirmed HNSCC treated with CRT were considered. All patients underwent a planning CT and two serial MR examinations (including T2w and DW images), one before and one at mid-CRT. For each patient the largest malignant LN was selected and within each LN, morphological and textural features were estimated from T2w-MRI and CT, besides a quantification of the apparent diffusion coefficient (ADC) from DW-MRI. After a median follow-up time of 26.6months, 19 LNs showed regional control, while 11 LNs showedregional failure at a median time of 4.6months. Linear discriminant analysis was used to test the accuracy of the image-based features in predicting the final response. RESULTS: Pre-treatment features showed higher predictive power than mid-CRT features, the ADC having the highest accuracy (80%); CT-based indices were found not predictive. When ADC was combined with TA, the classification performance increased (accuracy=82.8%). If only T2w-MRI features were considered, the best combination of pre-CRT indices and their variation reached an equivalent accuracy (81.8%). CONCLUSION: Our results may suggest that TA on T2w-MRI and ADC can be combined together to obtain a more accurate prediction of response to CRT.
PURPOSE: In the treatment of Head-and-Neck Squamous Cell Carcinoma (HNSCC), the early prediction of residual malignant lymph nodes (LNs) is currently required. Here, we investigated the potential of a multi-modal characterization (combination of CT, T2w-MRI and DW-MRI) at baseline and at mid-treatment, based on texture analysis (TA), for the early prediction of LNs response to chemo-radiotherapy (CRT). METHODS: 30 patients with pathologically confirmed HNSCC treated with CRT were considered. All patients underwent a planning CT and two serial MR examinations (including T2w and DW images), one before and one at mid-CRT. For each patient the largest malignant LN was selected and within each LN, morphological and textural features were estimated from T2w-MRI and CT, besides a quantification of the apparent diffusion coefficient (ADC) from DW-MRI. After a median follow-up time of 26.6months, 19 LNs showed regional control, while 11 LNs showedregional failure at a median time of 4.6months. Linear discriminant analysis was used to test the accuracy of the image-based features in predicting the final response. RESULTS: Pre-treatment features showed higher predictive power than mid-CRT features, the ADC having the highest accuracy (80%); CT-based indices were found not predictive. When ADC was combined with TA, the classification performance increased (accuracy=82.8%). If only T2w-MRI features were considered, the best combination of pre-CRT indices and their variation reached an equivalent accuracy (81.8%). CONCLUSION: Our results may suggest that TA on T2w-MRI and ADC can be combined together to obtain a more accurate prediction of response to CRT.
Authors: Maryam Gul; Kimberley-Jane C Bonjoc; David Gorlin; Chi Wah Wong; Amirah Salem; Vincent La; Aleksandr Filippov; Abbas Chaudhry; Muhammad H Imam; Ammar A Chaudhry Journal: Front Oncol Date: 2021-07-07 Impact factor: 6.244
Authors: Wolfgang A G Sauerwein; Lucie Sancey; Evamarie Hey-Hawkins; Martin Kellert; Luigi Panza; Daniela Imperio; Marcin Balcerzyk; Giovanna Rizzo; Elisa Scalco; Ken Herrmann; PierLuigi Mauri; Antonella De Palma; Andrea Wittig Journal: Life (Basel) Date: 2021-04-10
Authors: Amit Jethanandani; Timothy A Lin; Stefania Volpe; Hesham Elhalawani; Abdallah S R Mohamed; Pei Yang; Clifton D Fuller Journal: Front Oncol Date: 2018-05-14 Impact factor: 6.244