Hungarian
Albanian
Arabic
Armenian
Azerbaijani
Belarusian
Bengali
Bosnian
Catalan
Czech
Danish
Deutsch
Dutch
English
Estonian
Finnish
Français
Greek
Haitian Creole
Hebrew
Hindi
Hungarian
Icelandic
Indonesian
Irish
Italian
Japanese
Korean
Latvian
Lithuanian
Macedonian
Mongolian
Norwegian
Persian
Polish
Portuguese
Romanian
Russian
Serbian
Slovak
Slovenian
Spanish
Swahili
Swedish
Turkish
Ukrainian
Vietnamese
Български
中文(简体)
中文(繁體)
European Radiology 2020-Feb

SLIC-supervoxels-based response evaluation of osteosarcoma treated with neoadjuvant chemotherapy using multi-parametric MR imaging.

Csak regisztrált felhasználók fordíthatnak cikkeket
Belépés Regisztrálás
A hivatkozás a vágólapra kerül
Esha Kayal
Devasenathipathy Kandasamy
Raju Sharma
Mehar Sharma
Sameer Bakhshi
Amit Mehndiratta

Kulcsszavak

Absztrakt

Histopathological examination (HPE) is the current gold standard for assessing chemotherapy response to tumor, but it is possible only after surgery. The purpose of the study was to develop a noninvasive, imaging-based robust method to delineate, visualize, and quantify the proportions of necrosis and viable tissue present within the tumor along with peritumoral edema before and after neoadjuvant chemotherapy (NACT) and to evaluate treatment response with correlation to HPE necrosis after surgery.The MRI dataset of 30 patients (N = 30; male:female = 24:6; age = 17.6 ± 2.7 years) with osteosarcoma was acquired using 1.5 T Philips Achieva MRI scanner before (baseline) and after 3 cycles of NACT (follow-up). After NACT, all patients underwent surgical resection followed by HPE. Simple linear iterative clustering supervoxels and Otsu multithresholding were combined to develop the proposed method-SLICs+MTh-to subsegment and quantify viable and nonviable regions within tumor using multiparametric MRI. Manually drawn ground-truth ROIs and SLICs+MTh-based segmentation of tumor, edema, and necrosis were compared using Jacquard index (JI), Dice coefficient (DC), precision (P), and recall (R). Postcontrast T1W images (PC-T1W) were used to validate the SLICs+MTh-based necrosis. SLICs+MTh-based necrosis volume at follow-up was compared with HPE necrosis using paired t test (p ≤ 0.05).Active tumor, necrosis, and edema were segmented with moderate to satisfactory accuracy (JI = 62-78%; DC = 72-87%; P = 67-87%; R = 63-88%). Qualitatively and quantitatively (DC = 74 ± 9%), the SLICs+MTh-based necrosis area correlated well with the hypointense necrosis areas in PC-T1W. No significant difference (paired t test, p = 0.26; Bland-Altman plot, bias = 2.47) between SLICs+MTh-based necrosis at follow-up and HPE necrosis was observed.The proposed multiparametric MRI-based SLICs+MTh method performs noninvasive assessment of NACT response in osteosarcoma that may improve cancer treatment monitoring, planning, and overall prognosis.• The simple linear iterative clustering supervoxels and Otsu multithresholding-based technique (SLICs+MTh) successfully estimates the proportion of necrosis, viable tumor, and edema in osteosarcoma in the course of chemotherapy. • The proposed technique is noninvasive and uses multiparametric MRI to measure necrosis as an indication of anticancer treatment response. • SLICs+MTh-based necrosis was in satisfactory agreement with histological necrosis after surgery.

Csatlakozzon
facebook oldalunkhoz

A legteljesebb gyógynövény-adatbázis, amelyet a tudomány támogat

  • Működik 55 nyelven
  • A tudomány által támogatott gyógynövényes kúrák
  • Gyógynövények felismerése kép alapján
  • Interaktív GPS térkép - jelölje meg a gyógynövényeket a helyszínen (hamarosan)
  • Olvassa el a keresésével kapcsolatos tudományos publikációkat
  • Keresse meg a gyógynövényeket hatásuk szerint
  • Szervezze meg érdeklődését, és naprakész legyen a hírkutatással, a klinikai vizsgálatokkal és a szabadalmakkal

Írjon be egy tünetet vagy betegséget, és olvassa el azokat a gyógynövényeket, amelyek segíthetnek, beírhat egy gyógynövényt, és megtekintheti azokat a betegségeket és tüneteket, amelyek ellen használják.
* Minden információ publikált tudományos kutatáson alapul

Google Play badgeApp Store badge