366 related articles for article (PubMed ID: 35325372)
1. Radiomics-based prediction of two-year clinical outcome in locally advanced cervical cancer patients undergoing neoadjuvant chemoradiotherapy.
Autorino R; Gui B; Panza G; Boldrini L; Cusumano D; Russo L; Nardangeli A; Persiani S; Campitelli M; Ferrandina G; Macchia G; Valentini V; Gambacorta MA; Manfredi R
Radiol Med; 2022 May; 127(5):498-506. PubMed ID: 35325372
[TBL] [Abstract][Full Text] [Related]
2. [A prediction model of pathological complete response in patients with locally advanced rectal cancer after PD-1 antibody combined with total neoadjuvant chemoradiotherapy based on MRI radiomics].
Zhang XY; Zhu HT; Li XT; Li YJ; Li ZW; Wang WH; Wu AW; Sun YS; Zhang L
Zhonghua Wei Chang Wai Ke Za Zhi; 2022 Mar; 25(3):228-234. PubMed ID: 35340172
[No Abstract] [Full Text] [Related]
3. Is PET Radiomics Useful to Predict Pathologic Tumor Response and Prognosis in Locally Advanced Cervical Cancer?
Collarino A; Feudo V; Pasciuto T; Florit A; Pfaehler E; de Summa M; Bizzarri N; Annunziata S; Zannoni GF; de Geus-Oei LF; Ferrandina G; Gambacorta MA; Scambia G; Boellaard R; Sala E; Rufini V; van Velden FH
J Nucl Med; 2024 Jun; 65(6):962-970. PubMed ID: 38548352
[TBL] [Abstract][Full Text] [Related]
4. Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study.
Sun C; Tian X; Liu Z; Li W; Li P; Chen J; Zhang W; Fang Z; Du P; Duan H; Liu P; Wang L; Chen C; Tian J
EBioMedicine; 2019 Aug; 46():160-169. PubMed ID: 31395503
[TBL] [Abstract][Full Text] [Related]
5. Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer.
Cui Y; Yang X; Shi Z; Yang Z; Du X; Zhao Z; Cheng X
Eur Radiol; 2019 Mar; 29(3):1211-1220. PubMed ID: 30128616
[TBL] [Abstract][Full Text] [Related]
6. Radiomics-Based Pretherapeutic Prediction of Non-response to Neoadjuvant Therapy in Locally Advanced Rectal Cancer.
Zhou X; Yi Y; Liu Z; Cao W; Lai B; Sun K; Li L; Zhou Z; Feng Y; Tian J
Ann Surg Oncol; 2019 Jun; 26(6):1676-1684. PubMed ID: 30887373
[TBL] [Abstract][Full Text] [Related]
7. Pretreatment MRI Radiomics Based Response Prediction Model in Locally Advanced Cervical Cancer.
Gui B; Autorino R; Miccò M; Nardangeli A; Pesce A; Lenkowicz J; Cusumano D; Russo L; Persiani S; Boldrini L; Dinapoli N; Macchia G; Sallustio G; Gambacorta MA; Ferrandina G; Manfredi R; Valentini V; Scambia G
Diagnostics (Basel); 2021 Mar; 11(4):. PubMed ID: 33807494
[TBL] [Abstract][Full Text] [Related]
8. Multi-modal radiomics model to predict treatment response to neoadjuvant chemotherapy for locally advanced rectal cancer.
Li ZY; Wang XD; Li M; Liu XJ; Ye Z; Song B; Yuan F; Yuan Y; Xia CC; Zhang X; Li Q
World J Gastroenterol; 2020 May; 26(19):2388-2402. PubMed ID: 32476800
[TBL] [Abstract][Full Text] [Related]
9. A Predictive Model of 2yDFS During MR-Guided RT Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Patients.
Chiloiro G; Boldrini L; Preziosi F; Cusumano D; Yadav P; Romano A; Placidi L; Lenkowicz J; Dinapoli N; Bassetti MF; Gambacorta MA; Valentini V
Front Oncol; 2022; 12():831712. PubMed ID: 35280799
[TBL] [Abstract][Full Text] [Related]
10. Evaluation of early regression index as response predictor in cervical cancer: A retrospective study on T2 and DWI MR images.
Cusumano D; Russo L; Gui B; Autorino R; Boldrini L; D'Erme L; Persiani S; Catucci F; Broggi S; Panza G; Nardangeli A; Campitelli M; Ferrandina G; Macchia G; Fiorino C; Valentini V; Scambia G; Manfredi R; Gambacorta MA
Radiother Oncol; 2022 Sep; 174():30-36. PubMed ID: 35811004
[TBL] [Abstract][Full Text] [Related]
11. A multiple-time-scale comparative study for the added value of magnetic resonance imaging-based radiomics in predicting pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer.
Peng W; Wan L; Wang S; Zou S; Zhao X; Zhang H
Front Oncol; 2023; 13():1234619. PubMed ID: 37664046
[TBL] [Abstract][Full Text] [Related]
12. Delta radiomics analysis for prediction of intermediary- and high-risk factors for patients with locally advanced cervical cancer receiving neoadjuvant therapy.
Wu RR; Zhou YM; Xie XY; Chen JY; Quan KR; Wei YT; Xia XY; Chen WJ
Sci Rep; 2023 Nov; 13(1):19409. PubMed ID: 37938596
[TBL] [Abstract][Full Text] [Related]
13. Machine learning-based radiomics for predicting outcomes in cervical cancer patients undergoing concurrent chemoradiotherapy.
Xin W; Rixin S; Linrui L; Zhihui Q; Long L; Yu Z
Comput Biol Med; 2024 Jul; 177():108593. PubMed ID: 38801795
[TBL] [Abstract][Full Text] [Related]
14. Development and validation of an MRI-based radiomic nomogram to distinguish between good and poor responders in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiotherapy.
Wang J; Liu X; Hu B; Gao Y; Chen J; Li J
Abdom Radiol (NY); 2021 May; 46(5):1805-1815. PubMed ID: 33151359
[TBL] [Abstract][Full Text] [Related]
15. Pre-treatment prediction of early response to chemoradiotherapy by quantitative analysis of baseline staging FDG-PET/CT and MRI in locally advanced cervical cancer.
Min LA; Ackermans LL; Nowee ME; Griethuysen JJV; Roberti S; Maas M; Vogel WV; Beets-Tan RG; Lambregts DM
Acta Radiol; 2021 Jul; 62(7):940-948. PubMed ID: 32722967
[TBL] [Abstract][Full Text] [Related]
16. External validation of a combined PET and MRI radiomics model for prediction of recurrence in cervical cancer patients treated with chemoradiotherapy.
Lucia F; Visvikis D; Vallières M; Desseroit MC; Miranda O; Robin P; Bonaffini PA; Alfieri J; Masson I; Mervoyer A; Reinhold C; Pradier O; Hatt M; Schick U
Eur J Nucl Med Mol Imaging; 2019 Apr; 46(4):864-877. PubMed ID: 30535746
[TBL] [Abstract][Full Text] [Related]
17. Prediction of outcome using pretreatment
Lucia F; Visvikis D; Desseroit MC; Miranda O; Malhaire JP; Robin P; Pradier O; Hatt M; Schick U
Eur J Nucl Med Mol Imaging; 2018 May; 45(5):768-786. PubMed ID: 29222685
[TBL] [Abstract][Full Text] [Related]
18. MRI-based radiomics value for predicting the survival of patients with locally advanced cervical squamous cell cancer treated with concurrent chemoradiotherapy.
Zhang X; Zhao J; Zhang Q; Wang S; Zhang J; An J; Xie L; Yu X; Zhao X
Cancer Imaging; 2022 Jul; 22(1):35. PubMed ID: 35842679
[TBL] [Abstract][Full Text] [Related]
19. Prediction of out-of-field recurrence after chemoradiotherapy for cervical cancer using a combination model of clinical parameters and magnetic resonance imaging radiomics: a multi-institutional study of the Japanese Radiation Oncology Study Group.
Ikushima H; Haga A; Ando K; Kato S; Kaneyasu Y; Uno T; Okonogi N; Yoshida K; Ariga T; Isohashi F; Harima Y; Kanemoto A; Ii N; Wakatsuki M; Ohno T
J Radiat Res; 2022 Jan; 63(1):98-106. PubMed ID: 34865079
[TBL] [Abstract][Full Text] [Related]
20. Optimisation and evaluation of the random forest model in the efficacy prediction of chemoradiotherapy for advanced cervical cancer based on radiomics signature from high-resolution T2 weighted images.
Liu D; Zhang X; Zheng T; Shi Q; Cui Y; Wang Y; Liu L
Arch Gynecol Obstet; 2021 Mar; 303(3):811-820. PubMed ID: 33394142
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]