These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

140 related articles for article (PubMed ID: 39369120)

  • 1. Computed Tomography-Based Radiomics with Machine Learning Outperforms Radiologist Assessment in Estimating Colorectal Liver Metastases Pathologic Response After Chemotherapy.
    Karagkounis G; Horvat N; Danilova S; Chhabra S; Narayan RR; Barekzai AB; Kleshchelski A; Joanne C; Gonen M; Balachandran V; Soares KC; Wei AC; Kingham TP; Jarnagin WR; Shia J; Chakraborty J; D'Angelica MI
    Ann Surg Oncol; 2024 Dec; 31(13):9196-9204. PubMed ID: 39369120
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Machine learning and radiomics analysis by computed tomography in colorectal liver metastases patients for RAS mutational status prediction.
    Granata V; Fusco R; Setola SV; Brunese MC; Di Mauro A; Avallone A; Ottaiano A; Normanno N; Petrillo A; Izzo F
    Radiol Med; 2024 Jul; 129(7):957-966. PubMed ID: 38761342
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Exploring tumor heterogeneity in colorectal liver metastases by imaging: Unsupervised machine learning of preoperative CT radiomics features for prognostic stratification.
    Wang Q; Nilsson H; Xu K; Wei X; Chen D; Zhao D; Hu X; Wang A; Bai G
    Eur J Radiol; 2024 Jun; 175():111459. PubMed ID: 38636408
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Deep learning-based radiomics predicts response to chemotherapy in colorectal liver metastases.
    Wei J; Cheng J; Gu D; Chai F; Hong N; Wang Y; Tian J
    Med Phys; 2021 Jan; 48(1):513-522. PubMed ID: 33119899
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Association of computed tomography morphologic criteria with pathologic response and survival in patients treated with bevacizumab for colorectal liver metastases.
    Chun YS; Vauthey JN; Boonsirikamchai P; Maru DM; Kopetz S; Palavecino M; Curley SA; Abdalla EK; Kaur H; Charnsangavej C; Loyer EM
    JAMA; 2009 Dec; 302(21):2338-44. PubMed ID: 19952320
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Predicting response to neoadjuvant chemotherapy for colorectal liver metastasis using deep learning on prechemotherapy cross-sectional imaging.
    Davis JMK; Niazi MKK; Ricker AB; Tavolara TE; Robinson JN; Annanurov B; Smith K; Mantha R; Hwang J; Shrestha R; Iannitti DA; Martinie JB; Baker EH; Gurcan MN; Vrochides D
    J Surg Oncol; 2024 Jul; 130(1):93-101. PubMed ID: 38712939
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Baseline hepatobiliary MRI for predicting chemotherapeutic response and prognosis in initially unresectable colorectal cancer liver metastases.
    Chen Y; Lu T; Zhang Y; Li H; Xu J; Li M
    Abdom Radiol (NY); 2024 Aug; 49(8):2585-2594. PubMed ID: 39034308
    [TBL] [Abstract][Full Text] [Related]  

  • 8. CT-based radiomics for the identification of colorectal cancer liver metastases sensitive to first-line irinotecan-based chemotherapy.
    Qi W; Yang J; Zheng L; Lu Y; Liu R; Ju Y; Niu T; Wang D
    Med Phys; 2023 May; 50(5):2705-2714. PubMed ID: 36841949
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Radiomics Texture Analysis for the Identification of Colorectal Liver Metastases Sensitive to First-Line Oxaliplatin-Based Chemotherapy.
    Nakanishi R; Oki E; Hasuda H; Sano E; Miyashita Y; Sakai A; Koga N; Kuriyama N; Nonaka K; Fujimoto Y; Jogo T; Hokonohara K; Hu Q; Hisamatsu Y; Ando K; Kimura Y; Yoshizumi T; Mori M
    Ann Surg Oncol; 2021 Jun; 28(6):2975-2985. PubMed ID: 33454878
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Independent validation of CT radiomics models in colorectal liver metastases: predicting local tumour progression after ablation.
    van der Reijd DJ; Guerendel C; Staal FCR; Busard MP; De Oliveira Taveira M; Klompenhouwer EG; Kuhlmann KFD; Moelker A; Verhoef C; Starmans MPA; Lambregts DMJ; Beets-Tan RGH; Benson S; Maas M
    Eur Radiol; 2024 Jun; 34(6):3635-3643. PubMed ID: 37987835
    [TBL] [Abstract][Full Text] [Related]  

  • 11. CT-Based Radiomics Analysis Before Thermal Ablation to Predict Local Tumor Progression for Colorectal Liver Metastases.
    Taghavi M; Staal F; Gomez Munoz F; Imani F; Meek DB; Simões R; Klompenhouwer LG; van der Heide UA; Beets-Tan RGH; Maas M
    Cardiovasc Intervent Radiol; 2021 Jun; 44(6):913-920. PubMed ID: 33506278
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A combined model using pre-treatment CT radiomics and clinicopathological features of non-small cell lung cancer to predict major pathological responses after neoadjuvant chemoimmunotherapy.
    Wang F; Yang H; Chen W; Ruan L; Jiang T; Cheng L; Jiang H; Fang M
    Curr Probl Cancer; 2024 Jun; 50():101098. PubMed ID: 38704949
    [TBL] [Abstract][Full Text] [Related]  

  • 13. CT radiomics models are unable to predict new liver metastasis after successful thermal ablation of colorectal liver metastases.
    Taghavi M; Staal FC; Simões R; Hong EK; Lambregts DM; van der Heide UA; Beets-Tan RG; Maas M
    Acta Radiol; 2023 Jan; 64(1):5-12. PubMed ID: 34918955
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Interobserver Variability in CT-based Morphologic Tumor Response Assessment of Colorectal Liver Metastases.
    Wesdorp NJ; Kemna R; Bolhuis K; van Waesberghe JHTM; Nota IMGC; Struik F; Oulad Abdennabi I; Phoa SSKS; van Dieren S; van Amerongen MJ; Chapelle T; Dejong CHC; Engelbrecht MRW; Gerhards MF; Grünhagen D; van Gulik TM; Hermans JJ; de Jong KP; Klaase JM; Liem MSL; van Lienden KP; Molenaar IQ; Patijn GA; Rijken AM; Ruers TM; Verhoef C; de Wilt JHW; Swijnenburg RJ; Punt CJA; Huiskens J; Stoker J; Kazemier G;
    Radiol Imaging Cancer; 2022 May; 4(3):e210105. PubMed ID: 35522139
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Response evaluation in patients with colorectal liver metastases: RECIST version 1.1 versus modified CT criteria.
    Chung WS; Park MS; Shin SJ; Baek SE; Kim YE; Choi JY; Kim MJ
    AJR Am J Roentgenol; 2012 Oct; 199(4):809-15. PubMed ID: 22997372
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Optimizing the radiomics-machine-learning model based on non-contrast enhanced CT for the simplified risk categorization of thymic epithelial tumors: A large cohort retrospective study.
    Feng XL; Wang SZ; Chen HH; Huang YX; Xin YK; Zhang T; Cheng DL; Mao L; Li XL; Liu CX; Hu YC; Wang W; Cui GB; Nan HY
    Lung Cancer; 2022 Apr; 166():150-160. PubMed ID: 35287067
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Radiologic and pathologic response to neoadjuvant chemotherapy predicts survival in patients undergoing the liver-first approach for synchronous colorectal liver metastases.
    Berardi G; De Man M; Laurent S; Smeets P; Tomassini F; Ariotti R; Hoorens A; van Dorpe J; Varin O; Geboes K; Troisi RI
    Eur J Surg Oncol; 2018 Jul; 44(7):1069-1077. PubMed ID: 29615295
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Texture features of colorectal liver metastases on pretreatment contrast-enhanced CT may predict response and prognosis in patients treated with bevacizumab-containing chemotherapy: a pilot study including comparison with standard chemotherapy.
    Ravanelli M; Agazzi GM; Tononcelli E; Roca E; Cabassa P; Baiocchi G; Berruti A; Maroldi R; Farina D
    Radiol Med; 2019 Sep; 124(9):877-886. PubMed ID: 31172448
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Radiomics and machine learning for renal tumor subtype assessment using multiphase computed tomography in a multicenter setting.
    Uhlig A; Uhlig J; Leha A; Biggemann L; Bachanek S; Stöckle M; Reichert M; Lotz J; Zeuschner P; Maßmann A
    Eur Radiol; 2024 Oct; 34(10):6254-6263. PubMed ID: 38634876
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Machine learning-based analysis of CT radiomics model for prediction of colorectal metachronous liver metastases.
    Taghavi M; Trebeschi S; Simões R; Meek DB; Beckers RCJ; Lambregts DMJ; Verhoef C; Houwers JB; van der Heide UA; Beets-Tan RGH; Maas M
    Abdom Radiol (NY); 2021 Jan; 46(1):249-256. PubMed ID: 32583138
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.