Below are the most recent publications written about "Machine Learning" by people in Profiles.
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Akbari H, Bakas S, Sako C, Fathi Kazerooni A, Villanueva-Meyer J, Garcia JA, Mamourian E, Liu F, Cao Q, Shinohara RT, Baid U, Getka A, Pati S, Singh A, Calabrese E, Chang S, Rudie J, Sotiras A, LaMontagne P, Marcus DS, Milchenko M, Nazeri A, Balana C, Capellades J, Puig J, Badve C, Barnholtz-Sloan JS, Sloan AE, Vadmal V, Waite K, Ak M, Colen RR, Park YW, Ahn SS, Chang JH, Choi YS, Lee SK, Alexander GS, Ali AS, Dicker AP, Flanders AE, Liem S, Lombardo J, Shi W, Shukla G, Griffith B, Poisson LM, Rogers LR, Kotrotsou A, Booth TC, Jain R, Lee M, Mahajan A, Chakravarti A, Palmer JD, DiCostanzo D, Fathallah-Shaykh H, Cepeda S, Santonocito OS, Di Stefano AL, Wiestler B, Melhem ER, Woodworth GF, Tiwari P, Valdes P, Matsumoto Y, Otani Y, Imoto R, Aboian M, Koizumi S, Kurozumi K, Kawakatsu T, Alexander K, Satgunaseelan L, Rulseh AM, Bagley SJ, Bilello M, Binder ZA, Brem S, Desai AS, Lustig RA, Maloney E, Prior T, Amankulor N, Nasrallah MP, O'Rourke DM, Mohan S, Davatzikos C. Machine learning-based prognostic subgrouping of glioblastoma: A multicenter study. Neuro Oncol. 2025 May 15; 27(4):1102-1115.
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Hamaya R, Hara K, Manson JE, Rimm EB, Sacks FM, Xue Q, Qi L, Cook NR. Machine-learning approaches to predict individualized treatment effect using a randomized controlled trial. Eur J Epidemiol. 2025 Feb; 40(2):151-166.
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Jones N, Shih MC, Healey E, Zhai CW, Advani S, Smith-McLallen A, Sontag D, Kanjilal S. Use of Machine Learning to Assess the Management of Uncomplicated Urinary Tract Infection. JAMA Netw Open. 2025 01 02; 8(1):e2456950.
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Hermans S, Hu Z, Ball RL, Lin HM, Prevedello LM, Berger FH, Yusuf I, Rudie JD, Vazirabad M, Flanders AE, Shih G, Mongan J, Nicolaou S, Marinelli BS, Davis MA, Magudia K, Sejdic E, Colak E. RSNA 2023 Abdominal Trauma AI Challenge: Review and Outcomes. Radiol Artif Intell. 2025 01; 7(1):e240334.
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Gerratana L, Davis AA, Foffano L, Reduzzi C, Rossi T, Medford A, Clifton K, Shah AN, Bucheit L, Velimirovic M, Bandini S, Dai CS, Wehbe F, Gradishar WJ, Behdad A, Ulivi P, Ma CX, Puglisi F, Bardia A, Cristofanilli M. Integrating machine learning-predicted circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) in metastatic breast cancer: A proof of principle study on endocrine resistance profiling. Cancer Lett. 2025 Jan 28; 609:217325.
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Harris L, Shankar LK, Hildebrandt C, Rubinstein WS, Langlais K, Rodriguez H, Berger A, Freymann J, Huang EP, Williams PM, Zenklusen JC, Ochs R, Tezak Z, Sahiner B. Resource requirements to accelerate clinical applications of next-generation sequencing and radiomics: workshop commentary and review. J Natl Cancer Inst. 2024 10 01; 116(10):1562-1570.
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Karathanasis N, Papasavva PL, Oulas A, Spyrou GM. Combining clinical and molecular data for personalized treatment in acute myeloid leukemia: A machine learning approach. Comput Methods Programs Biomed. 2024 Dec; 257:108432.
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Di Cosimo S, Silvestri M, De Marco C, Calzoni A, De Santis MC, Carnevale MG, Reduzzi C, Cristofanilli M, Cappelletti V. Low-pass whole genome sequencing of circulating tumor cells to evaluate chromosomal instability in triple-negative breast cancer. Sci Rep. 2024 09 03; 14(1):20479.
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Tabaie A, Tran A, Calabria T, Bennett SS, Milicia A, Weintraub W, Gallagher WJ, Yosaitis J, Schubel LC, Hill MA, Smith KM, Miller K. Evaluation of a Natural Language Processing Approach to Identify Diagnostic Errors and Analysis of Safety Learning System Case Review Data: Retrospective Cohort Study. J Med Internet Res. 2024 08 26; 26:e50935.
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Kote R, Ravina M, Goyal H, Mohanty D, Gupta R, Shukla AK, Reddy M, Prasanth PN. Role of textural and radiomic analysis parameters in predicting histopathological parameters of the tumor in breast cancer patients. Nucl Med Commun. 2024 Oct 01; 45(10):835-847.