Matches in SemOpenAlex for { <https://semopenalex.org/work/W4308844583> ?p ?o ?g. }
- W4308844583 abstract "Abstract Renal cancer is responsible for over 100,000 yearly deaths and is principally discovered in computed tomography (CT) scans of the abdomen. CT screening would likely increase the rate of early renal cancer detection, and improve general survival rates, but it is expected to have a prohibitively high financial cost. Given recent advances in artificial intelligence (AI), it may be possible to reduce the cost of CT analysis and enable CT screening by automating the radiological tasks that constitute the early renal cancer detection pipeline. This review seeks to facilitate further interdisciplinary research in early renal cancer detection by summarising our current knowledge across AI, radiology, and oncology and suggesting useful directions for future novel work. Initially, this review discusses existing approaches in automated renal cancer diagnosis, and methods across broader AI research, to summarise the existing state of AI cancer analysis. Then, this review matches these methods to the unique constraints of early renal cancer detection and proposes promising directions for future research that may enable AI-based early renal cancer detection via CT screening. The primary targets of this review are clinicians with an interest in AI and data scientists with an interest in the early detection of cancer." @default.
- W4308844583 created "2022-11-17" @default.
- W4308844583 creator A5004371367 @default.
- W4308844583 creator A5015969462 @default.
- W4308844583 creator A5028460960 @default.
- W4308844583 creator A5033219330 @default.
- W4308844583 creator A5033880300 @default.
- W4308844583 creator A5052394542 @default.
- W4308844583 creator A5065018312 @default.
- W4308844583 date "2022-11-11" @default.
- W4308844583 modified "2023-10-14" @default.
- W4308844583 title "Artificial intelligence for early detection of renal cancer in computed tomography: A review" @default.
- W4308844583 cites W130099911 @default.
- W4308844583 cites W1901129140 @default.
- W4308844583 cites W1987151740 @default.
- W4308844583 cites W2021567082 @default.
- W4308844583 cites W2060503480 @default.
- W4308844583 cites W2071061528 @default.
- W4308844583 cites W2091289793 @default.
- W4308844583 cites W2094492216 @default.
- W4308844583 cites W2103348420 @default.
- W4308844583 cites W2111795806 @default.
- W4308844583 cites W2162809564 @default.
- W4308844583 cites W2164317031 @default.
- W4308844583 cites W2194775991 @default.
- W4308844583 cites W2767599153 @default.
- W4308844583 cites W2770027889 @default.
- W4308844583 cites W2791935486 @default.
- W4308844583 cites W2793120371 @default.
- W4308844583 cites W2795996358 @default.
- W4308844583 cites W2883683269 @default.
- W4308844583 cites W2885928324 @default.
- W4308844583 cites W2891016561 @default.
- W4308844583 cites W2946185430 @default.
- W4308844583 cites W2946760001 @default.
- W4308844583 cites W2951166644 @default.
- W4308844583 cites W2962884052 @default.
- W4308844583 cites W2964324957 @default.
- W4308844583 cites W2970708515 @default.
- W4308844583 cites W2978442853 @default.
- W4308844583 cites W3000125976 @default.
- W4308844583 cites W3001499099 @default.
- W4308844583 cites W3001554798 @default.
- W4308844583 cites W3004329836 @default.
- W4308844583 cites W3022494500 @default.
- W4308844583 cites W3022550028 @default.
- W4308844583 cites W3025192708 @default.
- W4308844583 cites W3033511611 @default.
- W4308844583 cites W3044073403 @default.
- W4308844583 cites W3044155519 @default.
- W4308844583 cites W3092287738 @default.
- W4308844583 cites W3095881014 @default.
- W4308844583 cites W3112701542 @default.
- W4308844583 cites W3127745304 @default.
- W4308844583 cites W3138516171 @default.
- W4308844583 cites W3152926200 @default.
- W4308844583 cites W3162855534 @default.
- W4308844583 cites W3172711942 @default.
- W4308844583 cites W3179874300 @default.
- W4308844583 cites W3206402530 @default.
- W4308844583 cites W3210579795 @default.
- W4308844583 cites W3211488171 @default.
- W4308844583 cites W4205620333 @default.
- W4308844583 cites W4212875960 @default.
- W4308844583 cites W4248721905 @default.
- W4308844583 cites W4312443924 @default.
- W4308844583 cites W967702446 @default.
- W4308844583 doi "https://doi.org/10.1017/pcm.2022.9" @default.
- W4308844583 hasPublicationYear "2022" @default.
- W4308844583 type Work @default.
- W4308844583 citedByCount "1" @default.
- W4308844583 countsByYear W43088445832023 @default.
- W4308844583 crossrefType "journal-article" @default.
- W4308844583 hasAuthorship W4308844583A5004371367 @default.
- W4308844583 hasAuthorship W4308844583A5015969462 @default.
- W4308844583 hasAuthorship W4308844583A5028460960 @default.
- W4308844583 hasAuthorship W4308844583A5033219330 @default.
- W4308844583 hasAuthorship W4308844583A5033880300 @default.
- W4308844583 hasAuthorship W4308844583A5052394542 @default.
- W4308844583 hasAuthorship W4308844583A5065018312 @default.
- W4308844583 hasBestOaLocation W43088445831 @default.
- W4308844583 hasConcept C121608353 @default.
- W4308844583 hasConcept C126322002 @default.
- W4308844583 hasConcept C126838900 @default.
- W4308844583 hasConcept C154945302 @default.
- W4308844583 hasConcept C177713679 @default.
- W4308844583 hasConcept C19527891 @default.
- W4308844583 hasConcept C2781068499 @default.
- W4308844583 hasConcept C2985322473 @default.
- W4308844583 hasConcept C41008148 @default.
- W4308844583 hasConcept C544519230 @default.
- W4308844583 hasConcept C71924100 @default.
- W4308844583 hasConceptScore W4308844583C121608353 @default.
- W4308844583 hasConceptScore W4308844583C126322002 @default.
- W4308844583 hasConceptScore W4308844583C126838900 @default.
- W4308844583 hasConceptScore W4308844583C154945302 @default.
- W4308844583 hasConceptScore W4308844583C177713679 @default.
- W4308844583 hasConceptScore W4308844583C19527891 @default.
- W4308844583 hasConceptScore W4308844583C2781068499 @default.
- W4308844583 hasConceptScore W4308844583C2985322473 @default.