The continuous improvement of digital assistance in the radiation oncologist's work: from web-based nomograms to the adoption of large-language models (LLMs). A systematic review by the young group of the Italian association of radiotherapy and clinical oncology (AIRO).
AI
LLM
Nomogram
Radiation oncology
Journal
La Radiologia medica
ISSN: 1826-6983
Titre abrégé: Radiol Med
Pays: Italy
ID NLM: 0177625
Informations de publication
Date de publication:
13 Oct 2024
13 Oct 2024
Historique:
received:
23
08
2024
accepted:
20
09
2024
medline:
14
10
2024
pubmed:
14
10
2024
entrez:
13
10
2024
Statut:
aheadofprint
Résumé
Recently, the availability of online medical resources for radiation oncologists and trainees has significantly expanded, alongside the development of numerous artificial intelligence (AI)-based tools. This review evaluates the impact of web-based clinical decision-making tools in the clinical practice of radiation oncology. We searched databases, including PubMed, EMBASE, and Scopus, using keywords related to web-based clinical decision-making tools and radiation oncology, adhering to PRISMA guidelines. Out of 2161 identified manuscripts, 70 were ultimately included in our study. These papers all supported the evidence that web-based tools can be transversally integrated into multiple radiation oncology fields, with online applications available for dose and clinical calculations, staging and other multipurpose intents. Specifically, the possible benefit of web-based nomograms for educational purposes was investigated in 35 of the evaluated manuscripts. As regards to the applications of digital and AI-based tools to treatment planning, diagnosis, treatment strategy selection and follow-up adoption, a total of 35 articles were selected. More specifically, 19 articles investigated the role of these tools in heterogeneous cancer types, while nine and seven articles were related to breast and head & neck cancers, respectively. Our analysis suggests that employing web-based and AI tools offers promising potential to enhance the personalization of cancer treatment.
Identifiants
pubmed: 39397129
doi: 10.1007/s11547-024-01891-y
pii: 10.1007/s11547-024-01891-y
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. Italian Society of Medical Radiology.
Références
Olsen DR, Bruland S, Davis BJ (2000) Telemedicine in radiotherapy treatment planning: requirements and applications. Radiother Oncol 54:255–259. https://doi.org/10.1016/s0167-8140(99)00185-1
doi: 10.1016/s0167-8140(99)00185-1
pubmed: 10738084
Zhang H, Cha EE, Lynch K, Cahlon O, Gomez DR, Shaverdian N, Gillespie EF (2020) Radiation oncologist perceptions of telemedicine from consultation to treatment planning: a mixed-methods study. Int J Radiat Oncol Biol Phys 108:421–429. https://doi.org/10.1016/j.ijrobp.2020.07.007
doi: 10.1016/j.ijrobp.2020.07.007
pubmed: 32890525
pmcid: 7462757
Piras A, Venuti V, D’Aviero A, Cusumano D, Pergolizzi S, Daidone A, Boldrini L (2022) Covid-19 and radiotherapy: a systematic review after 2 years of pandemic. Clin Transl Imaging. https://doi.org/10.1007/s40336-022-00513-9
doi: 10.1007/s40336-022-00513-9
pubmed: 35910079
pmcid: 9308500
Di Franco R, Borzillo V, D’Ippolito E, Scipilliti E, Petito A, Facchini G, Berretta M, Muto P (2020) COVID-19 and radiotherapy: potential new strategies for patients management with hypofractionation and telemedicine. Eur Rev Med Pharmacol Sci 24:12480–12489. https://doi.org/10.26355/eurrev_202012_24044
doi: 10.26355/eurrev_202012_24044
pubmed: 33336767
Culbert MM, Brisson RJ, Oladeru OT (2022) The landscape of digital resources in radiation oncology. Tech Innov Patient Supp Radiat Oncol 24:19–24. https://doi.org/10.1016/j.tipsro.2022.08.006
doi: 10.1016/j.tipsro.2022.08.006
Srivastav S, Chandrakar R, Gupta S, Babhulkar V, Agrawal S, Jaiswal A, Prasad R, Wanjari MB (2023) ChatGPT in radiology: the advantages and limitations of artificial intelligence for medical imaging diagnosis. Cureus 15:e41435. https://doi.org/10.7759/cureus.41435
doi: 10.7759/cureus.41435
pubmed: 37546142
pmcid: 10404120
Biswas S (2023) ChatGPT and the future of medical writing. Radiology 307:e223312. https://doi.org/10.1148/radiol.223312
doi: 10.1148/radiol.223312
pubmed: 36728748
Laudicella R, Davidzon GA, Dimos N, Provenzano G, Iagaru A, Bisdas S (2023) ChatGPT in nuclear medicine and radiology: lights and shadows in the AI bionetwork. Clin Transl Imaging 11:407–411. https://doi.org/10.1007/s40336-023-00574-4
doi: 10.1007/s40336-023-00574-4
A Scoping Review of Interactive and Personalized Web-Based Clinical Tools to Support Treatment Decision Making in Breast Cancer - PubMed Available online: https://pubmed.ncbi.nlm.nih.gov/34896693/ (accessed on 8 November 2023).
Pastorino R, De Vito C, Migliara G, Glocker K, Binenbaum I, Ricciardi W, Boccia S (2019) Benefits and challenges of big data in healthcare: an overview of the European initiatives. Eur J Public Health 29:23–27. https://doi.org/10.1093/eurpub/ckz168
doi: 10.1093/eurpub/ckz168
pubmed: 31738444
pmcid: 6859509
Syed K, Sleeman W IV, Ivey K, Hagan M, Palta J, Kapoor R, Ghosh P (2020) Integrated natural language processing and machine learning models for standardizing radiotherapy structure names. Healthcare (Basel) 8:120. https://doi.org/10.3390/healthcare8020120
doi: 10.3390/healthcare8020120
pubmed: 32365973
PRISMA-P Group, Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, Shekelle P, Stewart LA (2015) Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev 4:1. https://doi.org/10.1186/2046-4053-4-1
doi: 10.1186/2046-4053-4-1
pmcid: 4320440
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372:n71. https://doi.org/10.1136/bmj.n71
doi: 10.1136/bmj.n71
pubmed: 33782057
pmcid: 8005924
Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A (2016) Rayyan—a web and mobile app for systematic reviews. Syst Rev 5:210. https://doi.org/10.1186/s13643-016-0384-4
doi: 10.1186/s13643-016-0384-4
pubmed: 27919275
pmcid: 5139140
Calero JJ, Oton LF, Oton CA (2017) Apps for radiation oncology. Compr Rev Transl Oncol 10:108–114. https://doi.org/10.1016/j.tranon.2016.08.008
doi: 10.1016/j.tranon.2016.08.008
Balogun O, Ball A, Simonds H, Rodrigues B, Vanderpuye V, Hissourou M, Wilson S, Hardenbergh PH, Grover S (2020) Implementation of a web-based platform to improve radiation oncology education and quality in African Nations. Int J Radiat Oncol Biol Phys 108:e432–e433. https://doi.org/10.1016/j.ijrobp.2020.07.2516
doi: 10.1016/j.ijrobp.2020.07.2516
Gerard IJ, Wan B, Lalla VD, Skamene S, Alfieri J (2022) An interactive smartphone application for trainees in radiation oncology: “the rad onc handbook.” Int J Radiat Oncol Biol Phys 114:e17. https://doi.org/10.1016/j.ijrobp.2022.06.035
doi: 10.1016/j.ijrobp.2022.06.035
Hariu M, Hatanaka S, Kondo S, Shimbo M, Saito M, Goto S, Soda R, Yamano T, Nishimura K, Takahashi T (2020) Feasibility study for the development of an application for simulated virtual reality radiation therapy experiences using android and iOS devices. Igaku Butsuri 40:119–125. https://doi.org/10.11323/jjmp.40.4_119
doi: 10.11323/jjmp.40.4_119
pubmed: 33390377
Song SY, Ahn SD, Chung WK, Shin KH, Choi EK, Cho KH (2015) Development of new on-line statistical program for the Korean society for radiation oncology. Radiat Oncol J 33:142–148. https://doi.org/10.3857/roj.2015.33.2.142
doi: 10.3857/roj.2015.33.2.142
pubmed: 26157684
pmcid: 4493426
Winter JD, Adleman J, Purdie TG, Heaton J, McNiven A, Croke J (2020) An innovative learning tool for radiation therapy treatment plan evaluation: implementation and evaluation. Int J Radiat Oncol Biol Phys 107:844–849. https://doi.org/10.1016/j.ijrobp.2020.03.018
doi: 10.1016/j.ijrobp.2020.03.018
pubmed: 32259570
Ebrahimi B, Howard A, Carlson DJ, Al-Hallaq H (2023) ChatGPT: can a natural language processing tool be trusted for radiation oncology use? Int J Radiat Oncol Biol Phys 116:977–983. https://doi.org/10.1016/j.ijrobp.2023.03.075
doi: 10.1016/j.ijrobp.2023.03.075
pubmed: 37037358
Chow JCL, Wong V, Sanders L, Li K (2023) Developing an AI-assisted educational chatbot for radiotherapy using the IBM Watson assistant platform. Healthcare (Basel) 11:2417. https://doi.org/10.3390/healthcare11172417
doi: 10.3390/healthcare11172417
pubmed: 37685452
Culbert MM, Parekh A, Giap F, Indelicato DJ (2021) 1ONC: a comprehensive mobile and web application to improve access to clinical resources for practicing radiation oncologists. Int J Radiat Oncol Biol Phys 111:e13–e14. https://doi.org/10.1016/j.ijrobp.2021.05.160
doi: 10.1016/j.ijrobp.2021.05.160
Li Y, Li Z, Zhang K, Dan R, Jiang S, Zhang Y (2023) ChatDoctor: a medical chat model fine-tuned on a large language model meta-AI (LLaMA) using medical domain knowledge. Cureus 15:e40895. https://doi.org/10.7759/cureus.40895
doi: 10.7759/cureus.40895
pubmed: 37492832
pmcid: 10364849
Implementation of Web-Based Open-Source Radiotherapy Delineation Software (WORDS) in Organs at Risk Contouring Training for Newly Qualified Radiotherapists: Quantitative Comparison with Conventional One-to-One Coaching Approach - PubMed Available online: https://pubmed.ncbi.nlm.nih.gov/34749735/ (accessed on 8 November 2023).
De Bari B, Salembier C, Palmu M, Rivera S, Eriksen J, Kaylor S, Boyler A, Verfaillie C, Valentini V (2016) PO-0952: blended teaching reduces interobserver contouring variability: first results of the FALCON project. Radiother Oncol 119:S463. https://doi.org/10.1016/S0167-8140(16)32202-2
doi: 10.1016/S0167-8140(16)32202-2
De Felice F, Boldrini L, Greco C, Nardone V, Salvestrini V, Desideri I (2021) ESTRO vision 2030: the young Italian association of radiotherapy and clinical oncology (yAIRO) commitment statement. Radiol Med 126:1374–1376. https://doi.org/10.1007/s11547-021-01398-w
doi: 10.1007/s11547-021-01398-w
pubmed: 34283336
pmcid: 8520506
Gillespie E, Panjwani N, Sanghvi P, Murphy J (2017) PO-0751: Uptake of a novel interactive 3d web-based contouring atlas among the radiation oncology community. Radiother Oncol 123:S396. https://doi.org/10.1016/S0167-8140(17)31188-X
doi: 10.1016/S0167-8140(17)31188-X
Gillespie EF, Panjwani N, Golden DW, Gunther J, Chapman TR, Brower JV, Kosztyla R, Larson G, Neppala P, Moiseenko V et al (2017) Multi-institutional randomized trial testing the utility of an interactive three-dimensional contouring atlas among radiation oncology residents. Int J Radiat Oncol Biol Phys 98:547–554. https://doi.org/10.1016/j.ijrobp.2016.11.050
doi: 10.1016/j.ijrobp.2016.11.050
pubmed: 28262474
Duke SL, Tan LT, Eminowicz G, Park WHE, Wharrad H, Patel R, Doody G (2019) Rapid radiotherapy contouring practice: pilot study of a novel web-based tool enabling automated individualized feedback. Int J Radiat Oncol Biol Phys 105:E147. https://doi.org/10.1016/j.ijrobp.2019.06.2200
doi: 10.1016/j.ijrobp.2019.06.2200
Scheurleer J, Osorio EV, Assendelft E, Bel A, van Dijk I, Bijwaard H, van Herk M (2021) OC-0314 Panoptes-a novel tool for teaching organ at risk delineation to radiotherapy technologists. Radiother Oncol 161:S223–S225. https://doi.org/10.1016/S0167-8140(21)06861-4
doi: 10.1016/S0167-8140(21)06861-4
Holmes J, Liu Z, Zhang L, Ding Y, Sio TT, McGee LA, Ashman JB, Li X, Liu T, Shen J et al (2023) Evaluating large language models on a highly-specialized topic. Radiat Oncol Phys Front Oncol 13:1219326. https://doi.org/10.3389/fonc.2023.1219326
doi: 10.3389/fonc.2023.1219326
Toftegaard J, Lühr A, Sobolevsky N, Bassler N (2014) Improvements in the stopping power library libdEdx and release of the web GUI Dedx. Au.Dk. J Phys Conf Ser 489:012003. https://doi.org/10.1088/1742-6596/489/1/012003
doi: 10.1088/1742-6596/489/1/012003
Gh, A.; S, C.; F, N.; A, S.M.; K, E.G. 2020 Developing a mobile phone application for common radiotherapy calculations. J Biomed Phys Eng 10, 235–240, https://doi.org/10.31661/jbpe.v0i0.1216 .
Tsang DS, Townsend C, Cao X, Szumacher E (2015) RBApp: creation and patterns of use of an educational mobile application for radiobiology calculations in radiation therapy. J Med Imaging Radiat Sci 46:215–222. https://doi.org/10.1016/j.jmir.2015.03.001
doi: 10.1016/j.jmir.2015.03.001
pubmed: 31052096
Hanlon MD, Smith RL, Franich RD (2022) MaxiCalc: a tool for online dosimetric evaluation of source-tracking based treatment verification in HDR brachytherapy. Phys Med 94:58–64. https://doi.org/10.1016/j.ejmp.2021.12.008
doi: 10.1016/j.ejmp.2021.12.008
pubmed: 34998133
Casarino C, Russo G, Candiano GC, La Rocca G, Barbera R, Borasi G, Guatelli S, Messa C, Passaro G, Gilardi Mc (2015) A GEANT4 web-based application to support intra-operative electron radiotherapy using the European grid infrastructure. Concurr Comput Prac Exp 27:458–472. https://doi.org/10.1002/cpe.3268
doi: 10.1002/cpe.3268
Niraula D, Sun W, Jin J, Dinov ID, Cuneo K, Jamaluddin J, Matuszak MM, Luo Y, Lawrence TS, Jolly S et al (2023) A clinical decision support system for AI-assisted decision-making in response-adaptive radiotherapy (ARCliDS). Sci Rep 13:5279. https://doi.org/10.1038/s41598-023-32032-6
doi: 10.1038/s41598-023-32032-6
pubmed: 37002296
pmcid: 10066294
D’Aviero A, Re A, Catucci F, Piccari D, Votta C, Piro D, Piras A, Di Dio C, Iezzi M, Preziosi F et al (2022) Clinical validation of a deep-learning segmentation software in head and neck: an early analysis in a developing radiation oncology center. Int J Environ Res Public Health 19:9057. https://doi.org/10.3390/ijerph19159057
doi: 10.3390/ijerph19159057
pubmed: 35897425
pmcid: 9329735
Boldrini L, D’Aviero A, De Felice F, Desideri I, Grassi R, Greco C, Iorio GC, Nardone V, Piras A, Salvestrini V (2023) Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the young group of the Italian association of radiotherapy and clinical oncology (yAIRO). Radiol Med. https://doi.org/10.1007/s11547-023-01708-4
doi: 10.1007/s11547-023-01708-4
pubmed: 37801198
pmcid: 10116467
Kalpathy-Cramer J, Bedrick SD, Boccia K, Fuller CD (2011) A pilot prospective feasibility study of organ-at-risk definition using target contour testing/instructional computer software (TaCTICS), a training and evaluation platform for radiotherapy target delineation. AMIA Annu Symp Proc 2011:654–663
pubmed: 22195121
pmcid: 3243186
Chen G, Jia M, Zeng Q, Zhang H (2021) Development and validation of web-based nomograms for predicting cause-specific mortality in surgically resected nonmetastatic invasive breast cancer: a population-based study. Ann Surg Oncol 28:6537–6550. https://doi.org/10.1245/s10434-021-10129-4
doi: 10.1245/s10434-021-10129-4
pubmed: 34114183
Sanghani M, Truong PT, Raad RA, Niemierko A, Lesperance M, Olivotto IA, Wazer DE, Taghian AG (2010) Validation of a web-based predictive nomogram for ipsilateral breast tumor recurrence after breast conserving therapy. J Clin Oncol 28:718–722. https://doi.org/10.1200/JCO.2009.22.6662
doi: 10.1200/JCO.2009.22.6662
pubmed: 20048188
pmcid: 2834390
Jung SP, Hur SM, Lee SK, Kim S, Choi M-Y, Bae SY, Kim J, Kim MK, Kil WH, Choe J-H et al (2013) Validation of a web-based tool to predict the ipsilateral breast tumor recurrence (IBTR! 2.0) after breast-conserving therapy for Korean patients. J Breast Cancer 16:97–103. https://doi.org/10.4048/jbc.2013.16.1.97
doi: 10.4048/jbc.2013.16.1.97
pubmed: 23593089
pmcid: 3625777
Kindts I, Laenen A, Peeters S, Janssen H, Depuydt T, Nevelsteen I, Van Limbergen E, Weltens C (2016) Validation of the web-based IBTR! 2.0 nomogram to predict for ipsilateral breast tumor recurrence after breast-conserving therapy. Int J Radiat Oncol Biol Phys 95:1477–1484. https://doi.org/10.1016/j.ijrobp.2016.03.036
doi: 10.1016/j.ijrobp.2016.03.036
pubmed: 27315662
Lee BM, Chang JS, Cho YU, Park S, Park HS, Kim JY, Sohn JH, Kim GM, Koo JS, Keum KC et al (2018) External validation of IBTR! 2.0 nomogram for prediction of ipsilateral breast tumor recurrence. Radiat Oncol J 36:139–146. https://doi.org/10.3857/roj.2018.00059
doi: 10.3857/roj.2018.00059
pubmed: 29983034
pmcid: 6074074
Pleijhuis RG, Kwast ABG, Jansen L, de Vries J, Lanting R, Bart J, Wiggers T, van Dam GM, Siesling S (2013) A validated web-based nomogram for predicting positive surgical margins following breast-conserving surgery as a preoperative tool for clinical decision-making. Breast 22:773–779. https://doi.org/10.1016/j.breast.2013.01.010
doi: 10.1016/j.breast.2013.01.010
pubmed: 23462681
Mook S, Schmidt MK, Rutgers EJ, van de Velde AO, Visser O, Rutgers SM, Armstrong N, van’t Veer LJ, Ravdin PM (2009) Calibration and discriminatory accuracy of prognosis calculation for breast cancer with the online adjuvant! program: a hospital-based retrospective cohort study. Lancet Oncol 10:1070–1076. https://doi.org/10.1016/S1470-2045(09)70254-2
doi: 10.1016/S1470-2045(09)70254-2
pubmed: 19801202
Bhoo-Pathy N, Yip C-H, Hartman M, Saxena N, Taib NA, Ho G-F, Looi L-M, Bulgiba AM, van der Graaf Y, Verkooijen HM (2012) Adjuvant! online is overoptimistic in predicting survival of asian breast cancer patients. Eur J Cancer 48:982–989. https://doi.org/10.1016/j.ejca.2012.01.034
doi: 10.1016/j.ejca.2012.01.034
pubmed: 22366561
Paridaens RJ, Gelber S, Cole BF, Gelber RD, Thürlimann B, Price KN, Holmberg SB, Crivellari D, Coates AS, Goldhirsch A (2010) Adjuvant! online estimation of chemotherapy effectiveness when added to ovarian function suppression plus tamoxifen for premenopausal women with estrogen-receptor-positive breast cancer. Breast Cancer Res Treat 123:303–310. https://doi.org/10.1007/s10549-010-0794-2
doi: 10.1007/s10549-010-0794-2
pubmed: 20195744
pmcid: 3588884
Ozanne EM, Schneider KH, Soeteman D, Stout N, Schrag D, Fordis M, Punglia RS (2015) A web-based decision aid for DCIS treatment. Breast Cancer Res Treat 154:181–190. https://doi.org/10.1007/s10549-015-3605-y
doi: 10.1007/s10549-015-3605-y
pubmed: 26475704
Jin Y-N, Yang Q-Q, Li Z-Q, Ou X-Q, Zhang W-J, Marks T, Yao J-J, Xia L-P (2022) Development of a web-based prognostic model to quantify the survival benefit of cumulative cisplatin dose during concurrent chemoradiotherapy in childhood nasopharyngeal carcinoma. Radiother Oncol 166:118–125. https://doi.org/10.1016/j.radonc.2021.11.014
doi: 10.1016/j.radonc.2021.11.014
pubmed: 34838885
Yao J-J, Lin L, Gao T-S, Zhang W-J, Lawrence WR, Ma J, Sun Y (2021) Development and validation of web-based nomograms to precisely predict survival outcomes of non-metastatic nasopharyngeal carcinoma in an endemic area. Cancer Res Treat 53:657–670. https://doi.org/10.4143/crt.2020.899
doi: 10.4143/crt.2020.899
pubmed: 33285052
Wu C-F, Lv J-W, Lin L, Mao Y-P, Deng B, Zheng W-H, Wen D-W, Chen Y, Kou J, Chen F-P et al (2021) Development and validation of a web-based calculator to predict individualized conditional risk of site-specific recurrence in nasopharyngeal carcinoma: analysis of 10,058 endemic cases. Cancer Commun 41:37–50. https://doi.org/10.1002/cac2.12113
doi: 10.1002/cac2.12113
Chen S, He S, Wang D, Liu Y, Shao S, Tang L, Li C, Shi Q, Liu J, Wang F et al (2023) Developing a predictive nomogram and web-based survival calculator for locally advanced hypopharyngeal cancer: a propensity score-adjusted population-based study. Biomol Biomed 23:902–913. https://doi.org/10.17305/bb.2023.8978
doi: 10.17305/bb.2023.8978
pubmed: 37096424
pmcid: 10494849
Wang J, Liu X, Tang J, Zhang Q, Zhao Y (2021) A web-based prediction model for cancer-specific survival of elderly patients with hypopharyngeal squamous cell carcinomas: a population-based study. Front Public Health 9:815631. https://doi.org/10.3389/fpubh.2021.815631
doi: 10.3389/fpubh.2021.815631
pubmed: 35096758
Kim, J.W.; Marsilla, J.; Kazmierski, M.; Tkachuk, D.; Huang, S.H.; Xu, W.; Cho, J.; Ringash, J.; Bratman, S.; Haibe-Kains, B.; et al. Development of web-based quality-assurance tool for radiotherapy target delineation for head and neck cancer: quality evaluation of nasopharyngeal carcinoma 2021, 2021.02.24.21252123.
Kim JW, Marsilla J, Kazmierski M, Tkachuk D, Huang SH, Xu W, Cho J, Ringash J, Bratman S, Haibe-Kains B et al (2023) Effect of radiation therapy quality assurance on nasopharyngeal carcinoma: usage of a novel, web-based quality assurance application. Pract Radiat Oncol 13:e354–e364. https://doi.org/10.1016/j.prro.2023.03.003
doi: 10.1016/j.prro.2023.03.003
pubmed: 36948414
Gallant F, Portelance L, Al-Halabi H, Alfieri J, Al-Wassia R, Sultanem K, Cury F (2010) Needs assessment and development of an e-learning module on head and neck anatomy for residents in radiation oncology. Int J Radiat Oncol Biol Phys 78:S155–S156. https://doi.org/10.1016/j.ijrobp.2010.07.384
doi: 10.1016/j.ijrobp.2010.07.384
Du Y, Shao S, Lv M, Zhu Y, Yan L, Qiao T (2020) Radiotherapy versus surgery-which is better for patients with T1–2N0M0 glottic laryngeal squamous cell carcinoma? individualized survival prediction based on web-based nomograms. Front Oncol 10:1669. https://doi.org/10.3389/fonc.2020.01669
doi: 10.3389/fonc.2020.01669
pubmed: 33014833
pmcid: 7507900
Vitzthum L, Noticewala SS, Hines P, Zakeri K, Nguyen C, Shen H, Mell LK (2017) A web-based tool to compare comorbidity models and geriatric risk-assessment in head and neck cancer patients. Int J Radiat Oncol Biol Phys 99:E379. https://doi.org/10.1016/j.ijrobp.2017.06.1508
doi: 10.1016/j.ijrobp.2017.06.1508
LGG-06. Development of a web-based mobile device calculator application for predicting short- and long-term survival in Pediatric optic pathway glioma: a population-based database analysis | NEURO-Oncology | Oxford Academic Available online: https://academic.oup.com/neuro-oncology/article/25/Supplement_1/i56/7194586 (accessed on 9 November 2023).
Zhang Z, Zhang D, Shi X, Tao B, Liu Y, Zhang J (2022) A nomogram to predict recurrence-free survival following surgery for vestibular schwannoma. Front Oncol 12:838112. https://doi.org/10.3389/fonc.2022.838112
doi: 10.3389/fonc.2022.838112
pubmed: 35574416
pmcid: 9097914
A Predictive Web-Based Nomogram for Elderly Patients Newly Diagnosed as Uveal Melanoma: A Population-Based Study - PubMed Available online: https://pubmed.ncbi.nlm.nih.gov/35814753/ (accessed on 9 November 2023).
Haemmerli J, Sveikata L, Nouri A, May A, Egervari K, Freyschlag C, Lobrinus JA, Migliorini D, Momjian S, Sanda N et al (2023) ChatGPT in glioma adjuvant therapy decision making: ready to assume the role of a doctor in the tumour board? BMJ Health Care Inform 30:e100775. https://doi.org/10.1136/bmjhci-2023-100775
doi: 10.1136/bmjhci-2023-100775
pubmed: 37399360
pmcid: 10314415
Li J, Huang Y, Li Y, Liu P, Cheng H, Song H, Sun N, Shamil MA, Zhang W (2022) A web-based prognostic model for pediatric genitourinary rhabdomyosarcoma: analysis of population-based cohort with external validation. Front Public Health 10:870187. https://doi.org/10.3389/fpubh.2022.870187
doi: 10.3389/fpubh.2022.870187
pubmed: 35619827
pmcid: 9127601
Wei J, Liu L, Li Z, Ren Z, Zhang C, Cao H, Fen Z, Jin Y (2023) Web-based nomogram to predict postresection risk of distant metastasis in patients with leiomyosarcoma: retrospective analysis of the SEER database and a Chinese cohort. J Int Med Res 51:3000605231188647. https://doi.org/10.1177/03000605231188647
doi: 10.1177/03000605231188647
pubmed: 37523501
Construction, Validation and, Visualization of a Web-Based Nomogram for Predicting the Overall Survival and Cancer-Specific Survival of Leiomyosarcoma Patients with Lung Metastasis - PubMed Available online: https://pubmed.ncbi.nlm.nih.gov/34164199/ (accessed on 9 November 2023).
He T, Chen T, Liu X, Zhang B, Yue S, Cao J, Zhang G (2021) A web-based prediction model for cancer-specific survival of elderly patients with early hepatocellular carcinoma: a study based on SEER database. Front Public Health 9:789026. https://doi.org/10.3389/fpubh.2021.789026
doi: 10.3389/fpubh.2021.789026
pubmed: 35096742
Zhan G, Peng H, Zhou L, Jin L, Xie X, He Y, Wang X, Du Z, Cao P (2023) A web-based nomogram model for predicting the overall survival of hepatocellular carcinoma patients with external beam radiation therapy: a population study based on SEER database and a Chinese cohort. Front Endocrinol (Lausanne) 14:1070396. https://doi.org/10.3389/fendo.2023.1070396
doi: 10.3389/fendo.2023.1070396
pubmed: 36798659
Huang G, Lin Q, Yin P, Mao K, Zhang J (2023) Development and validation of web-based prognostic nomograms for massive hepatocellular carcinoma (≥10 cm): a retrospective study based on the SEER database. Cancer Med 12:13167–13181. https://doi.org/10.1002/cam4.6003
doi: 10.1002/cam4.6003
pubmed: 37102245
pmcid: 10315854
Kang S, Nam B-H, Park J-Y, Seo S-S, Ryu S-Y, Kim JW, Kim S-C, Park S-Y, Nam J-H (2012) Risk assessment tool for distant recurrence after platinum-based concurrent chemoradiation in patients with locally advanced cervical cancer: a Korean gynecologic oncology group study. J Clin Oncol 30:2369–2374. https://doi.org/10.1200/JCO.2011.37.5923
doi: 10.1200/JCO.2011.37.5923
pubmed: 22614984
Ding L, Xia B, Zhang Y, Liu Z, Wang J (2022) Web-based prediction models for overall survival and cancer-specific survival of patients with primary urachal carcinoma: a study based on SEER database. Front Public Health 10:870920. https://doi.org/10.3389/fpubh.2022.870920
doi: 10.3389/fpubh.2022.870920
pubmed: 35719613
pmcid: 9201252
Pennington Z, Ehresman J, Feghali J, Schilling A, Hersh A, Hung B, Lubelski D, Sciubba DM (2021) A clinical calculator for predicting intraoperative blood loss and transfusion risk in spine tumor patients. Spine J 21:302–311. https://doi.org/10.1016/j.spinee.2020.09.011
doi: 10.1016/j.spinee.2020.09.011
pubmed: 33007469
Tong Y, Cui Y, Jiang L, Zeng Y, Zhao D (2022) Construction, validation, and visualization of two web-based nomograms for predicting overall survival and cancer-specific survival in elderly patients with primary osseous spinal neoplasms. J Oncol 2022:7987967. https://doi.org/10.1155/2022/7987967
doi: 10.1155/2022/7987967
pubmed: 35419057
pmcid: 9001131
Cui Y, Wang Q, Shi X, Ye Q, Lei M, Wang B (2022) Development of a web-based calculator to predict three-month mortality among patients with bone metastases from cancer of unknown primary: an internally and externally validated study using machine-learning techniques. Front Oncol 12:1095059. https://doi.org/10.3389/fonc.2022.1095059
doi: 10.3389/fonc.2022.1095059
pubmed: 36568149
pmcid: 9768185
Yin M, Guan S, Ding X, Zhuang R, Sun Z, Wang T, Zheng J, Li L, Gao X, Wei H et al (2022) Construction and validation of a novel web-based nomogram for patients with lung cancer with bone metastasis: a real-world analysis based on the SEER database. Front Oncol 12:1075217. https://doi.org/10.3389/fonc.2022.1075217
doi: 10.3389/fonc.2022.1075217
pubmed: 36568214
pmcid: 9780685
Laviana AA, Zhao Z, Huang L-C, Koyama T, Conwill R, Hoffman K, Goodman M, Hamilton AS, Wu X-C, Paddock LE et al (2020) Development and internal validation of a web-based tool to predict sexual, urinary, and bowel function longitudinally after radiation therapy, surgery, or observation. Eur Urol 78:248–255. https://doi.org/10.1016/j.eururo.2020.02.007
doi: 10.1016/j.eururo.2020.02.007
pubmed: 32098731
pmcid: 7384934
Kim KH, Lee S, Ju EB, Shim JB, Yang DS, Yoon WS, Park YJ, Lee NK, Kim CY, Chang KH et al (2019) Development of a web-based radiation toxicity prediction system using metarule-guided mining to predict radiation pneumonitis and esophagitis in lung cancer patients. J Korean Phys Soc 75:319–325. https://doi.org/10.3938/jkps.75.319
doi: 10.3938/jkps.75.319
Haleem A, Javaid M, Singh RP, Suman R (2021) Telemedicine for healthcare: capabilities, features, barriers, and applications. Sens Int 2:100117. https://doi.org/10.1016/j.sintl.2021.100117
doi: 10.1016/j.sintl.2021.100117
pubmed: 34806053
pmcid: 8590973
Rösler W, Altenbuchinger M, Baeßler B, Beissbarth T, Beutel G, Bock R, von Bubnoff N, Eckardt J-N, Foersch S, Loeffler CML et al (2023) An overview and a roadmap for artificial intelligence in hematology and oncology. J Cancer Res Clin Oncol 149:7997–8006. https://doi.org/10.1007/s00432-023-04667-5
doi: 10.1007/s00432-023-04667-5
pubmed: 36920563
pmcid: 10374829
Balachandran VP, Gonen M, Smith JJ, DeMatteo RP (2015) Nomograms in oncology – more than meets the eye. Lancet Oncol 16:e173–e180. https://doi.org/10.1016/S1470-2045(14)71116-7
doi: 10.1016/S1470-2045(14)71116-7
pubmed: 25846097
pmcid: 4465353