Search

Your search keyword '"Shellawell, G."' showing total 3 results
Start Over Author "Shellawell, G."
3 results on '"Shellawell, G."'

1. Management strategies for patients with advanced rectal cancer and liver metastases using modified Delphi methodology: results from the PelvEx Collaborative

Author

Kelly M. E., Agj A., Abdul Aziz N., Abecasis N., Abraham-Nordling M., Akiyoshi T., Alberda W., Albert M., Andric M., Angenete E., Antoniou A., Auer R., Austin K. K., Aziz O., Baker R. P., Bali M., Baseckas G., Bebington B., Bednarski B. K., Beets G. L., Berg P. L., Beynon J., Biondo S., Boyle K., Bordeianou L., Bremers A. B., Brunner M., Buchwald P., Bui A., Burgess A., Jwa B., Burling D., Campain N., Carvalhal S., Castro L., Caycedo-Marulanda A., Kkl C., Chang G. J., Chew M. H., Chong P., Christensen H. K., Clouston H., Codd M., Collins D., Colquhoun A. J., Corr A., Coscia M., Coyne P. E., Creavin B., Croner R. S., Damjanovic L., Daniels I. R., Davies M., Davies R. J., Delaney C. P., de Wilt J., Denost Q., Deutsch C., Dietz D., Domingo S., Dozois E. J., Duff M., Eglinton T., Enrique-Navascues J. M., Espin-Basany E., Evans M. D., Fearnhead N. S., Flatmark K., Fleming F., Frizelle F. A., Gallego M. A., Garcia-Granero E., Garcia-Sabrido J. L., Gentilini L., George M. L., Ghouti L., Giner F., Ginther N., Glynn R., Golda T., Griffiths B., Harris D. A., Jaw H., Hanchanale V., Harji D. P., Helewa R. M., Heriot A. G., Hochman D., Hohenberger W., Holm T., Hompes R., Jenkins J. T., Kaffenberger S., Kandaswamy G. V., Kapur S., Kanemitsu Y., Kelley S. R., Keller D. S., Khan M. S., Kiran R. P., Kim H., Kim H. J., Koh C. E., Nfm K., Kokelaar R., Kontovounisios C., Kristensen H. O., Kroon H. M., Kusters M., Lago V., Larsen S. G., Larson D. W., Law W. L., Laurberg S., Lee P. J., Limbert M., Lydrup M. L., Lyons A., Lynch A. C., Mantyh C., Mathis K. L., Cfs M., Martling A., Wjhj M., Merkel S., Mehta A. M., McArthur D. R., McDermott F. D., McGrath J. S., Malde S., Mirnezami A., Jrt M., Morton J. R., Mullaney T. G., Negoi I., Jwm N., Nguyen B., Nielsen M. B., Gap N., Nilsson P. J., O'Connell P. R., O'Dwyer S. T., Palmer G., Pappou E., Park J., Patsouras D., Pellino G., Peterson A. C., Poggioli G., Proud D., Quinn M., Quyn A., Radwan R. W., Rasheed S., Rasmussen P. C., Regenbogen S. E., Renehan A., Rocha R., Rochester M., Rohila J., Rothbarth J., Rottoli M., Roxburgh C., Hjt R., Ryan E. J., Safar B., Sagar P. M., Sahai A., Saklani A., Sammour T., Sayyed R., Amp S., Schwarzkopf E., Scripcariu V., Selvasekar C., Shaikh I., Shellawell G., Shida D., Simpson A., Smart N. J., Smart P., Smith J. J., Solbakken A. M., Solomon M. J., Sorensen M. M., Steele S. R., Steffens D., Stitzenberg K., Stocchi L., Stylianides N. A., Sumrien H., Sutton P. A., Swartking T., Taylor C., Tekkis P. P., Teras J., Thurairaja R., Toh E. L., Tsarkov P., Tsukada Y., Tsukamoto S., Tuech J. J., Turner W. H., Tuynman J. B., van Ramshorst G., van Zoggel D., Vasquez-Jimenez W., Verhoef C., Vizzielli G., Elk V., Uehara K., Wakeman C., Warrier S., Wasmuth H. H., Weber K., Weiser M. R., Jmd W., Wild J., Wilson M., Wolthuis A., Yano H., Yip B., Yip J., Yoo R. N., Winter D. C., Rottoli M, Poggioli G, Kelly, M. E., Agj, A., Abdul Aziz, N., Abecasis, N., Abraham-Nordling, M., Akiyoshi, T., Alberda, W., Albert, M., Andric, M., Angenete, E., Antoniou, A., Auer, R., Austin, K. K., Aziz, O., Baker, R. P., Bali, M., Baseckas, G., Bebington, B., Bednarski, B. K., Beets, G. L., Berg, P. L., Beynon, J., Biondo, S., Boyle, K., Bordeianou, L., Bremers, A. B., Brunner, M., Buchwald, P., Bui, A., Burgess, A., Jwa, B., Burling, D., Campain, N., Carvalhal, S., Castro, L., Caycedo-Marulanda, A., Kkl, C., Chang, G. J., Chew, M. H., Chong, P., Christensen, H. K., Clouston, H., Codd, M., Collins, D., Colquhoun, A. J., Corr, A., Coscia, M., Coyne, P. E., Creavin, B., Croner, R. S., Damjanovic, L., Daniels, I. R., Davies, M., Davies, R. J., Delaney, C. P., de Wilt, J., Denost, Q., Deutsch, C., Dietz, D., Domingo, S., Dozois, E. J., Duff, M., Eglinton, T., Enrique-Navascues, J. M., Espin-Basany, E., Evans, M. D., Fearnhead, N. S., Flatmark, K., Fleming, F., Frizelle, F. A., Gallego, M. A., Garcia-Granero, E., Garcia-Sabrido, J. L., Gentilini, L., George, M. L., Ghouti, L., Giner, F., Ginther, N., Glynn, R., Golda, T., Griffiths, B., Harris, D. A., Jaw, H., Hanchanale, V., Harji, D. P., Helewa, R. M., Heriot, A. G., Hochman, D., Hohenberger, W., Holm, T., Hompes, R., Jenkins, J. T., Kaffenberger, S., Kandaswamy, G. V., Kapur, S., Kanemitsu, Y., Kelley, S. R., Keller, D. S., Khan, M. S., Kiran, R. P., Kim, H., Kim, H. J., Koh, C. E., Nfm, K., Kokelaar, R., Kontovounisios, C., Kristensen, H. O., Kroon, H. M., Kusters, M., Lago, V., Larsen, S. G., Larson, D. W., Law, W. L., Laurberg, S., Lee, P. J., Limbert, M., Lydrup, M. L., Lyons, A., Lynch, A. C., Mantyh, C., Mathis, K. L., Cfs, M., Martling, A., Wjhj, M., Merkel, S., Mehta, A. M., Mcarthur, D. R., Mcdermott, F. D., Mcgrath, J. S., Malde, S., Mirnezami, A., Jrt, M., Morton, J. R., Mullaney, T. G., Negoi, I., Jwm, N., Nguyen, B., Nielsen, M. B., Gap, N., Nilsson, P. J., O'Connell, P. R., O'Dwyer, S. T., Palmer, G., Pappou, E., Park, J., Patsouras, D., Pellino, G., Peterson, A. C., Poggioli, G., Proud, D., Quinn, M., Quyn, A., Radwan, R. W., Rasheed, S., Rasmussen, P. C., Regenbogen, S. E., Renehan, A., Rocha, R., Rochester, M., Rohila, J., Rothbarth, J., Rottoli, M., Roxburgh, C., Hjt, R., Ryan, E. J., Safar, B., Sagar, P. M., Sahai, A., Saklani, A., Sammour, T., Sayyed, R., Amp, S., Schwarzkopf, E., Scripcariu, V., Selvasekar, C., Shaikh, I., Shellawell, G., Shida, D., Simpson, A., Smart, N. J., Smart, P., Smith, J. J., Solbakken, A. M., Solomon, M. J., Sorensen, M. M., Steele, S. R., Steffens, D., Stitzenberg, K., Stocchi, L., Stylianides, N. A., Sumrien, H., Sutton, P. A., Swartking, T., Taylor, C., Tekkis, P. P., Teras, J., Thurairaja, R., Toh, E. L., Tsarkov, P., Tsukada, Y., Tsukamoto, S., Tuech, J. J., Turner, W. H., Tuynman, J. B., van Ramshorst, G., van Zoggel, D., Vasquez-Jimenez, W., Verhoef, C., Vizzielli, G., Elk, V., Uehara, K., Wakeman, C., Warrier, S., Wasmuth, H. H., Weber, K., Weiser, M. R., Jmd, W., Wild, J., Wilson, M., Wolthuis, A., Yano, H., Yip, B., Yip, J., Yoo, R. N., Winter, D. C., and Academic Medical Center

Subjects
Liver metastasisSurvival Outcome, medicine.medical_specialty, survival outcomes, Colorectal cancer, surgical outcome, medicine.medical_treatment, Delphi method, Rectum, Disease, surgical outcomes, 03 medical and health sciences, Liver disease, 0302 clinical medicine, SDG 3 - Good Health and Well-being, Hepatectomy, Humans, Medicine, liver metastasi, Rectal cancer, Neoplasm Staging, Pelvic exenteration, Rectal Neoplasms, business.industry, General surgery, Liver Neoplasms, Gastroenterology, Induction chemotherapy, medicine.disease, liver metastasis, medicine.anatomical_structure, 030220 oncology & carcinogenesis, international collaboration, 030211 gastroenterology & hepatology, business
Abstract

Aim: A total of 15–20% of patients with rectal cancer have liver metastases on presentation. The management of these patients is controversial. Heterogeneity in management strategies is considerable, with management often being dependent on local resources and available expertise. Method: Members of the PelvEx Collaborative were invited to participate in the generation of a consensus statement on the optimal management of patients with advanced rectal cancer with liver involvement. Fifteen statements were created for topical discussion on diagnostic and management issues. Panellists were asked to vote on statements and anonymous feedback was given. A collaborative meeting was used to discuss any nuances and clarify any obscurity. Consensus was considered when > 85% agreement on a statement was achieved. Results: A total of 135 participants were involved in the final round of the Delphi questionnaire. Nine of the 15 statements reached consensus regarding the management of patients with advanced rectal cancer and oligometastatic liver disease. Routine use of liver MRI was not recommended for patients with locally advanced rectal cancer, unless there was concern for metastatic disease on initial computed tomography staging scan. Induction chemotherapy was advocated as first-line treatment in those with synchronous liver metastases in locally advanced rectal cancer. In the presence of symptomatic primary disease, a diverting stoma may be required to facilitate induction chemotherapy. Overall, only one-quarter of the panellists would consider simultaneous pelvic exenteration and liver resection. Conclusion: This Delphi process highlights the diverse treatment of advanced rectal cancer with liver metastases and provides recommendations from an experienced international group regarding the multidisciplinary management approach.

Published
2020
Full Text
View/download PDF

2. Predicting outcomes of pelvic exenteration using machine learning

Author

Dudurych I., Kelly M. E., Aalbers A. G. J., Abdul Aziz N., Abecasis N., Abraham-Nordling M., Akiyoshi T., Alberda W., Albert M., Andric M., Angenete E., Antoniou A., Auer R., Austin K. K., Aziz O., Baker R. P., Bali M., Baseckas G., Bebington B., Bedford M., Bednarski B. K., Beets G. L., Berg P. L., Beynon J., Biondo S., Boyle K., Bordeianou L., Bremers A. B., Brunner M., Buchwald P., Bui A., Burgess A., Burger J. W. A., Burling D., Burns E., Campain N., Carvalhal S., Castro L., Caycedo-Marulanda A., Chan K. K. L., Chang G. J., Chew M. H., Chok A. K., Chong P., Christensen H. K., Clouston H., Codd M., Collins D., Colquhoun A. J., Corr A., Coscia M., Coyne P. E., Creavin B., Croner R. S., Damjanovic L., Daniels I. R., Davies M., Davies R. J., Delaney C. P., Wilt J. H. W., Denost Q., Deutsch C., Dietz D., Domingo S., Dozois E. J., Duff M., Eglinton T., Enrique-Navascues J. M., Espin-Basany E., Evans M. D., Fearnhead N. S., Flatmark K., Fleming F., Frizelle F. A., Gallego M. A., Garcia-Granero E., Garcia-Sabrido J. L., Gentilini L., George M. L., George V., Ghouti L., Giner F., Ginther N., Glynn R., Golda T., Griffiths B., Harris D. A., Hagemans J. A. W., Hanchanale V., Harji D. P., Helewa R. M., Heriot A. G., Hochman D., Hohenberger W., Holm T., Hompes R., Jenkins J. T., Kaffenberger S., Kandaswamy G. V., Kapur S., Kanemitsu Y., Kelley S. R., Keller D. S., Khan M. S., Kiran R. P., Kim H., Kim H. J., Koh C. E., Kok N. F. M., Kokelaar R., Kontovounisios C., Kristensen H. O., Kroon H. M., Kusters M., Lago V., Larsen S. G., Larson D. W., Law W. L., Laurberg S., Lee P. J., Limbert M., Lydrup M. L., Lyons A., Lynch A. C., Mantyh C., Mathis K. L., Margues C. F. S., Martling A., Meijerink W. J. H. J., Merkel S., Mehta A. M., McArthur D. R., McDermott F. D., McGrath J. S., Malde S., Mirnezami A., Monson J. R. T., Morton J. R., Mullaney T. G., Negoi I., Neto J. W. M., Nguyen B., Nielsen M. B., Nieuwenhuijzen G. A. P., Nilsson P. J., Oliver A., O'Connell P. R., O'Dwyer S. T., Palmer G., Pappou E., Park J., Patsouras D., Pellino G., Peterson A. C., Poggioli G., Proud D., Quinn M., Quyn A., Radwan R. W., Rasheed S., Rasmussen P. C., Regenbogen S. E., Renehan A., Rocha R., Rochester M., Rohila J., Rothbarth J., Rottoli M., Roxburgh C., Rutten H. J. T., Ryan E. J., Safar B., Sagar P. M., Sahai A., Saklani A., Sammour T., Sayyed R., Schizas A. M. P., Schwarzkopf E., Scripcariu V., Selvasekar C., Shaikh I., Shellawell G., Shida D., Simpson A., Smart N. J., Smart P., Smith J. J., Solbakken A. M., Solomon M. J., Sorensen M. M., Steele S. R., Steffens D., Stitzenberg K., Stocchi L., Stylianides N. A., Swartling T., Sumrien H., Sutton P. A., Swartking T., Tan E. J., Taylor C., Tekkis P. P., Teras J., Thurairaja R., Toh E. L., Tsarkov P., Tsukada Y., Tsukamoto S., Tuech J. J., Turner W. H., Tuynman J. B., van Ramshorst G. H., van Zoggel D., Vasquez-Jimenez W., Verhoef C., Vizzielli G., Voogt E. L. K., Uehara K., Wakeman C., Warrier S., Wasmuth H. H., Weber K., Weiser M. R., Wheeler J. M. D., Wild J., Wilson M., Wolthuis A., Yano H., Yip B., Yip J., Yoo R. N., Winter D. C., Dudurych I., Kelly M.E., Aalbers A.G.J., Abdul Aziz N., Abecasis N., Abraham-Nordling M., Akiyoshi T., Alberda W., Albert M., Andric M., Angenete E., Antoniou A., Auer R., Austin K.K., Aziz O., Baker R.P., Bali M., Baseckas G., Bebington B., Bedford M., Bednarski B.K., Beets G.L., Berg P.L., Beynon J., Biondo S., Boyle K., Bordeianou L., Bremers A.B., Brunner M., Buchwald P., Bui A., Burgess A., Burger J.W.A., Burling D., Burns E., Campain N., Carvalhal S., Castro L., Caycedo-Marulanda A., Chan K.K.L., Chang G.J., Chew M.H., Chok A.K., Chong P., Christensen H.K., Clouston H., Codd M., Collins D., Colquhoun A.J., Corr A., Coscia M., Coyne P.E., Creavin B., Croner R.S., Damjanovic L., Daniels I.R., Davies M., Davies R.J., Delaney C.P., Wilt J.H.W., Denost Q., Deutsch C., Dietz D., Domingo S., Dozois E.J., Duff M., Eglinton T., Enrique-Navascues J.M., Espin-Basany E., Evans M.D., Fearnhead N.S., Flatmark K., Fleming F., Frizelle F.A., Gallego M.A., Garcia-Granero E., Garcia-Sabrido J.L., Gentilini L., George M.L., George V., Ghouti L., Giner F., Ginther N., Glynn R., Golda T., Griffiths B., Harris D.A., Hagemans J.A.W., Hanchanale V., Harji D.P., Helewa R.M., Heriot A.G., Hochman D., Hohenberger W., Holm T., Hompes R., Jenkins J.T., Kaffenberger S., Kandaswamy G.V., Kapur S., Kanemitsu Y., Kelley S.R., Keller D.S., Khan M.S., Kiran R.P., Kim H., Kim H.J., Koh C.E., Kok N.F.M., Kokelaar R., Kontovounisios C., Kristensen H.O., Kroon H.M., Kusters M., Lago V., Larsen S.G., Larson D.W., Law W.L., Laurberg S., Lee P.J., Limbert M., Lydrup M.L., Lyons A., Lynch A.C., Mantyh C., Mathis K.L., Margues C.F.S., Martling A., Meijerink W.J.H.J., Merkel S., Mehta A.M., McArthur D.R., McDermott F.D., McGrath J.S., Malde S., Mirnezami A., Monson J.R.T., Morton J.R., Mullaney T.G., Negoi I., Neto J.W.M., Nguyen B., Nielsen M.B., Nieuwenhuijzen G.A.P., Nilsson P.J., Oliver A., O'Connell P.R., O'Dwyer S.T., Palmer G., Pappou E., Park J., Patsouras D., Pellino G., Peterson A.C., Poggioli G., Proud D., Quinn M., Quyn A., Radwan R.W., Rasheed S., Rasmussen P.C., Regenbogen S.E., Renehan A., Rocha R., Rochester M., Rohila J., Rothbarth J., Rottoli M., Roxburgh C., Rutten H.J.T., Ryan E.J., Safar B., Sagar P.M., Sahai A., Saklani A., Sammour T., Sayyed R., Schizas A.M.P., Schwarzkopf E., Scripcariu V., Selvasekar C., Shaikh I., Shellawell G., Shida D., Simpson A., Smart N.J., Smart P., Smith J.J., Solbakken A.M., Solomon M.J., Sorensen M.M., Steele S.R., Steffens D., Stitzenberg K., Stocchi L., Stylianides N.A., Swartling T., Sumrien H., Sutton P.A., Swartking T., Tan E.J., Taylor C., Tekkis P.P., Teras J., Thurairaja R., Toh E.L., Tsarkov P., Tsukada Y., Tsukamoto S., Tuech J.J., Turner W.H., Tuynman J.B., van Ramshorst G.H., van Zoggel D., Vasquez-Jimenez W., Verhoef C., Vizzielli G., Voogt E.L.K., Uehara K., Wakeman C., Warrier S., Wasmuth H.H., Weber K., Weiser M.R., Wheeler J.M.D., Wild J., Wilson M., Wolthuis A., Yano H., Yip B., Yip J., Yoo R.N., Winter D.C., Surgery, Dudurych, I., Kelly, M. E., Aalbers, A. G. J., Abdul Aziz, N., Abecasis, N., Abraham-Nordling, M., Akiyoshi, T., Alberda, W., Albert, M., Andric, M., Angenete, E., Antoniou, A., Auer, R., Austin, K. K., Aziz, O., Baker, R. P., Bali, M., Baseckas, G., Bebington, B., Bedford, M., Bednarski, B. K., Beets, G. L., Berg, P. L., Beynon, J., Biondo, S., Boyle, K., Bordeianou, L., Bremers, A. B., Brunner, M., Buchwald, P., Bui, A., Burgess, A., Burger, J. W. A., Burling, D., Burns, E., Campain, N., Carvalhal, S., Castro, L., Caycedo-Marulanda, A., Chan, K. K. L., Chang, G. J., Chew, M. H., Chok, A. K., Chong, P., Christensen, H. K., Clouston, H., Codd, M., Collins, D., Colquhoun, A. J., Corr, A., Coscia, M., Coyne, P. E., Creavin, B., Croner, R. S., Damjanovic, L., Daniels, I. R., Davies, M., Davies, R. J., Delaney, C. P., Wilt, J. H. W., Denost, Q., Deutsch, C., Dietz, D., Domingo, S., Dozois, E. J., Duff, M., Eglinton, T., Enrique-Navascues, J. M., Espin-Basany, E., Evans, M. D., Fearnhead, N. S., Flatmark, K., Fleming, F., Frizelle, F. A., Gallego, M. A., Garcia-Granero, E., Garcia-Sabrido, J. L., Gentilini, L., George, M. L., George, V., Ghouti, L., Giner, F., Ginther, N., Glynn, R., Golda, T., Griffiths, B., Harris, D. A., Hagemans, J. A. W., Hanchanale, V., Harji, D. P., Helewa, R. M., Heriot, A. G., Hochman, D., Hohenberger, W., Holm, T., Hompes, R., Jenkins, J. T., Kaffenberger, S., Kandaswamy, G. V., Kapur, S., Kanemitsu, Y., Kelley, S. R., Keller, D. S., Khan, M. S., Kiran, R. P., Kim, H., Kim, H. J., Koh, C. E., Kok, N. F. M., Kokelaar, R., Kontovounisios, C., Kristensen, H. O., Kroon, H. M., Kusters, M., Lago, V., Larsen, S. G., Larson, D. W., Law, W. L., Laurberg, S., Lee, P. J., Limbert, M., Lydrup, M. L., Lyons, A., Lynch, A. C., Mantyh, C., Mathis, K. L., Margues, C. F. S., Martling, A., Meijerink, W. J. H. J., Merkel, S., Mehta, A. M., Mcarthur, D. R., Mcdermott, F. D., Mcgrath, J. S., Malde, S., Mirnezami, A., Monson, J. R. T., Morton, J. R., Mullaney, T. G., Negoi, I., Neto, J. W. M., Nguyen, B., Nielsen, M. B., Nieuwenhuijzen, G. A. P., Nilsson, P. J., Oliver, A., O'Connell, P. R., O'Dwyer, S. T., Palmer, G., Pappou, E., Park, J., Patsouras, D., Pellino, G., Peterson, A. C., Poggioli, G., Proud, D., Quinn, M., Quyn, A., Radwan, R. W., Rasheed, S., Rasmussen, P. C., Regenbogen, S. E., Renehan, A., Rocha, R., Rochester, M., Rohila, J., Rothbarth, J., Rottoli, M., Roxburgh, C., Rutten, H. J. T., Ryan, E. J., Safar, B., Sagar, P. M., Sahai, A., Saklani, A., Sammour, T., Sayyed, R., Schizas, A. M. P., Schwarzkopf, E., Scripcariu, V., Selvasekar, C., Shaikh, I., Shellawell, G., Shida, D., Simpson, A., Smart, N. J., Smart, P., Smith, J. J., Solbakken, A. M., Solomon, M. J., Sorensen, M. M., Steele, S. R., Steffens, D., Stitzenberg, K., Stocchi, L., Stylianides, N. A., Swartling, T., Sumrien, H., Sutton, P. A., Swartking, T., Tan, E. J., Taylor, C., Tekkis, P. P., Teras, J., Thurairaja, R., Toh, E. L., Tsarkov, P., Tsukada, Y., Tsukamoto, S., Tuech, J. J., Turner, W. H., Tuynman, J. B., van Ramshorst, G. H., van Zoggel, D., Vasquez-Jimenez, W., Verhoef, C., Vizzielli, G., Voogt, E. L. K., Uehara, K., Wakeman, C., Warrier, S., Wasmuth, H. H., Weber, K., Weiser, M. R., Wheeler, J. M. D., Wild, J., Wilson, M., Wolthuis, A., Yano, H., Yip, B., Yip, J., Yoo, R. N., and Winter, D. C.

Subjects
Artificial intelligence, medicine.medical_treatment, Machine learning, computer.software_genre, Logistic regression, Tumours of the digestive tract Radboud Institute for Health Sciences [Radboudumc 14], SDG 3 - Good Health and Well-being, Medicine, Humans, Pelvic exenteration, Receiver operating characteristic, Artificial neural network, business.industry, Rectal Neoplasms, Deep learning, Gastroenterology, Prognosis, pelvic exenteration, Support vector machine, machine learning, Test set, colorectal surgery, Neoplasm Recurrence, Local, business, computer, Predictive modelling, artificial neural network
Abstract

Aim: We aim to compare machine learning with neural network performance in predicting R0 resection (R0), length of stay >14days (LOS), major complication rates at 30days postoperatively (COMP) and survival greater than 1 year (SURV) for patients having pelvic exenteration for locally advanced and recurrent rectal cancer. Method: A deep learning computer was built and the programming environment was established. The PelvEx Collaborative database was used which contains anonymized data on patients who underwent pelvic exenteration for locally advanced or locally recurrent colorectal cancer between 2004 and 2014. Logistic regression, a support vector machine and an artificial neural network (ANN) were trained. Twenty per cent of the data were used as a test set for calculating prediction accuracy for R0, LOS, COMP and SURV. Model performance was measured by plotting receiver operating characteristic (ROC) curves and calculating the area under the ROC curve (AUROC). Results: Machine learning models and ANNs were trained on 1147 cases. The AUROC for all outcome predictions ranged from 0.608 to 0.793 indicating modest to moderate predictive ability. The models performed best at predicting LOS >14days with an AUROC of 0.793 using preoperative and operative data. Visualized logistic regression model weights indicate a varying impact of variables on the outcome in question. Conclusion: This paper highlights the potential for predictive modelling of large international databases. Current data allow moderate predictive ability of both complex ANNs and more classic methods.

Published
2020

3. Predicting outcomes of pelvic exenteration using machine learning

Author

Dudurych, I, Kelly, ME, Aalbers, AGJ, Aziz, NA, Abecasis, N, Abraham-Nordling, M, Akiyoshi, T, Alberda, W, Albert, M, Andric, M, Angenete, E, Antoniou, A, Auer, R, Austin, KK, Aziz, O, Baker, RP, Bali, M, Baseckas, G, Bebington, B, Bedford, M, Bednarski, BK, Beets, GL, Berg, PL, Beynon, J, Biondo, S, Boyle, K, Bordeianou, L, Bremers, AB, Brunner, M, Buchwald, P, Bui, A, Burgess, A, Burger, JWA, Burling, D, Burns, E, Campain, N, Carvalhal, S, Castro, L, Caycedo-Marulanda, A, Chan, KKL, Chang, GJ, Chew, MH, Chok, AK, Chong, P, Christensen, HK, Clouston, H, Codd, M, Collins, D, Colquhoun, AJ, Corr, A, Coscia, M, Coyne, PE, Creavin, B, Croner, RS, Damjanovic, L, Daniels, IR, Davies, M, Davies, RJ, Delaney, CP, de Wilt, JHW, Denost, Q, Deutsch, C, Dietz, D, Domingo, S, Dozois, EJ, Duff, M, Eglinton, T, Enrique-Navascues, JM, Espin-Basany, E, Evans, MD, Fearnhead, NS, Flatmark, K, Fleming, F, Frizelle, FA, Gallego, MA, Garcia-Granero, E, Garcia-Sabrido, JL, Gentilini, L, George, ML, George, V, Ghouti, L, Giner, F, Ginther, N, Glynn, R, Golda, T, Griffiths, B, Harris, DA, Hagemans, JAW, Hanchanale, V, Harji, DP, Helewa, RM, Heriot, AG, Hochman, D, Hohenberger, W, Holm, T, Hompes, R, Jenkins, JT, Kaffenberger, S, Kandaswamy, GV, Kapur, S, Kanemitsu, Y, Kelley, SR, Keller, DS, Khan, MS, Kiran, RP, Kim, H, Kim, HJ, Koh, CE, Kok, NFM, Kokelaar, R, Kontovounisios, C, Kristensen, HO, Kroon, HM, Kusters, M, Lago, V, Larsen, SG, Larson, DW, Law, WL, Laurberg, S, Lee, PJ, Limbert, M, Lydrup, ML, Lyons, A, Lynch, AC, Mantyh, C, Mathis, KL, Margues, CFS, Martling, A, Meijerink, WJHJ, Merkel, S, Mehta, AM, McArthur, DR, McDermott, FD, McGrath, JS, Malde, S, Mirnezami, A, Monson, JRT, Morton, JR, Mullaney, TG, Negoi, I, Neto, JWM, Nguyen, B, Nielsen, MB, Nieuwenhuijzen, GAP, Nilsson, PJ, Oliver, A, O'Connell, PR, O'Dwyer, ST, Palmer, G, Pappou, E, Park, J, Patsouras, D, Pellino, G, Peterson, AC, Poggioli, G, Proud, D, Quinn, M, Quyn, A, Radwan, RW, Rasheed, S, Rasmussen, PC, Regenbogen, SE, Renehan, A, Rocha, R, Rochester, M, Rohila, J, Rothbarth, J, Rottoli, M, Roxburgh, C, Rutten, HJT, Ryan, EJ, Safar, B, Sagar, PM, Sahai, A, Saklani, A, Sammour, T, Sayyed, R, Schizas, AMP, Schwarzkopf, E, Scripcariu, V, Selvasekar, C, Shaikh, I, Shellawell, G, Shida, D, Simpson, A, Smart, NJ, Smart, P, Smith, JJ, Solbakken, AM, Solomon, MJ, Sorensen, MM, Steele, SR, Steffens, D, Stitzenberg, K, Stocchi, L, Stylianides, NA, Swartling, T, Sumrien, H, Sutton, PA, Swartking, T, Tan, EJ, Taylor, C, Tekkis, PP, Teras, J, Thurairaja, R, Toh, EL, Tsarkov, P, Tsukada, Y, Tsukamoto, S, Tuech, JJ, Turner, WH, Tuynman, JB, van Ramshorst, GH, van Zoggel, D, Vasquez-Jimenez, W, Verhoef, C, Vizzielli, G, Voogt, ELK, Uehara, K, Wakeman, C, Warrier, S, Wasmuth, HH, Weber, K, Weiser, MR, Wheeler, JMD, Wild, J, Wilson, M, Wolthuis, A, Yano, H, Yip, B, Yip, J, Yoo, RN, Winter, DC, Dudurych, I, Kelly, ME, Aalbers, AGJ, Aziz, NA, Abecasis, N, Abraham-Nordling, M, Akiyoshi, T, Alberda, W, Albert, M, Andric, M, Angenete, E, Antoniou, A, Auer, R, Austin, KK, Aziz, O, Baker, RP, Bali, M, Baseckas, G, Bebington, B, Bedford, M, Bednarski, BK, Beets, GL, Berg, PL, Beynon, J, Biondo, S, Boyle, K, Bordeianou, L, Bremers, AB, Brunner, M, Buchwald, P, Bui, A, Burgess, A, Burger, JWA, Burling, D, Burns, E, Campain, N, Carvalhal, S, Castro, L, Caycedo-Marulanda, A, Chan, KKL, Chang, GJ, Chew, MH, Chok, AK, Chong, P, Christensen, HK, Clouston, H, Codd, M, Collins, D, Colquhoun, AJ, Corr, A, Coscia, M, Coyne, PE, Creavin, B, Croner, RS, Damjanovic, L, Daniels, IR, Davies, M, Davies, RJ, Delaney, CP, de Wilt, JHW, Denost, Q, Deutsch, C, Dietz, D, Domingo, S, Dozois, EJ, Duff, M, Eglinton, T, Enrique-Navascues, JM, Espin-Basany, E, Evans, MD, Fearnhead, NS, Flatmark, K, Fleming, F, Frizelle, FA, Gallego, MA, Garcia-Granero, E, Garcia-Sabrido, JL, Gentilini, L, George, ML, George, V, Ghouti, L, Giner, F, Ginther, N, Glynn, R, Golda, T, Griffiths, B, Harris, DA, Hagemans, JAW, Hanchanale, V, Harji, DP, Helewa, RM, Heriot, AG, Hochman, D, Hohenberger, W, Holm, T, Hompes, R, Jenkins, JT, Kaffenberger, S, Kandaswamy, GV, Kapur, S, Kanemitsu, Y, Kelley, SR, Keller, DS, Khan, MS, Kiran, RP, Kim, H, Kim, HJ, Koh, CE, Kok, NFM, Kokelaar, R, Kontovounisios, C, Kristensen, HO, Kroon, HM, Kusters, M, Lago, V, Larsen, SG, Larson, DW, Law, WL, Laurberg, S, Lee, PJ, Limbert, M, Lydrup, ML, Lyons, A, Lynch, AC, Mantyh, C, Mathis, KL, Margues, CFS, Martling, A, Meijerink, WJHJ, Merkel, S, Mehta, AM, McArthur, DR, McDermott, FD, McGrath, JS, Malde, S, Mirnezami, A, Monson, JRT, Morton, JR, Mullaney, TG, Negoi, I, Neto, JWM, Nguyen, B, Nielsen, MB, Nieuwenhuijzen, GAP, Nilsson, PJ, Oliver, A, O'Connell, PR, O'Dwyer, ST, Palmer, G, Pappou, E, Park, J, Patsouras, D, Pellino, G, Peterson, AC, Poggioli, G, Proud, D, Quinn, M, Quyn, A, Radwan, RW, Rasheed, S, Rasmussen, PC, Regenbogen, SE, Renehan, A, Rocha, R, Rochester, M, Rohila, J, Rothbarth, J, Rottoli, M, Roxburgh, C, Rutten, HJT, Ryan, EJ, Safar, B, Sagar, PM, Sahai, A, Saklani, A, Sammour, T, Sayyed, R, Schizas, AMP, Schwarzkopf, E, Scripcariu, V, Selvasekar, C, Shaikh, I, Shellawell, G, Shida, D, Simpson, A, Smart, NJ, Smart, P, Smith, JJ, Solbakken, AM, Solomon, MJ, Sorensen, MM, Steele, SR, Steffens, D, Stitzenberg, K, Stocchi, L, Stylianides, NA, Swartling, T, Sumrien, H, Sutton, PA, Swartking, T, Tan, EJ, Taylor, C, Tekkis, PP, Teras, J, Thurairaja, R, Toh, EL, Tsarkov, P, Tsukada, Y, Tsukamoto, S, Tuech, JJ, Turner, WH, Tuynman, JB, van Ramshorst, GH, van Zoggel, D, Vasquez-Jimenez, W, Verhoef, C, Vizzielli, G, Voogt, ELK, Uehara, K, Wakeman, C, Warrier, S, Wasmuth, HH, Weber, K, Weiser, MR, Wheeler, JMD, Wild, J, Wilson, M, Wolthuis, A, Yano, H, Yip, B, Yip, J, Yoo, RN, and Winter, DC

Abstract

AIM: We aim to compare machine learning with neural network performance in predicting R0 resection (R0), length of stay > 14 days (LOS), major complication rates at 30 days postoperatively (COMP) and survival greater than 1 year (SURV) for patients having pelvic exenteration for locally advanced and recurrent rectal cancer. METHOD: A deep learning computer was built and the programming environment was established. The PelvEx Collaborative database was used which contains anonymized data on patients who underwent pelvic exenteration for locally advanced or locally recurrent colorectal cancer between 2004 and 2014. Logistic regression, a support vector machine and an artificial neural network (ANN) were trained. Twenty per cent of the data were used as a test set for calculating prediction accuracy for R0, LOS, COMP and SURV. Model performance was measured by plotting receiver operating characteristic (ROC) curves and calculating the area under the ROC curve (AUROC). RESULTS: Machine learning models and ANNs were trained on 1147 cases. The AUROC for all outcome predictions ranged from 0.608 to 0.793 indicating modest to moderate predictive ability. The models performed best at predicting LOS > 14 days with an AUROC of 0.793 using preoperative and operative data. Visualized logistic regression model weights indicate a varying impact of variables on the outcome in question. CONCLUSION: This paper highlights the potential for predictive modelling of large international databases. Current data allow moderate predictive ability of both complex ANNs and more classic methods.

Published
2020

Catalog

Books, media, physical & digital resources
See catalog results