Identification and validation of a machine learning model of complete response to radiation in rectal cancer reveals immune infiltrate and TGFβ as key predictors

Domingo E., Rathee S., Blake A., Samuel L., Murray G., Sebag-Montefiore D., Gollins S., West N., Begum R., Richman S., Quirke P., Redmond K., Chatzipli A., Barberis A., Hassanieh S., Mahmood U., Youdell M., McDermott U., Koelzer V., Leedham S., Tomlinson I., Dunne P., Blake A., Buffa F., Domingo E., Higgins G., Holmes C., Koelzer V., Leedham S., Maughan T., McKenna G., Robineau J., Tomlinson I., Youdell M., Quirke P., Richman S., Sebag-Montefiore D., Seymour M., West N., Dunne P., Kennedy R., Lawler M., Redmond K., Salto-Tellez M., Campbell P., Chatzipli A., Hardy C., McDermott U., Bach S., Beggs A., Cazier J-B., Middleton G., Morton D., Whalley C., Brown L., Kaplan R., Murray G., Wilson R., Adams R., Sullivan R., Samuel L., Harkin P., Walker S., Hill J., Wu C-H., Horgan D., Buffa FM., Maughan TS.

DOI

10.1016/j.ebiom.2024.105228

Type

Journal article

Publisher

Elsevier BV

Publication Date

2024-08-01T00:00:00+00:00

Volume

106

Pages

105228 - 105228

Total pages

0

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