Noninvasive assessment of breast cancer molecular subtypes on multiparametric MRI using convolutional neural network with transfer learning

Abstract Background To evaluate the performances of multiparametric MRI‐based convolutional neural networks (CNNs) for the preoperative assessment of breast cancer molecular subtypes. Methods A total of 136 patients with 136 pathologically confirmed invasive breast cancers were randomly divided into...
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Autor*in:	Haolin Yin [verfasserIn] Lutian Bai [verfasserIn] Huihui Jia [verfasserIn] Guangwu Lin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	breast cancer breast MRI deep learning molecular subtypes neural network

Übergeordnetes Werk:	In: Thoracic Cancer - Wiley, 2015, 13(2022), 22, Seite 3183-3191
Übergeordnetes Werk:	volume:13 ; year:2022 ; number:22 ; pages:3183-3191

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1111/1759-7714.14673

Katalog-ID:	DOAJ026000024

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520			\|a Abstract Background To evaluate the performances of multiparametric MRI‐based convolutional neural networks (CNNs) for the preoperative assessment of breast cancer molecular subtypes. Methods A total of 136 patients with 136 pathologically confirmed invasive breast cancers were randomly divided into training, validation, and testing sets in this retrospective study. The CNN models were established based on contrast‐enhanced T1‐weighted imaging (T1C), Apparent diffusion coefficient (ADC), and T2‐weighted imaging (T2W) using the training and validation sets. The performances of CNN models were evaluated on the testing set. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated to assess the performance. Results For the separation of each subtype from other subtypes on the testing set, the T1C‐based models yielded AUCs from 0.762 to 0.920; the ADC‐based models yielded AUCs from 0.686 to 0.851; and the T2W‐based models achieved AUCs from 0.639 to 0.697. Conclusion T1C‐based models performed better than ADC‐based models and T2W‐based models in assessing the breast cancer molecular subtypes. The discriminating performances of our CNN models for triple negative and human epidermal growth factor receptor 2‐enriched subtypes were better than that of luminal A and luminal B subtypes.
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653		0	\|a Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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allfields	10.1111/1759-7714.14673 doi (DE-627)DOAJ026000024 (DE-599)DOAJ520e753db98a4af5a170e2ade18c0bd8 DE-627 ger DE-627 rakwb eng RC254-282 Haolin Yin verfasserin aut Noninvasive assessment of breast cancer molecular subtypes on multiparametric MRI using convolutional neural network with transfer learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background To evaluate the performances of multiparametric MRI‐based convolutional neural networks (CNNs) for the preoperative assessment of breast cancer molecular subtypes. Methods A total of 136 patients with 136 pathologically confirmed invasive breast cancers were randomly divided into training, validation, and testing sets in this retrospective study. The CNN models were established based on contrast‐enhanced T1‐weighted imaging (T1C), Apparent diffusion coefficient (ADC), and T2‐weighted imaging (T2W) using the training and validation sets. The performances of CNN models were evaluated on the testing set. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated to assess the performance. Results For the separation of each subtype from other subtypes on the testing set, the T1C‐based models yielded AUCs from 0.762 to 0.920; the ADC‐based models yielded AUCs from 0.686 to 0.851; and the T2W‐based models achieved AUCs from 0.639 to 0.697. Conclusion T1C‐based models performed better than ADC‐based models and T2W‐based models in assessing the breast cancer molecular subtypes. The discriminating performances of our CNN models for triple negative and human epidermal growth factor receptor 2‐enriched subtypes were better than that of luminal A and luminal B subtypes. breast cancer breast MRI deep learning molecular subtypes neural network Neoplasms. Tumors. Oncology. Including cancer and carcinogens Lutian Bai verfasserin aut Huihui Jia verfasserin aut Guangwu Lin verfasserin aut In Thoracic Cancer Wiley, 2015 13(2022), 22, Seite 3183-3191 (DE-627)629836809 (DE-600)2559245-2 17597714 nnns volume:13 year:2022 number:22 pages:3183-3191 https://doi.org/10.1111/1759-7714.14673 kostenfrei https://doaj.org/article/520e753db98a4af5a170e2ade18c0bd8 kostenfrei https://doi.org/10.1111/1759-7714.14673 kostenfrei https://doaj.org/toc/1759-7706 Journal toc kostenfrei https://doaj.org/toc/1759-7714 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022 22 3183-3191
spelling	10.1111/1759-7714.14673 doi (DE-627)DOAJ026000024 (DE-599)DOAJ520e753db98a4af5a170e2ade18c0bd8 DE-627 ger DE-627 rakwb eng RC254-282 Haolin Yin verfasserin aut Noninvasive assessment of breast cancer molecular subtypes on multiparametric MRI using convolutional neural network with transfer learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background To evaluate the performances of multiparametric MRI‐based convolutional neural networks (CNNs) for the preoperative assessment of breast cancer molecular subtypes. Methods A total of 136 patients with 136 pathologically confirmed invasive breast cancers were randomly divided into training, validation, and testing sets in this retrospective study. The CNN models were established based on contrast‐enhanced T1‐weighted imaging (T1C), Apparent diffusion coefficient (ADC), and T2‐weighted imaging (T2W) using the training and validation sets. The performances of CNN models were evaluated on the testing set. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated to assess the performance. Results For the separation of each subtype from other subtypes on the testing set, the T1C‐based models yielded AUCs from 0.762 to 0.920; the ADC‐based models yielded AUCs from 0.686 to 0.851; and the T2W‐based models achieved AUCs from 0.639 to 0.697. Conclusion T1C‐based models performed better than ADC‐based models and T2W‐based models in assessing the breast cancer molecular subtypes. The discriminating performances of our CNN models for triple negative and human epidermal growth factor receptor 2‐enriched subtypes were better than that of luminal A and luminal B subtypes. breast cancer breast MRI deep learning molecular subtypes neural network Neoplasms. Tumors. Oncology. Including cancer and carcinogens Lutian Bai verfasserin aut Huihui Jia verfasserin aut Guangwu Lin verfasserin aut In Thoracic Cancer Wiley, 2015 13(2022), 22, Seite 3183-3191 (DE-627)629836809 (DE-600)2559245-2 17597714 nnns volume:13 year:2022 number:22 pages:3183-3191 https://doi.org/10.1111/1759-7714.14673 kostenfrei https://doaj.org/article/520e753db98a4af5a170e2ade18c0bd8 kostenfrei https://doi.org/10.1111/1759-7714.14673 kostenfrei https://doaj.org/toc/1759-7706 Journal toc kostenfrei https://doaj.org/toc/1759-7714 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022 22 3183-3191
allfields_unstemmed	10.1111/1759-7714.14673 doi (DE-627)DOAJ026000024 (DE-599)DOAJ520e753db98a4af5a170e2ade18c0bd8 DE-627 ger DE-627 rakwb eng RC254-282 Haolin Yin verfasserin aut Noninvasive assessment of breast cancer molecular subtypes on multiparametric MRI using convolutional neural network with transfer learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background To evaluate the performances of multiparametric MRI‐based convolutional neural networks (CNNs) for the preoperative assessment of breast cancer molecular subtypes. Methods A total of 136 patients with 136 pathologically confirmed invasive breast cancers were randomly divided into training, validation, and testing sets in this retrospective study. The CNN models were established based on contrast‐enhanced T1‐weighted imaging (T1C), Apparent diffusion coefficient (ADC), and T2‐weighted imaging (T2W) using the training and validation sets. The performances of CNN models were evaluated on the testing set. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated to assess the performance. Results For the separation of each subtype from other subtypes on the testing set, the T1C‐based models yielded AUCs from 0.762 to 0.920; the ADC‐based models yielded AUCs from 0.686 to 0.851; and the T2W‐based models achieved AUCs from 0.639 to 0.697. Conclusion T1C‐based models performed better than ADC‐based models and T2W‐based models in assessing the breast cancer molecular subtypes. The discriminating performances of our CNN models for triple negative and human epidermal growth factor receptor 2‐enriched subtypes were better than that of luminal A and luminal B subtypes. breast cancer breast MRI deep learning molecular subtypes neural network Neoplasms. Tumors. Oncology. Including cancer and carcinogens Lutian Bai verfasserin aut Huihui Jia verfasserin aut Guangwu Lin verfasserin aut In Thoracic Cancer Wiley, 2015 13(2022), 22, Seite 3183-3191 (DE-627)629836809 (DE-600)2559245-2 17597714 nnns volume:13 year:2022 number:22 pages:3183-3191 https://doi.org/10.1111/1759-7714.14673 kostenfrei https://doaj.org/article/520e753db98a4af5a170e2ade18c0bd8 kostenfrei https://doi.org/10.1111/1759-7714.14673 kostenfrei https://doaj.org/toc/1759-7706 Journal toc kostenfrei https://doaj.org/toc/1759-7714 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022 22 3183-3191
allfieldsGer	10.1111/1759-7714.14673 doi (DE-627)DOAJ026000024 (DE-599)DOAJ520e753db98a4af5a170e2ade18c0bd8 DE-627 ger DE-627 rakwb eng RC254-282 Haolin Yin verfasserin aut Noninvasive assessment of breast cancer molecular subtypes on multiparametric MRI using convolutional neural network with transfer learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background To evaluate the performances of multiparametric MRI‐based convolutional neural networks (CNNs) for the preoperative assessment of breast cancer molecular subtypes. Methods A total of 136 patients with 136 pathologically confirmed invasive breast cancers were randomly divided into training, validation, and testing sets in this retrospective study. The CNN models were established based on contrast‐enhanced T1‐weighted imaging (T1C), Apparent diffusion coefficient (ADC), and T2‐weighted imaging (T2W) using the training and validation sets. The performances of CNN models were evaluated on the testing set. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated to assess the performance. Results For the separation of each subtype from other subtypes on the testing set, the T1C‐based models yielded AUCs from 0.762 to 0.920; the ADC‐based models yielded AUCs from 0.686 to 0.851; and the T2W‐based models achieved AUCs from 0.639 to 0.697. Conclusion T1C‐based models performed better than ADC‐based models and T2W‐based models in assessing the breast cancer molecular subtypes. The discriminating performances of our CNN models for triple negative and human epidermal growth factor receptor 2‐enriched subtypes were better than that of luminal A and luminal B subtypes. breast cancer breast MRI deep learning molecular subtypes neural network Neoplasms. Tumors. Oncology. Including cancer and carcinogens Lutian Bai verfasserin aut Huihui Jia verfasserin aut Guangwu Lin verfasserin aut In Thoracic Cancer Wiley, 2015 13(2022), 22, Seite 3183-3191 (DE-627)629836809 (DE-600)2559245-2 17597714 nnns volume:13 year:2022 number:22 pages:3183-3191 https://doi.org/10.1111/1759-7714.14673 kostenfrei https://doaj.org/article/520e753db98a4af5a170e2ade18c0bd8 kostenfrei https://doi.org/10.1111/1759-7714.14673 kostenfrei https://doaj.org/toc/1759-7706 Journal toc kostenfrei https://doaj.org/toc/1759-7714 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022 22 3183-3191
allfieldsSound	10.1111/1759-7714.14673 doi (DE-627)DOAJ026000024 (DE-599)DOAJ520e753db98a4af5a170e2ade18c0bd8 DE-627 ger DE-627 rakwb eng RC254-282 Haolin Yin verfasserin aut Noninvasive assessment of breast cancer molecular subtypes on multiparametric MRI using convolutional neural network with transfer learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background To evaluate the performances of multiparametric MRI‐based convolutional neural networks (CNNs) for the preoperative assessment of breast cancer molecular subtypes. Methods A total of 136 patients with 136 pathologically confirmed invasive breast cancers were randomly divided into training, validation, and testing sets in this retrospective study. The CNN models were established based on contrast‐enhanced T1‐weighted imaging (T1C), Apparent diffusion coefficient (ADC), and T2‐weighted imaging (T2W) using the training and validation sets. The performances of CNN models were evaluated on the testing set. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated to assess the performance. Results For the separation of each subtype from other subtypes on the testing set, the T1C‐based models yielded AUCs from 0.762 to 0.920; the ADC‐based models yielded AUCs from 0.686 to 0.851; and the T2W‐based models achieved AUCs from 0.639 to 0.697. Conclusion T1C‐based models performed better than ADC‐based models and T2W‐based models in assessing the breast cancer molecular subtypes. The discriminating performances of our CNN models for triple negative and human epidermal growth factor receptor 2‐enriched subtypes were better than that of luminal A and luminal B subtypes. breast cancer breast MRI deep learning molecular subtypes neural network Neoplasms. Tumors. Oncology. Including cancer and carcinogens Lutian Bai verfasserin aut Huihui Jia verfasserin aut Guangwu Lin verfasserin aut In Thoracic Cancer Wiley, 2015 13(2022), 22, Seite 3183-3191 (DE-627)629836809 (DE-600)2559245-2 17597714 nnns volume:13 year:2022 number:22 pages:3183-3191 https://doi.org/10.1111/1759-7714.14673 kostenfrei https://doaj.org/article/520e753db98a4af5a170e2ade18c0bd8 kostenfrei https://doi.org/10.1111/1759-7714.14673 kostenfrei https://doaj.org/toc/1759-7706 Journal toc kostenfrei https://doaj.org/toc/1759-7714 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022 22 3183-3191
language	English
source	In Thoracic Cancer 13(2022), 22, Seite 3183-3191 volume:13 year:2022 number:22 pages:3183-3191
sourceStr	In Thoracic Cancer 13(2022), 22, Seite 3183-3191 volume:13 year:2022 number:22 pages:3183-3191
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	breast cancer breast MRI deep learning molecular subtypes neural network Neoplasms. Tumors. Oncology. Including cancer and carcinogens
isfreeaccess_bool	true
container_title	Thoracic Cancer
authorswithroles_txt_mv	Haolin Yin @@aut@@ Lutian Bai @@aut@@ Huihui Jia @@aut@@ Guangwu Lin @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	629836809
id	DOAJ026000024
language_de	englisch
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Methods A total of 136 patients with 136 pathologically confirmed invasive breast cancers were randomly divided into training, validation, and testing sets in this retrospective study. The CNN models were established based on contrast‐enhanced T1‐weighted imaging (T1C), Apparent diffusion coefficient (ADC), and T2‐weighted imaging (T2W) using the training and validation sets. The performances of CNN models were evaluated on the testing set. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated to assess the performance. Results For the separation of each subtype from other subtypes on the testing set, the T1C‐based models yielded AUCs from 0.762 to 0.920; the ADC‐based models yielded AUCs from 0.686 to 0.851; and the T2W‐based models achieved AUCs from 0.639 to 0.697. Conclusion T1C‐based models performed better than ADC‐based models and T2W‐based models in assessing the breast cancer molecular subtypes. 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callnumber-first	R - Medicine
author	Haolin Yin
spellingShingle	Haolin Yin misc RC254-282 misc breast cancer misc breast MRI misc deep learning misc molecular subtypes misc neural network misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens Noninvasive assessment of breast cancer molecular subtypes on multiparametric MRI using convolutional neural network with transfer learning
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topic_title	RC254-282 Noninvasive assessment of breast cancer molecular subtypes on multiparametric MRI using convolutional neural network with transfer learning breast cancer breast MRI deep learning molecular subtypes neural network
topic	misc RC254-282 misc breast cancer misc breast MRI misc deep learning misc molecular subtypes misc neural network misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_unstemmed	misc RC254-282 misc breast cancer misc breast MRI misc deep learning misc molecular subtypes misc neural network misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_browse	misc RC254-282 misc breast cancer misc breast MRI misc deep learning misc molecular subtypes misc neural network misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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title	Noninvasive assessment of breast cancer molecular subtypes on multiparametric MRI using convolutional neural network with transfer learning
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title_full	Noninvasive assessment of breast cancer molecular subtypes on multiparametric MRI using convolutional neural network with transfer learning
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title_sort	noninvasive assessment of breast cancer molecular subtypes on multiparametric mri using convolutional neural network with transfer learning
callnumber	RC254-282
title_auth	Noninvasive assessment of breast cancer molecular subtypes on multiparametric MRI using convolutional neural network with transfer learning
abstract	Abstract Background To evaluate the performances of multiparametric MRI‐based convolutional neural networks (CNNs) for the preoperative assessment of breast cancer molecular subtypes. Methods A total of 136 patients with 136 pathologically confirmed invasive breast cancers were randomly divided into training, validation, and testing sets in this retrospective study. The CNN models were established based on contrast‐enhanced T1‐weighted imaging (T1C), Apparent diffusion coefficient (ADC), and T2‐weighted imaging (T2W) using the training and validation sets. The performances of CNN models were evaluated on the testing set. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated to assess the performance. Results For the separation of each subtype from other subtypes on the testing set, the T1C‐based models yielded AUCs from 0.762 to 0.920; the ADC‐based models yielded AUCs from 0.686 to 0.851; and the T2W‐based models achieved AUCs from 0.639 to 0.697. Conclusion T1C‐based models performed better than ADC‐based models and T2W‐based models in assessing the breast cancer molecular subtypes. The discriminating performances of our CNN models for triple negative and human epidermal growth factor receptor 2‐enriched subtypes were better than that of luminal A and luminal B subtypes.
abstractGer	Abstract Background To evaluate the performances of multiparametric MRI‐based convolutional neural networks (CNNs) for the preoperative assessment of breast cancer molecular subtypes. Methods A total of 136 patients with 136 pathologically confirmed invasive breast cancers were randomly divided into training, validation, and testing sets in this retrospective study. The CNN models were established based on contrast‐enhanced T1‐weighted imaging (T1C), Apparent diffusion coefficient (ADC), and T2‐weighted imaging (T2W) using the training and validation sets. The performances of CNN models were evaluated on the testing set. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated to assess the performance. Results For the separation of each subtype from other subtypes on the testing set, the T1C‐based models yielded AUCs from 0.762 to 0.920; the ADC‐based models yielded AUCs from 0.686 to 0.851; and the T2W‐based models achieved AUCs from 0.639 to 0.697. Conclusion T1C‐based models performed better than ADC‐based models and T2W‐based models in assessing the breast cancer molecular subtypes. The discriminating performances of our CNN models for triple negative and human epidermal growth factor receptor 2‐enriched subtypes were better than that of luminal A and luminal B subtypes.
abstract_unstemmed	Abstract Background To evaluate the performances of multiparametric MRI‐based convolutional neural networks (CNNs) for the preoperative assessment of breast cancer molecular subtypes. Methods A total of 136 patients with 136 pathologically confirmed invasive breast cancers were randomly divided into training, validation, and testing sets in this retrospective study. The CNN models were established based on contrast‐enhanced T1‐weighted imaging (T1C), Apparent diffusion coefficient (ADC), and T2‐weighted imaging (T2W) using the training and validation sets. The performances of CNN models were evaluated on the testing set. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated to assess the performance. Results For the separation of each subtype from other subtypes on the testing set, the T1C‐based models yielded AUCs from 0.762 to 0.920; the ADC‐based models yielded AUCs from 0.686 to 0.851; and the T2W‐based models achieved AUCs from 0.639 to 0.697. Conclusion T1C‐based models performed better than ADC‐based models and T2W‐based models in assessing the breast cancer molecular subtypes. The discriminating performances of our CNN models for triple negative and human epidermal growth factor receptor 2‐enriched subtypes were better than that of luminal A and luminal B subtypes.
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title_short	Noninvasive assessment of breast cancer molecular subtypes on multiparametric MRI using convolutional neural network with transfer learning
url	https://doi.org/10.1111/1759-7714.14673 https://doaj.org/article/520e753db98a4af5a170e2ade18c0bd8 https://doaj.org/toc/1759-7706 https://doaj.org/toc/1759-7714
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Methods A total of 136 patients with 136 pathologically confirmed invasive breast cancers were randomly divided into training, validation, and testing sets in this retrospective study. The CNN models were established based on contrast‐enhanced T1‐weighted imaging (T1C), Apparent diffusion coefficient (ADC), and T2‐weighted imaging (T2W) using the training and validation sets. The performances of CNN models were evaluated on the testing set. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated to assess the performance. Results For the separation of each subtype from other subtypes on the testing set, the T1C‐based models yielded AUCs from 0.762 to 0.920; the ADC‐based models yielded AUCs from 0.686 to 0.851; and the T2W‐based models achieved AUCs from 0.639 to 0.697. Conclusion T1C‐based models performed better than ADC‐based models and T2W‐based models in assessing the breast cancer molecular subtypes. 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Noninvasive assessment of breast cancer molecular subtypes on multiparametric MRI using convolutional neural network with transfer learning

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