Citations 2
MRI Deep Learning for Differentiating Glioblastoma, IDH Wild-type from Central Nervous System Diffuse Large B-cell Lymphoma
Moassefi, Mana , Decker, Paul A. , Conte, Gian Marco , Kosel, Matthew L., Molinaro, Annette M. , Niederschweiberer, Moritz , Nikanpour, Yalda , Ruff, Michael W. , Burns, Terry C. , Farooq, Umar , Derdeyn, C, Jenkins, RB , Lachance, Daniel H , Eckel‐Passow, Jeanette E. , Kosel, Matthew , Niederschweiberer, Moritz A , Derdeyn, Colin P , Habermann, Thomas , Cerhan, James R , Greenlee, Jeremy D W , Howard, Matthew A , Slager, Susan L , Vaubel, Rachael A , Lachance, Daniel , Erickson, Bradley J , Tobin, W Oliver , Eckel-Passow, Jeanette E , Farooq, Umar , Conte, Gian Marco , Burns, Terry C , Moassefi, Mana , Decker, Paul A. , Kosel, Matthew , Molinaro, Annette M. , Derdeyn, Colin P. , Habermann, Thomas M. , Cerhan, James R. , Greenlee, Jeremy D.W. , Howard, Matthew A. , Slager, Susan L. , Vaubel, Rachael A. , Jenkins, Robert B. , Erickson, Bradley J.
Cancer Research Communications | May 4, 2026 | Volume: 6 | Issue: 5 | Pages: 1168-1179
Abstract Glioblastoma (GBM), isocitrate dehydrogenase wild-type (IDHwt) and central nervous system diffuse large B-cell lymphoma (CNS-DLBCL) are aggressive brain tumors with overlapping MRI features, yet distinct treatment approaches. Noninvasive tools are needed to aid in differential diagnosis. Deep learning on T1 postcontrast and T2-weighted MRI sequences were used to differentiate GBM and CNS-DLBCL. A three-stage temporal study design was utilized. Model development was performed on 146 patients with CNS-DLBCL and 146 age-matched, sex-matched, and MRI year–matched patients with GBM diagnosed at Mayo Clinic between 1998 and 2019. Models were tested on independent temporal test cohorts. Initial testing included 240 independent GBM diagnosed at Mayo Clinic between 1998 and 2019. The prospective test cohort included 37 patients with CNS-DLBCL and 256 patients with GBM diagnosed at Mayo Clinic after January 1, 2020, and 36 patients with CNS-DLBCL diagnosed at an external institution. Of the patients diagnosed at Mayo Clinic, 47% had MRIs generated from non-Mayo institutions. Two different model approaches were compared: (i) ensemble approach using area under the receiver operating characteristic curve (AUC) and cross-validation for model selection and (ii) loss approach minimizing cross-entropy loss and cross-validation to evaluate prediction performance. The AUCs on the prospective test cohort were 0.84 [95% confidence interval (CI), 0.78–0.90] and 0.83 (95% CI, 0.77–0.88) for the ensemble and loss approaches, respectively. Stability of ensemble prediction improved with the increasing number of models. Stratified AUC analysis demonstrated consistent performance across sex and age. We utilized a robust temporal study design and applied 2 different analytic approaches to develop a classification model. The findings confirm the feasibility of using MRI-based deep learning models to differentiate GBM from CNS-DLBCL. Significance: GBM, IDHwt and CNS-DLBCL are aggressive brain tumors with overlapping MRI features, yet distinct treatment approaches. Noninvasive tools are needed to aid in differential diagnosis. We developed MRI-based deep learning models to differentiate GBM, IDHwt from CNS-DLBCL using a rigorous three-stage temporal design that included prospective validation. The model AUC on a prospective cohort was 0.84.
19p13.1 Is a Triple-Negative–Specific Breast Cancer Susceptibility Locus
Stevens, Kristen N., Fredericksen, Zachary, Vachon, Celine M. , Margolin, Sara , Lindblom, Annika , Nevanlinna, Heli , Greco, Dario , Aittomäki, Kristiina, Blomqvist, Carl , Chang‐Claude, Jenny , Vrieling, Alina , Flesch‐Janys, Dieter, Sinn, Hans‐Peter , Wang‐Gohrke, Shan , Nickels, Stefan , Brauch, Hiltrud , Ko, Yon‐Dschun, Fischer, Hans‐Peter , Schmutzler, Rita K., Meindl, Alfons, Bartram, Claus R., Schott, Sarah , Engel, Christoph , Weaver, JoEllen , Sharma, Priyanka , Brenner, Hermann , Stegmaier, Christa, Miron, Penelope , Yannoukakos, Drakoulis , Stavropoulou, Alexandra , Fountzilas, George , Swann, Ruth , Dwek, Miriam , Perkins, Annie, Zamora, María Pilar, Bojesen, Stig E. , Nielsen, Sune F. , Nordestgaard, Børge G. , Flyger, Henrik, Guénel, Pascal , Truong, Thérèse , Ménégaux, F. , Cordina‐Duverger, Emilie, Burwinkel, Barbara , Marmé, Frederick, Schneeweiß, Andreas , Sohn, Christof , Sawyer, Elinor J. , Tomlinson, Ian , Kerin, Michael J. , Peto, Julian , Johnson, Nichola , dos‐Santos‐Silva, Isabel , Fasching, Peter A. , Beckmann, Matthias W. , Hartmann, Arndt , Ekici, Arif B. , Lophatananon, Artitaya, Muir, Kenneth , Puttawibul, Puttisak, Wiangnon, Surapon , Schmidt, Marjanka K. , Braaf, Linde M., Rosenberg, Efraim H. , Hopper, John L. , Apicella, Carmel, Park, Daniel J. , Southey, Melissa C. , Swerdlow, Anthony J. , Ashworth, Alan , Orr, Nicholas, Schoemaker, Minouk J. , Dur, Christina Clarke, Shen, Chen‐Yang , Yu, Jyh-Cherng, Hsu, Huan-Ming, Hsiung, Chia‐Ni , Hamann, Ute , Dünnebier, Thomas, Rüdiger, Thomas , Ulmer, Hans Ulrich, Dunning, Alison M. , Humphreys, Manjeet K., Wang, Qin , Cox, Angela , Cross, Simon S. , Reed, Malcolm , Czene, Kamila , Ademuyiwa, Foluso O. , Hwang, Helena, Eccles, Diana M., García‐Closas, Montserrat , Figueroa, Jonine D. , Sherman, Mark E., Lissowska, Jolanta , Seynaeve, Caroline, Tollenaar, Rob A.�E.�M. , Hooning, Maartje J. , Andrulis, Irene L. , Knight, Julia A. , Glendon, Gord, Mulligan, Anna Marie , Winqvist, Robert , Pylkäs, Katri , Jukkola‐Vuorinen, Arja , Grip, Mervi, John, Esther M. , Miron, Alexander, Alnæs, Grethe Grenaker, Kristensen, Vessela, Børresen‐Dale, Anne‐Lise , Giles, Graham G. , Baglietto, Laura , McLean, Catriona , Severi, Gianluca , Kosel, Matthew L., Pankratz, V.S., Slager, Susan, Radice, Paolo , Peterlongo, Paolo , Manoukian, Siranoush , Barile, Monica, Lambrechts, Diether , Hatse, Sigrid , Dieudonné, Anne-Sophie, Christiaens, Marie-Rose, Chenevix‐Trench, Georgia , Chen, Xiaoqing, Mannermaa, Arto, Kosma, Veli‐Matti , Hartikainen, Jaana M. , Soini, Ylermi, Easton, Douglas F., Eccles, Diana , Orr, Nick , Thorne, Heather , Purrington, Kristen S. , Lambrechts, Diether , Vachon, Celine M. , Wang, Xianshu , Meindl, Alfons, Godwin, Andrew K. , Pathak, Harsh B. , Müller, Heiko , Arndt, Volker , Gogas, Helen J , Milne, Roger L. , Benı́tez, Javier , Pérez, José Ignacio Arias , Fletcher, Olivia , Broeks, Annegien , Orr, Nicholas, Anton‐Culver, Hoda , Ziogas, Argyrios , Bernstein, Leslie , Pharoah, Paul D.P. , Hall, Per N , Ambrosone, Christine B. , Eccles, Diana M., Devilee, Peter , Kristensen, Vessela N. , Kosel, Matthew , Olson, Janet E. , Beesley, Jonathan , Chen, Xiaoqing, Mannermaa, Arto, Couch, Fergus J. , Nevanlinna, H , Lambrechts, Diether , Giles, Graham G , Peterlongo, Paolo , Eccles, Diana , Swerdlow, Anthony J , Radice, Paolo , Lissowska, Jolanta , John, Esther M , Southey, Melissa C , Sinn, Peter , Muir, Kenneth , Dunning, Alison M , Lambrechts, Diether , Brenner, Hermann , Cox, Angela , Guénel, Pascal , García-Closas, Montserrat , Schmidt, Marjanka K , Severi, Gianluca , Ekici, Arif B , Hartikainen, Jaana M , Engel, Christoph , Chenevix-Trench, Georgia , Figueroa, Jonine D , Czene, Kamila , Ademuyiwa, Foluso O , Pylkäs, Katri , Yannoukakos, Drakoulis , Manoukian, Siranoush , Baglietto, Laura , Bojesen, Stig E , Purrington, Kristen S , Sharma, Priyanka , Schoemaker, Minouk J , Truong, Therese , Mclean, Catriona , Brauch, Hiltrud , Lindblom, Annika , Sawyer, Elinor J , Orr, Nick
Cancer Research | February 13, 2012 | Volume: 72 | Issue: 7 | Pages: 1795-1803
Abstract The 19p13.1 breast cancer susceptibility locus is a modifier of breast cancer risk in BRCA1 mutation carriers and is also associated with the risk of ovarian cancer. Here, we investigated 19p13.1 variation and risk of breast cancer subtypes, defined by estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2) status, using 48,869 breast cancer cases and 49,787 controls from the Breast Cancer Association Consortium (BCAC). Variants from 19p13.1 were not associated with breast cancer overall or with ER-positive breast cancer but were significantly associated with ER-negative breast cancer risk [rs8170 OR, 1.10; 95% confidence interval (CI), 1.05–1.15; P = 3.49 × 10−5] and triple-negative (ER-, PR-, and HER2-negative) breast cancer (rs8170: OR, 1.22; 95% CI, 1.13–1.31; P = 2.22 × 10−7). However, rs8170 was no longer associated with ER-negative breast cancer risk when triple-negative cases were excluded (OR, 0.98; 95% CI, 0.89–1.07; P = 0.62). In addition, a combined analysis of triple-negative cases from BCAC and the Triple Negative Breast Cancer Consortium (TNBCC; N = 3,566) identified a genome-wide significant association between rs8170 and triple-negative breast cancer risk (OR, 1.25; 95% CI, 1.18–1.33; P = 3.31 × 10−13]. Thus, 19p13.1 is the first triple-negative–specific breast cancer risk locus and the first locus specific to a histologic subtype defined by ER, PR, and HER2 to be identified. These findings provide convincing evidence that genetic susceptibility to breast cancer varies by tumor subtype and that triple-negative tumors and other subtypes likely arise through distinct etiologic pathways. Cancer Res; 72(7); 1795–803. ©2012 AACR.