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1. The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

Author

Moawad, Ahmed W., Janas, Anastasia, Baid, Ujjwal, Ramakrishnan, Divya, Saluja, Rachit, Ashraf, Nader, Jekel, Leon, Amiruddin, Raisa, Adewole, Maruf, Albrecht, Jake, Anazodo, Udunna, Aneja, Sanjay, Anwar, Syed Muhammad, Bergquist, Timothy, Calabrese, Evan, Chiang, Veronica, Chung, Verena, Conte, Gian Marco Marco, Dako, Farouk, Eddy, James, Ezhov, Ivan, Familiar, Ariana, Farahani, Keyvan, Iglesias, Juan Eugenio, Jiang, Zhifan, Johanson, Elaine, Kazerooni, Anahita Fathi, Kofler, Florian, Krantchev, Kiril, LaBella, Dominic, Van Leemput, Koen, Li, Hongwei Bran, Linguraru, Marius George, Link, Katherine E., Liu, Xinyang, Maleki, Nazanin, Meier, Zeke, Menze, Bjoern H, Moy, Harrison, Osenberg, Klara, Piraud, Marie, Reitman, Zachary, Shinohara, Russel Takeshi, Tahon, Nourel hoda, Nada, Ayman, Velichko, Yuri S., Wang, Chunhao, Wiestler, Benedikt, Wiggins, Walter, Shafique, Umber, Willms, Klara, Avesta, Arman, Bousabarah, Khaled, Chakrabarty, Satrajit, Gennaro, Nicolo, Holler, Wolfgang, Kaur, Manpreet, LaMontagne, Pamela, Lin, MingDe, Lost, Jan, Marcus, Daniel S., Maresca, Ryan, Merkaj, Sarah, Nada, Ayaman, Pedersen, Gabriel Cassinelli, von Reppert, Marc, Sotiras, Aristeidis, Teytelboym, Oleg, Tillmans, Niklas, Westerhoff, Malte, Youssef, Ayda, Godfrey, Devon, Floyd, Scott, Rauschecker, Andreas, Villanueva-Meyer, Javier, Pflüger, Irada, Cho, Jaeyoung, Bendszus, Martin, Brugnara, Gianluca, Cramer, Justin, Perez-Carillo, Gloria J. Guzman, Johnson, Derek R., Kam, Anthony, Kwan, Benjamin Yin Ming, Lai, Lillian, Lall, Neil U., Memon, Fatima, Patro, Satya Narayana, Petrovic, Bojan, So, Tiffany Y., Thompson, Gerard, Wu, Lei, Schrickel, E. Brooke, Bansal, Anu, Barkhof, Frederik, Besada, Cristina, Chu, Sammy, Druzgal, Jason, Dusoi, Alexandru, Farage, Luciano, Feltrin, Fabricio, Fong, Amy, Fung, Steve H., Gray, R. Ian, Ikuta, Ichiro, Iv, Michael, Postma, Alida A., Mahajan, Amit, Joyner, David, Krumpelman, Chase, Letourneau-Guillon, Laurent, Lincoln, Christie M., Maros, Mate E., Miller, Elka, Morón, Fanny, Nimchinsky, Esther A., Ozsarlak, Ozkan, Patel, Uresh, Rohatgi, Saurabh, Saha, Atin, Sayah, Anousheh, Schwartz, Eric D., Shih, Robert, Shiroishi, Mark S., Small, Juan E., Tanwar, Manoj, Valerie, Jewels, Weinberg, Brent D., White, Matthew L., Young, Robert, Zohrabian, Vahe M., Azizova, Aynur, Brüßeler, Melanie Maria Theresa, Fehringer, Pascal, Ghonim, Mohanad, Ghonim, Mohamed, Gkampenis, Athanasios, Okar, Abdullah, Pasquini, Luca, Sharifi, Yasaman, Singh, Gagandeep, Sollmann, Nico, Soumala, Theodora, Taherzadeh, Mahsa, Yordanov, Nikolay, Vollmuth, Philipp, Foltyn-Dumitru, Martha, Malhotra, Ajay, Abayazeed, Aly H., Dellepiane, Francesco, Lohmann, Philipp, Pérez-García, Víctor M., Elhalawani, Hesham, Al-Rubaiey, Sanaria, Armindo, Rui Duarte, Ashraf, Kholod, Asla, Moamen M., Badawy, Mohamed, Bisschop, Jeroen, Lomer, Nima Broomand, Bukatz, Jan, Chen, Jim, Cimflova, Petra, Corr, Felix, Crawley, Alexis, Deptula, Lisa, Elakhdar, Tasneem, Shawali, Islam H., Faghani, Shahriar, Frick, Alexandra, Gulati, Vaibhav, Haider, Muhammad Ammar, Hierro, Fátima, Dahl, Rasmus Holmboe, Jacobs, Sarah Maria, Hsieh, Kuang-chun Jim, Kandemirli, Sedat G., Kersting, Katharina, Kida, Laura, Kollia, Sofia, Koukoulithras, Ioannis, Li, Xiao, Abouelatta, Ahmed, Mansour, Aya, Maria-Zamfirescu, Ruxandra-Catrinel, Marsiglia, Marcela, Mateo-Camacho, Yohana Sarahi, McArthur, Mark, McDonnell, Olivia, McHugh, Maire, Moassefi, Mana, Morsi, Samah Mostafa, Muntenu, Alexander, Nandolia, Khanak K., Naqvi, Syed Raza, Nikanpour, Yalda, Alnoury, Mostafa, Nouh, Abdullah Mohamed Aly, Pappafava, Francesca, Patel, Markand D., Petrucci, Samantha, Rawie, Eric, Raymond, Scott, Roohani, Borna, Sabouhi, Sadeq, Sanchez-Garcia, Laura M., Shaked, Zoe, Suthar, Pokhraj P., Altes, Talissa, Isufi, Edvin, Dhermesh, Yaseen, Gass, Jaime, Thacker, Jonathan, Tarabishy, Abdul Rahman, Turner, Benjamin, Vacca, Sebastiano, Vilanilam, George K., Warren, Daniel, Weiss, David, Worede, Fikadu, Yousry, Sara, Lerebo, Wondwossen, Aristizabal, Alejandro, Karargyris, Alexandros, Kassem, Hasan, Pati, Sarthak, Sheller, Micah, Bakas, Spyridon, Rudie, Jeffrey D., and Aboian, Mariam

Subjects
Quantitative Biology - Other Quantitative Biology, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space.The BraTS-METS 2023 challenge successfully curated well-annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied clinical environments and providing personalized volumetric reports to patients undergoing BM treatment.

Published
2023

3. NIMG-26. DIFFERENTIATION OF IDH-WILDTYPE GLIOBLASTOMA AND TUMEFACTIVE MULTIPLE SCLEROSIS USING PRE-OPERATIVE BRAIN MRI AND DEEP LEARNING: RIGOROUS AND UNBIASED EXPERIMENTAL DESIGN AND ANALYTICAL APPROACH

Author

Conte, Gian Marco, primary, Decker, Paul, additional, Kosel, Matthew, additional, Kollmeyer, Thomas, additional, McCarthy, Christina, additional, Sagen, Jessica, additional, Nikanpour, Yalda, additional, Fereidan-Esfahani, Mahboubeh, additional, Erickson, Bradley, additional, Jenkins, Robert, additional, Lachance, Daniel, additional, Tobin, W Oliver, additional, and Eckel-Passow, Jeanette, additional

Published
2023
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5. NIMG-72. VALIDATION OF CONDITIONAL AND SUPER-RESOLUTION GENERATIVE ADVERSARIAL NETWORKS FOR IMPUTATION OF MISSING BRAIN MRI SEQUENCES

Author

Conte, Gian Marco, primary, Tobin, W Oliver, additional, Moassefi, Mana, additional, Faghani, Shahriar, additional, Decker, Paul, additional, Kosel, Matthew, additional, Nikanpour, Yalda, additional, Zhang, Kuan, additional, Lachance, Daniel Honore, additional, Jenkins, Robert, additional, Erickson, Bradley, additional, and Eckel-Passow, Jeanette, additional

Published
2022
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9. Cyclophosphamide Versus Rituximab in Progressive Forms of Multiple Sclerosis.

Author

Etemadifar, Masoud, Ghourchian, Shadi, Mahinparvar, Nazanin, Salari, Mehri, Etemadifar, Fatemeh, Nikanpour, Yalda, Sanaei, Shahin, and Akbari, Mojtaba

Subjects
RITUXIMAB, MULTIPLE sclerosis, CLINICAL trials, CLINICAL indications, AGE distribution
Abstract

This study aimed to compare the efficacy of rituximab versus Cyclophosphamide on active secondary progressive multiple sclerosis (SPMS). The randomized clinical trial was performed from 2015 to 2017 in multiple sclerosis (MS) clinics affiliated to Isfahan MS society (IMSS). Patients were randomized to two groups, and one of them received Rituximab that was repeated every six months in case of medical indication. The other one received a monthly pulse of methylprednisolone plus cyclophosphamide (Endoxan, Baxter, UK) until two years. Expanded disabilities status scale (EDSS), clinical, and MRI findings were assessed every six months. Statistical analysis was performed using SPSS software. 39 patients in the Rituximab group and 30 in the Cyclophosphamide group with similar age and gender distribution were entered for analysis. At baseline, the mean number of attacks in the Rituximab group was significantly more than the Cyclophosphamide group (P=0.0001). After 6, 12, and 18 months of treatment, the rate of attacks was similar between groups although it increased significantly in the Rituximab group (P=0.030) after 24 months of treatment. EDSS was increased in the Rituximab group more than the other group at the end of the study. Both drugs were well-tolerated by patients. The EDSS was increased in the Rituximab group but the disability score did not worsen in the Cyclophosphamide group. Both therapies were associated with a reduction in disease attacks and improvement in radiologic findings in a two-year period of follow-up. [ABSTRACT FROM AUTHOR]

Published
2019

10. The Brain Tumor Segmentation - Metastases (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI.

Author

Moawad AW, Janas A, Baid U, Ramakrishnan D, Saluja R, Ashraf N, Jekel L, Amiruddin R, Adewole M, Albrecht J, Anazodo U, Aneja S, Anwar SM, Bergquist T, Calabrese E, Chiang V, Chung V, Conte GMM, Dako F, Eddy J, Ezhov I, Familiar A, Farahani K, Iglesias JE, Jiang Z, Johanson E, Kazerooni AF, Kofler F, Krantchev K, LaBella D, Van Leemput K, Li HB, Linguraru MG, Link KE, Liu X, Maleki N, Meier Z, Menze BH, Moy H, Osenberg K, Piraud M, Reitman Z, Shinohara RT, Tahon NH, Nada A, Velichko YS, Wang C, Wiestler B, Wiggins W, Shafique U, Willms K, Avesta A, Bousabarah K, Chakrabarty S, Gennaro N, Holler W, Kaur M, LaMontagne P, Lin M, Lost J, Marcus DS, Maresca R, Merkaj S, Nada A, Pedersen GC, von Reppert M, Sotiras A, Teytelboym O, Tillmans N, Westerhoff M, Youssef A, Godfrey D, Floyd S, Rauschecker A, Villanueva-Meyer J, Pflüger I, Cho J, Bendszus M, Brugnara G, Cramer J, Perez-Carillo GJG, Johnson DR, Kam A, Kwan BYM, Lai L, Lall NU, Memon F, Patro SN, Petrovic B, So TY, Thompson G, Wu L, Schrickel EB, Bansal A, Barkhof F, Besada C, Chu S, Druzgal J, Dusoi A, Farage L, Feltrin F, Fong A, Fung SH, Gray RI, Ikuta I, Iv M, Postma AA, Mahajan A, Joyner D, Krumpelman C, Letourneau-Guillon L, Lincoln CM, Maros ME, Miller E, Morón F, Nimchinsky EA, Ozsarlak O, Patel U, Rohatgi S, Saha A, Sayah A, Schwartz ED, Shih R, Shiroishi MS, Small JE, Tanwar M, Valerie J, Weinberg BD, White ML, Young R, Zohrabian VM, Azizova A, Brüßeler MMT, Fehringer P, Ghonim M, Ghonim M, Gkampenis A, Okar A, Pasquini L, Sharifi Y, Singh G, Sollmann N, Soumala T, Taherzadeh M, Yordanov N, Vollmuth P, Foltyn-Dumitru M, Malhotra A, Abayazeed AH, Dellepiane F, Lohmann P, Pérez-García VM, Elhalawani H, Al-Rubaiey S, Armindo RD, Ashraf K, Asla MM, Badawy M, Bisschop J, Lomer NB, Bukatz J, Chen J, Cimflova P, Corr F, Crawley A, Deptula L, Elakhdar T, Shawali IH, Faghani S, Frick A, Gulati V, Haider MA, Hierro F, Dahl RH, Jacobs SM, Hsieh KJ, Kandemirli SG, Kersting K, Kida L, Kollia S, Koukoulithras I, Li X, Abouelatta A, Mansour A, Maria-Zamfirescu RC, Marsiglia M, Mateo-Camacho YS, McArthur M, McDonnell O, McHugh M, Moassefi M, Morsi SM, Muntenu A, Nandolia KK, Naqvi SR, Nikanpour Y, Alnoury M, Nouh AMA, Pappafava F, Patel MD, Petrucci S, Rawie E, Raymond S, Roohani B, Sabouhi S, Sanchez-Garcia LM, Shaked Z, Suthar PP, Altes T, Isufi E, Dhermesh Y, Gass J, Thacker J, Tarabishy AR, Turner B, Vacca S, Vilanilam GK, Warren D, Weiss D, Willms K, Worede F, Yousry S, Lerebo W, Aristizabal A, Karargyris A, Kassem H, Pati S, Sheller M, Bakas S, Rudie JD, and Aboian M

Abstract

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well-annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied clinical environments and providing personalized volumetric reports to patients undergoing BM treatment., Competing Interests: Conflicts of Interest No conflicts of interest to disclose.

Published
2024

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