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22. Standalone AI for Breast Cancer Detection at Screening Digital Mammography and Digital Breast Tomosynthesis: A Systematic Review and Meta-Analysis.

Author

Yoon, Jung, Strand, Fredrik, Baltzer, Pascal, Conant, Emily, Gilbert, Fiona, Lehman, Constance, Mullen, Lisa, Nishikawa, Robert, Sharma, Nisha, Vejborg, Ilse, Moy, Linda, Mann, Ritse, and Morris, Elizabeth

Subjects
Female, Humans, Artificial Intelligence, Breast Neoplasms, Early Detection of Cancer, Mammography, Breast, Retrospective Studies
Abstract

Background There is considerable interest in the potential use of artificial intelligence (AI) systems in mammographic screening. However, it is essential to critically evaluate the performance of AI before it can become a modality used for independent mammographic interpretation. Purpose To evaluate the reported standalone performances of AI for interpretation of digital mammography and digital breast tomosynthesis (DBT). Materials and Methods A systematic search was conducted in PubMed, Google Scholar, Embase (Ovid), and Web of Science databases for studies published from January 2017 to June 2022. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) values were reviewed. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 and Comparative (QUADAS-2 and QUADAS-C, respectively). A random effects meta-analysis and meta-regression analysis were performed for overall studies and for different study types (reader studies vs historic cohort studies) and imaging techniques (digital mammography vs DBT). Results In total, 16 studies that include 1 108 328 examinations in 497 091 women were analyzed (six reader studies, seven historic cohort studies on digital mammography, and four studies on DBT). Pooled AUCs were significantly higher for standalone AI than radiologists in the six reader studies on digital mammography (0.87 vs 0.81, P = .002), but not for historic cohort studies (0.89 vs 0.96, P = .152). Four studies on DBT showed significantly higher AUCs in AI compared with radiologists (0.90 vs 0.79, P < .001). Higher sensitivity and lower specificity were seen for standalone AI compared with radiologists. Conclusion Standalone AI for screening digital mammography performed as well as or better than radiologists. Compared with digital mammography, there is an insufficient number of studies to assess the performance of AI systems in the interpretation of DBT screening examinations. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Scaranelo in this issue.

Published
2023

24. Multiplicative Generalized Hardy-Rogers-Type -Proximal Non-self Mappings and Best Proximity Point Approximation

Author

Sharma, Nisha, Dutta, Hemen, Gowda, G. D. Veerappa, Series Editor, Kesavan, S., Series Editor, Manchanda, Pammy, Series Editor, Nekka, Fahima, Series Editor, Aslan, Zafer, Editorial Board Member, Balachandran, K., Editorial Board Member, Brokate, Martin, Editorial Board Member, Gupta, N. K., Editorial Board Member, Khan, Akhtar A., Editorial Board Member, Lozi, René Pierre, Editorial Board Member, Nashed, M. Zuhair, Editorial Board Member, Rangarajan, Govindan, Editorial Board Member, Sreenivasan, K. R., Editorial Board Member, Zahra, Noore, Editorial Board Member, Tripathy, Binod Chandra, editor, Dutta, Hemen, editor, Paikray, Susanta Kumar, editor, and Jena, Bidu Bhusan, editor

Published
2024
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33. List of contributors

Author

Ahmed, Shakeel, primary, Ali, Akbar, additional, Ali, S. Wazed, additional, Bairagi, Satyaranjan, additional, Bharti, Kanchan, additional, Bhattacharyya, Debarati, additional, Bhosale, Rohit, additional, Can, Özlem Pelin, additional, Chopra, Hitesh, additional, Dar, Umar Ali, additional, Dedebas, Tugba, additional, Dubey, Satish, additional, Gadkari, Pooja, additional, Göksel Saraç, Meryem, additional, Gowda, Devegowda, additional, Guzel Kaya, Gulcihan, additional, Halagali, Praveen, additional, Jha, Abhishek, additional, Kapoor, Ramit, additional, Kazi, Heena, additional, Kiran Raj, G., additional, Kumar, Charchit, additional, Kumar, Manish, additional, Kumar, Shashikant, additional, Kumar, Vaneet, additional, Kumar, Vijay, additional, Maiti, Sabyasachi, additional, Mehta, Arundhati, additional, Mishra, Brahmeshwar, additional, Mittal, Pooja, additional, Mulvihill, Daniel M., additional, Nath, Jyotendra, additional, Osmani, Riyaz Ali, additional, Pokale, Rahul, additional, Rana, Vikrant Singh, additional, Rong, Liyuan, additional, Saleem, Shabnum, additional, Sehgal, Rakesh, additional, Sehgal, Rishabh, additional, Sharma, Kashma, additional, Sharma, Nisha, additional, Sharma, Vishal, additional, Shen, Mingyue, additional, Soman, Reshma, additional, Tantary, Aafaq Ahmad, additional, Tekchandani, Pawan, additional, Wani, Shahid Ud Din, additional, Xie, Jianhua, additional, and Yadav, Harsh, additional

Published
2024
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35. Development and Validation of Spectrophotometric and Spectrofluorimetric Methods for Glufosinate-Ammonium Herbicide and Carbofuran Insecticide Contamination in Environmental Samples

Author

Sharma, Nisha, Kaura, Ankita, and Chaudhary, Akhil

Subjects
Spectrophotometry -- Usage -- Methods -- Analysis, Fluorimetry -- Usage -- Methods -- Analysis, Herbicides -- Identification and classification -- Properties, Insecticides -- Identification and classification -- Properties, Chemistry
Abstract

The excessive use of glufosinateËammonium herbicide and carbofuran insecticide in agriculture leaves residues on agricultural samples and contaminates groundwater and surface water bodies through surface runoff and through leaching. To monitor pollution from the use of these pesticides, two simple, accurate and precise optical methods for their determination have been developed and validated in the commercial formulation and agricultural samples, viz., water, grains and vegetables. The first method is based on the reaction of the amino function of both pesticides with carbon disulfide and Ni(II) acetate in an aqueous acetonitrile medium and the measurement of color developed at 380 and 365 nm for glufosinateËammonium and carbofuran, respectively. Beer's law was valid within a concentration range of 0.4-8 and 0.4-9 [micro]g/mL for glufosinateËammonium and carbofuran, respectively. The second spectrofluorimetric method is based on the condensation of the primary amino group of glufosinateËammonium and carbofuran with acetylacetone and formaldehyde (Hantzsch reaction) in the presence of buffer of pH 5.5 producing a yellowËcolored product. The fluorescence intensity of the reaction products was measured at an emission wavelength of 470 nm after excitation at a wavelength of 380 nm for glufosinate-ammonium and 388 nm for carbofuran. The linearity range found was 0.13-5 and 0.14-6 [micro]g/ mL for glufosinateËammonium and carbofuran respectively. All the variables affecting the reactions, such as hydrolysis time, heating time, pH, concentration and volume of reagent were carefully studied and optimized. The high recoveries of glufosinate-ammonium and carbofuran from spiked water, grain and vegetable samples in both methods indicate good accuracy and precision of the methods., Author(s): Nisha Sharma [sup.1] , Ankita Kaura [sup.1] , Akhil Chaudhary [sup.1] Author Affiliations: (1) https://ror.org/02s5yma07, grid.412137.2, 0000 0001 0744 1069, Department of Chemistry, Himachal Pradesh University, , 171005, Shimla, [...]

Published
2023
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46. Bridging pre-surgical endocrine therapy for breast cancer during the COVID-19 pandemic: outcomes from the B-MaP-C study

Author

Dave, Rajiv V., Elsberger, Beatrix, Taxiarchi, Vicky P., Gandhi, Ashu, Kirwan, Cliona C., Kim, Baek, Camacho, Elizabeth M., Coles, Charlotte E., Copson, Ellen, Courtney, Alona, Horgan, Kieran, Fairbrother, Patricia, Holcombe, Chris, Kirkham, Jamie J., Leff, Daniel R., McIntosh, Stuart A., O’Connell, Rachel, Pardo, Ricardo, Potter, Shelley, Rattay, Tim, Sharma, Nisha, Vidya, Raghavan, and Cutress, Ramsey I.

Published
2023
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48. Four-year overall survival update from the phase III HIMALAYA study of tremelimumab plus durvalumab in unresectable hepatocellular carcinoma

Author

Azevedo, Sergio, Braghiroli, Maria Ignez, Girotto, Gustavo, Bragagnoli, Arinilda, Branco, Ricardo, Faccio, Adilson, Moretto, Andrea, Skare, Nils, Dutra, Jamille, Viola, Luciana, Vianna, Karina, Meton, Fernando, Sette, Claudia, Faulhaber, Amanda, Tam, Vincent C., Couture, Felix, Biagi, Jim, Castel, Helene, Mulder, Karen, Ko, Yoo-Joung, Zbuk, Kevin, Welch, Stephen, Beaudoin, Annie, Heurgué, Alexandra, Assenat, Eric, Archambeaud, Isabelle, Tougeron, David, Peron, Jean-Marie, Gilabert, Marine, Bronowicki, Jean-Pierre, Cattan, Stephane, Blanc, Jean-Frederic, Bouattour, Mohamed, Phelip, Jean-Marc, Boige, Valerie, Michel, Pierre, Frin, Anne-Claire, De Toni, Enrico N., Berres, Marie-Luise, Vogel, Arndt, Berg, Thomas, Ettrich, Thomas, Waldschmidt, Dirk, Wedemeyer, Hans Heinrich, Worns, Marcus-Alexander, Bitzer, Michael, Weiss, Karl-Heinz, Lau, George, Chan, Stephen L., Yau, Thomas, Tai, Yin Ping, Lee, Ann Shing, Thungappa, Satheesh Chiradoni, N, Lokesh K., Sureshchand Ostwal, Vikas, Ashok, Kattimani Kiran, Mittal, Sushant, Goyal, Hari, Srinivasan, Sankar, Biswas, Ghanashyam, Mohan, Mallavarapu, Limaye, Sewanti, Asarawala, Nirav, Rimassa, Lorenza, Falcone, Alfredo, Gianni, Luca, Gasbarrini, Antonio, Daniele, Bruno, Avallone, Antonio, Paolo Frassineti, Giovanni Luca, Roila, Fausto, Kudo, Masatoshi, Kawaoka, Tomokazu, Morimoto, Manabu, Takikawa, Yasuhiro, Kato, Naoya, Yamashita, Tatsuya, Osaki, Yukio, Motomura, Kenta, Tateishi, Ryosuke, Ohkawa, Kazuyoshi, Wada, Yoshiyuki, Onishi, Hideki, Sasahira, Naoki, Inaba, Yoshitaka, Kurosaki, Masayuki, Tsuji, Kunihiko, Takei, Yoshiyuki, Aramaki, Takeshi, Hagihara, Atsushi, Furuse, Junji, Kioka, Kiyohide, Koga, Hironori, Sasaki, Yutaka, Numata, Kazushi, Tada, Toshifumi, Kawaguchi, Yasunori, Nadano, Seijin, Vasilyev, Alexander, Breder, Valery, Lipatov, Oleg, Dvorkin, Mikhail, Zarubenkov, Oleg, Kutukova, Svetlana, Ponomarev, Roman, Shostka, Kirill, Alyasova, Anna, Topuzov, Eldar, Severtsev, Alexey, Petrov, Yuryi, Erygin, Dmitriy, Berdov, Boris, Kang, Yoon-Koo, Tak, Won-Young, Park, Joong-Won, Lim, Ho Yeong, Heo, Jeong, Kim, Jee Hyun, Kim, Tae-You, Choi, Hye Jin, Varela, María, Reig Monzon, María Elisa, Sangro, Bruno, Martin, Carlos Gómez, Ponce, Carmen Guillén, López, Carlos, Cheng, Ann-Lii, Chao, Yee, Feng, Yin-Hsun, Jeng, Long-Bin, Hung, Chao-Hung, Hou, Ming-Mo, Wang Tsang-En Wang, Jing-Houng, Yen, Chia-Jui, Sukeepaisarnjaroen, Wattana, Sunpaweravong, Patrapim, Charoentum, Chaiyut, Tanasanvimon, Suebpong, Sirachainan, Ekaphop, Ungtrakul, Teerapat, Prasongsook, Naiyarat, Maneenil, Kunlatida, Jitpewngarm, Wittawat, Ostapenko, Yurii, Skoryi, Denys, Bondarenko, Igor, Shparyk, Yaroslav, Trukhin, Dmytro, Hotko, Yevhen, Ursol, Grygorii, Kryzhanivska, Anna, Abou-Alfa, Ghassan K., Mody, Kabir, Dayyani, Farshid, Al-Rajabi, Raed, Yarchoan, Mark, Gandhi, Sunil, Crysler, Oxana, He, Aiwu Ruth, Reeves, James, Bahary, Nathan, Mahipal, Amit, Kelley, Robin Kate, Dasgupta, Anirudha, Rowe, Julie, Thota, Ramya, Beg, Muhammad, Morse, Michael, Choi, Sung-hee, Crocenzi, Todd, Somer, Bradley, Abrams, Thomas, Denlinger, Crystal, Zhang, Yue, Sharma, Nisha, Dao, Tu V., Thinh, Nguyen Tien, Tuyet Phuong, Le Thi, Sangro, B., Chan, S.L., Kelley, R.K., Lau, G., Kudo, M., Sukeepaisarnjaroen, W., Yarchoan, M., De Toni, E.N., Furuse, J., Kang, Y.K., Galle, P.R., Rimassa, L., Heurgué, A., Tam, V.C., Van Dao, T., Thungappa, S.C., Breder, V., Ostapenko, Y., Reig, M., Makowsky, M., Paskow, M.J., Gupta, C., Kurland, J.F., Negro, A., and Abou-Alfa, G.K.

Published
2024
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