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1. Evaluation of ChatGPT Family of Models for Biomedical Reasoning and Classification

Author

Chen, Shan, Li, Yingya, Lu, Sheng, Van, Hoang, Aerts, Hugo JWL, Savova, Guergana K., and Bitterman, Danielle S.

Subjects
FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computation and Language (cs.CL)
Abstract

Recent advances in large language models (LLMs) have shown impressive ability in biomedical question-answering, but have not been adequately investigated for more specific biomedical applications. This study investigates the performance of LLMs such as the ChatGPT family of models (GPT-3.5s, GPT-4) in biomedical tasks beyond question-answering. Because no patient data can be passed to the OpenAI API public interface, we evaluated model performance with over 10000 samples as proxies for two fundamental tasks in the clinical domain - classification and reasoning. The first task is classifying whether statements of clinical and policy recommendations in scientific literature constitute health advice. The second task is causal relation detection from the biomedical literature. We compared LLMs with simpler models, such as bag-of-words (BoW) with logistic regression, and fine-tuned BioBERT models. Despite the excitement around viral ChatGPT, we found that fine-tuning for two fundamental NLP tasks remained the best strategy. The simple BoW model performed on par with the most complex LLM prompting. Prompt engineering required significant investment., 28 pages, 2 tables and 4 figures. Submitting for review

Published
2023

2. Body composition and lung cancer-associated cachexia in TRACERx

Author

Al-Sawaf, Othman, Weiss, Jakob, Skrzypski, Marcin, Lam, Jie Min, Karasaki, Takahiro, Zambrana, Francisco, Kidd, Andrew C, Frankell, Alexander M, Watkins, Thomas BK, Martinez-Ruiz, Carlos, Puttick, Clare, Black, James RM, Huebner, Ariana, Al Bakir, Maise, Sokac, Mateo, Collins, Susie, Veeriah, Selvaraju, Magno, Neil, Naceur-Lombardelli, Cristina, Prymas, Paulina, Toncheva, Antonia, Ward, Sophia, Jayanth, Nick, Salgado, Roberto, Bridge, Christopher P, Christiani, David C, Mak, Raymond H, Bay, Camden, Rosenthal, Michael, Sattar, Naveed, Welsh, Paul, Liu, Ying, Perrimon, Norbert, Popuri, Karteek, Beg, Mirza Faisal, McGranahan, Nicholas, Hackshaw, Allan, Breen, Danna M, O'Rahilly, Stephen, Birkbak, Nicolai J, Aerts, Hugo JWL, Jamal-Hanjani, Mariam, Swanton, Charles, Lester, Jason F, Bajaj, Amrita, Nakas, Apostolos, Sodha-Ramdeen, Azmina, Ang, Keng, Tufail, Mohamad, Chowdhry, Mohammed Fiyaz, Scotland, Molly, Boyles, Rebecca, Rathinam, Sridhar, Wilson, Claire, Marrone, Domenic, Dulloo, Sean, Fennell, Dean A, Matharu, Gurdeep, Shaw, Jacqui A, Riley, Joan, Primrose, Lindsay, Boleti, Ekaterini, Cheyne, Heather, Khalil, Mohammed, Richardson, Shirley, Cruickshank, Tracey, Price, Gillian, Kerr, Keith M, Benafif, Sarah, Gilbert, Kayleigh, Naidu, Babu, Patel, Akshay J, Osman, Aya, Lacson, Christer, Langman, Gerald, Shackleford, Helen, Djearaman, Madava, Kadiri, Salma, Middleton, Gary, Leek, Angela, Hodgkinson, Jack Davies, Totten, Nicola, Montero, Angeles, Smith, Elaine, Fontaine, Eustace, Granato, Felice, Doran, Helen, Novasio, Juliette, Rammohan, Kendadai, Joseph, Leena, Bishop, Paul, Shah, Rajesh, Moss, Stuart, Joshi, Vijay, Crosbie, Philip, Gomes, Fabio, Brown, Kate, Carter, Mathew, Chaturvedi, Anshuman, Priest, Lynsey, Oliveira, Pedro, Lindsay, Colin R, Blackhall, Fiona H, Krebs, Matthew G, Summers, Yvonne, Clipson, Alexandra, Tugwood, Jonathan, Kerr, Alastair, Rothwell, Dominic G, Kilgour, Elaine, Dive, Caroline, Schwarz, Roland F, Kaufmann, Tom L, Wilson, Gareth A, Rosenthal, Rachel, Van Loo, Peter, Szallasi, Zoltan, Kisistok, Judit, Diossy, Miklos, Demeulemeester, Jonas, Bunkum, Abigail, Stewart, Aengus, Magness, Alastair, Rowan, Andrew, Karamani, Angeliki, Chain, Benny, Campbell, Brittany B, Castignani, Carla, Bailey, Chris, Abbosh, Christopher, Weeden, Clare E, Lee, Claudia, Richard, Corentin, Hiley, Crispin T, Moore, David A, Pearce, David R, Karagianni, Despoina, Biswas, Dhruva, Levi, Dina, Hoxha, Elena, Cadieux, Elizabeth Larose, Lim, Emilia L, Colliver, Emma, Nye, Emma, Gronroos, Eva, Galvez-Cancino, Felip, Athanasopoulou, Foteini, Gimeno-Valiente, Francisco, Kassiotis, George, Stavrou, Georgia, Mastrokalos, Gerasimos, Zhai, Haoran, Lowe, Helen L, Matos, Ignacio Garcia, Goldman, Jacki, Reading, James L, Herrero, Javier, Rane, Jayant K, Nicod, Jerome, Hartley, John A, Peggs, Karl S, Enfield, Katey SS, Selvaraju, Kayalvizhi, Thol, Kerstin, Litchfield, Kevin, Ng, Kevin W, Chen, Kezhong, Dijkstra, Krijn, Grigoriadis, Kristiana, Thakkar, Krupa, Ensell, Leah, Shah, Mansi, Duran, Marcos Vasquez, Litovchenko, Maria, Sunderland, Mariana Werner, Hill, Mark S, Dietzen, Michelle, Leung, Michelle, Escudero, Mickael, Angelova, Mihaela, Tanic, Miljana, Sivakumar, Monica, Kanu, Nnennaya, Chervova, Olga, Lucas, Olivia, Pich, Oriol, Hobson, Philip, Pawlik, Piotr, Stone, Richard Kevin, Bentham, Robert, Hynds, Robert E, Vendramin, Roberto, Saghafinia, Sadegh, Lopez, Saioa, Gamble, Samuel, Ung, Seng Kuong Anakin, Quezada, Sergio A, Vanloo, Sharon, Zaccaria, Simone, Hessey, Sonya, Boeing, Stefan, Beck, Stephan, Bola, Supreet Kaur, Denner, Tamara, Marafioti, Teresa, Mourikis, Thanos P, Spanswick, Victoria, Barbe, Vittorio, Lu, Wei-Ting, Hill, William, Liu, Wing Kin, Wu, Yin, Naito, Yutaka, Ramsden, Zoe, Veiga, Catarina, Royle, Gary, Collins-Fekete, Charles-Antoine, Fraioli, Francesco, Ashford, Paul, Clark, Tristan, Forster, Martin D, Lee, Siow Ming, Borg, Elaine, Falzon, Mary, Papadatos-Pastos, Dionysis, Wilson, James, Ahmad, Tanya, Procter, Alexander James, Ahmed, Asia, Taylor, Magali N, Nair, Arjun, Lawrence, David, Patrini, Davide, Navani, Neal, Thakrar, Ricky M, Janes, Sam M, Hoogenboom, Emilie Martinoni, Monk, Fleur, Holding, James W, Choudhary, Junaid, Bhakhri, Kunal, Scarci, Marco, Hayward, Martin, Panagiotopoulos, Nikolaos, Gorman, Pat, Khiroya, Reena, Stephens, Robert CM, Wong, Yien Ning Sophia, Bandula, Steve, Sharp, Abigail, Smith, Sean, Gower, Nicole, Dhanda, Harjot Kaur, Chan, Kitty, Pilotti, Camilla, Leslie, Rachel, Grapa, Anca, Zhang, Hanyun, AbdulJabbar, Khalid, Pan, Xiaoxi, Yuan, Yinyin, Chuter, David, MacKenzie, Mairead, Chee, Serena, Alzetani, Aiman, Cave, Judith, Scarlett, Lydia, Richards, Jennifer, Ingram, Papawadee, Austin, Silvia, Lim, Eric, De Sousa, Paulo, Jordan, Simon, Rice, Alexandra, Raubenheimer, Hilgardt, Bhayani, Harshil, Ambrose, Lyn, Devaraj, Anand, Chavan, Hema, Begum, Sofina, Buderi, Silviu, Kaniu, Daniel, Malima, Mpho, Booth, Sarah, Nicholson, Andrew G, Fernandes, Nadia, Shah, Pratibha, Proli, Chiara, Hewish, Madeleine, Danson, Sarah, Shackcloth, Michael J, Robinson, Lily, Russell, Peter, Blyth, Kevin G, Dick, Craig, Le Quesne, John, Kirk, Alan, Asif, Mo, Bilancia, Rocco, Kostoulas, Nikos, and Thomas, Mathew

Subjects
Male, Proteomics, Cachexia, Lung Neoplasms, Antigens, Neoplasm, Carcinoma, Non-Small-Cell Lung, Body Weight, Body Composition, Humans, Neoplasm Recurrence, Local, Muscle, Skeletal, Neoplasm Proteins
Abstract

Cancer-associated cachexia (CAC) is a major contributor to morbidity and mortality in individuals with non-small cell lung cancer. Key features of CAC include alterations in body composition and body weight. Here, we explore the association between body composition and body weight with survival and delineate potential biological processes and mediators that contribute to the development of CAC. Computed tomography-based body composition analysis of 651 individuals in the TRACERx (TRAcking non-small cell lung Cancer Evolution through therapy (Rx)) study suggested that individuals in the bottom 20th percentile of the distribution of skeletal muscle or adipose tissue area at the time of lung cancer diagnosis, had significantly shorter lung cancer-specific survival and overall survival. This finding was validated in 420 individuals in the independent Boston Lung Cancer Study. Individuals classified as having developed CAC according to one or more features at relapse encompassing loss of adipose or muscle tissue, or body mass index-adjusted weight loss were found to have distinct tumor genomic and transcriptomic profiles compared with individuals who did not develop such features. Primary non-small cell lung cancers from individuals who developed CAC were characterized by enrichment of inflammatory signaling and epithelial-mesenchymal transitional pathways, and differentially expressed genes upregulated in these tumors included cancer-testis antigen MAGEA6 and matrix metalloproteinases, such as ADAMTS3. In an exploratory proteomic analysis of circulating putative mediators of cachexia performed in a subset of 110 individuals from TRACERx, a significant association between circulating GDF15 and loss of body weight, skeletal muscle and adipose tissue was identified at relapse, supporting the potential therapeutic relevance of targeting GDF15 in the management of CAC. ispartof: NATURE MEDICINE vol:29 issue:4 ispartof: location:United States status: Published online

Published
2023

3. The evolution of non-small cell lung cancer metastases in TRACERx

Author

Al Bakir, Maise, Huebner, Ariana, Martinez-Ruiz, Carlos, Grigoriadis, Kristiana, Watkins, Thomas BK, Pich, Oriol, Moore, David A, Veeriah, Selvaraju, Ward, Sophia, Laycock, Joanne, Johnson, Diana, Rowan, Andrew, Razaq, Maryam, Akther, Mita, Naceur-Lombardelli, Cristina, Prymas, Paulina, Toncheva, Antonia, Hessey, Sonya, Dietzen, Michelle, Colliver, Emma, Frankell, Alexander, Bunkum, Abigail, Lim, Emilia L, Karasaki, Takahiro, Abbosh, Christopher, Hiley, Crispin T, Hill, Mark S, Cook, Daniel E, Wilson, Gareth A, Salgado, Roberto, Nye, Emma, Stone, Richard Kevin, Fennell, Dean A, Price, Gillian, Kerr, Keith M, Naidu, Babu, Middleton, Gary, Summers, Yvonne, Lindsay, Colin R, Blackhall, Fiona H, Cave, Judith, Blyth, Kevin G, Nair, Arjun, Ahmed, Asia, Taylor, Magali N, Procter, Alexander James, Falzon, Mary, Lawrence, David, Navani, Neal, Thakrar, Ricky M, Janes, Sam M, Papadatos-Pastos, Dionysis, Forster, Martin D, Lee, Siow Ming, Ahmad, Tanya, Quezada, Sergio, Peggs, Karl S, Van Loo, Peter, Dive, Caroline, Hackshaw, Allan, Birkbak, Nicolai J, Zaccaria, Simone, Jamal-Hanjani, Mariam, McGranahan, Nicholas, Swanton, Charles, Lester, Jason F, Bajaj, Amrita, Nakas, Apostolos, Sodha-Ramdeen, Azmina, Ang, Keng, Tufail, Mohamad, Chowdhry, Mohammed Fiyaz, Scotland, Molly, Boyles, Rebecca, Rathinam, Sridhar, Wilson, Claire, Marrone, Domenic, Dulloo, Sean, Matharu, Gurdeep, Shaw, Jacqui A, Riley, Joan, Primrose, Lindsay, Boleti, Ekaterini, Cheyne, Heather, Khalil, Mohammed, Richardson, Shirley, Cruickshank, Tracey, Benafif, Sarah, Gilbert, Kayleigh, Patel, Akshay J, Osman, Aya, Lacson, Christer, Langman, Gerald, Shackleford, Helen, Djearaman, Madava, Kadiri, Salma, Leek, Angela, Hodgkinson, Jack Davies, Totten, Nicola, Montero, Angeles, Smith, Elaine, Fontaine, Eustace, Granato, Felice, Doran, Helen, Novasio, Juliette, Rammohan, Kendadai, Joseph, Leena, Bishop, Paul, Shah, Rajesh, Moss, Stuart, Joshi, Vijay, Crosbie, Philip, Gomes, Fabio, Brown, Kate, Carter, Mathew, Chaturvedi, Anshuman, Priest, Lynsey, Oliveira, Pedro, Krebs, Matthew G, Clipson, Alexandra, Tugwood, Jonathan, Kerr, Alastair, Rothwell, Dominic G, Kilgour, Elaine, Aerts, Hugo JWL, Schwarz, Roland F, Kaufmann, Tom L, Rosenthal, Rachel, Szallasi, Zoltan, Kisistok, Judit, Sokac, Mateo, Diossy, Miklos, Demeulemeester, Jonas, Stewart, Aengus, Magness, Alastair, Karamani, Angeliki, Chain, Benny, Campbell, Brittany B, Castignani, Carla, Bailey, Chris, Puttick, Clare, Weeden, Clare E, Lee, Claudia, Richard, Corentin, Pearce, David R, Karagianni, Despoina, Biswas, Dhruva, Levi, Dina, Hoxha, Elena, Larose Cadieux, Elizabeth, Gronroos, Eva, Galvez-Cancino, Felip, Athanasopoulou, Foteini, Gimeno-Valiente, Francisco, Kassiotis, George, Stavrou, Georgia, Mastrokalos, Gerasimos, Zhai, Haoran, Lowe, Helen L, Matos, Ignacio, Goldman, Jacki, Reading, James L, Black, James RM, Herrero, Javier, Rane, Jayant K, Nicod, Jerome, Lam, Jie Min, Hartley, John A, Enfield, Katey SS, Selvaraju, Kayalvizhi, Thol, Kerstin, Litchfield, Kevin, Ng, Kevin W, Chen, Kezhong, Dijkstra, Krijn, Thakkar, Krupa, Ensell, Leah, Shah, Mansi, Vasquez, Marcos, Litovchenko, Maria, Werner Sunderland, Mariana, Leung, Michelle, Escudero, Mickael, Angelova, Mihaela, Tanic, Miljana, Sivakumar, Monica, Kanu, Nnennaya, Chervova, Olga, Lucas, Olivia, Al-Sawaf, Othman, Hobson, Philip, Pawlik, Piotr, Bentham, Robert, Hynds, Robert E, Vendramin, Roberto, Saghafinia, Sadegh, Lopez, Saioa, Gamble, Samuel, Ung, Seng Kuong Anakin, Vanloo, Sharon, Boeing, Stefan, Beck, Stephan, Bola, Supreet Kaur, Denner, Tamara, Marafioti, Teresa, Mourikis, Thanos P, Spanswick, Victoria, Barbe, Vittorio, Lu, Wei-Ting, Hill, William, Liu, Wing Kin, Wu, Yin, Naito, Yutaka, Ramsden, Zoe, Veiga, Catarina, Royle, Gary, Collins-Fekete, Charles-Antoine, Fraioli, Francesco, Ashford, Paul, Clark, Tristan, Borg, Elaine, Wilson, James, Patrini, Davide, Martinoni Hoogenboom, Emilie, Monk, Fleur, Holding, James W, Choudhary, Junaid, Bhakhri, Kunal, Scarci, Marco, Hayward, Martin, Panagiotopoulos, Nikolaos, Gorman, Pat, Khiroya, Reena, Stephens, Robert CM, Wong, Yien Ning Sophia, Bandula, Steve, Sharp, Abigail, Smith, Sean, Gower, Nicole, Dhanda, Harjot Kaur, Chan, Kitty, Pilotti, Camilla, Leslie, Rachel, Grapa, Anca, Zhang, Hanyun, AbdulJabbar, Khalid, Pan, Xiaoxi, Yuan, Yinyin, Chuter, David, MacKenzie, Mairead, Chee, Serena, Alzetani, Aiman, Scarlett, Lydia, Richards, Jennifer, Ingram, Papawadee, Austin, Silvia, Lim, Eric, De Sousa, Paulo, Jordan, Simon, Rice, Alexandra, Raubenheimer, Hilgardt, Bhayani, Harshil, Ambrose, Lyn, Devaraj, Anand, Chavan, Hema, Begum, Sofina, Buderi, Silviu, Kaniu, Daniel, Malima, Mpho, Booth, Sarah, Nicholson, Andrew G, Fernandes, Nadia, Shah, Pratibha, Proli, Chiara, Hewish, Madeleine, Danson, Sarah, Shackcloth, Michael J, Robinson, Lily, Russell, Peter, Dick, Craig, Le Quesne, John, Kirk, Alan, Asif, Mo, Bilancia, Rocco, Kostoulas, Nikos, and Thomas, Mathew

Subjects
Clonal Evolution, Cohort Studies, Evolution, Molecular, Lung Neoplasms, Carcinoma, Non-Small-Cell Lung, Disease Progression, Humans, Neoplasm Metastasis, Neoplasm Recurrence, Local, Clone Cells
Abstract

Metastatic disease is responsible for the majority of cancer-related deaths1. We report the longitudinal evolutionary analysis of 126 non-small cell lung cancer (NSCLC) tumours from 421 prospectively recruited patients in TRACERx who developed metastatic disease, compared with a control cohort of 144 non-metastatic tumours. In 25% of cases, metastases diverged early, before the last clonal sweep in the primary tumour, and early divergence was enriched for patients who were smokers at the time of initial diagnosis. Simulations suggested that early metastatic divergence more frequently occurred at smaller tumour diameters (less than 8 mm). Single-region primary tumour sampling resulted in 83% of late divergence cases being misclassified as early, highlighting the importance of extensive primary tumour sampling. Polyclonal dissemination, which was associated with extrathoracic disease recurrence, was found in 32% of cases. Primary lymph node disease contributed to metastatic relapse in less than 20% of cases, representing a hallmark of metastatic potential rather than a route to subsequent recurrences/disease progression. Metastasis-seeding subclones exhibited subclonal expansions within primary tumours, probably reflecting positive selection. Our findings highlight the importance of selection in metastatic clone evolution within untreated primary tumours, the distinction between monoclonal versus polyclonal seeding in dictating site of recurrence, the limitations of current radiological screening approaches for early diverging tumours and the need to develop strategies to target metastasis-seeding subclones before relapse. ispartof: NATURE vol:616 issue:7957 ispartof: location:England status: Published online

Published
2023

4. The evolution of lung cancer and impact of subclonal selection in TRACERx

Author

Frankell, Alexander M, Dietzen, Michelle, Al Bakir, Maise, Lim, Emilia L, Karasaki, Takahiro, Ward, Sophia, Veeriah, Selvaraju, Colliver, Emma, Huebner, Ariana, Bunkum, Abigail, Hill, Mark S, Grigoriadis, Kristiana, Moore, David A, Black, James RM, Liu, Wing Kin, Thol, Kerstin, Pich, Oriol, Watkins, Thomas BK, Naceur-Lombardelli, Cristina, Cook, Daniel E, Salgado, Roberto, Wilson, Gareth A, Bailey, Chris, Angelova, Mihaela, Bentham, Robert, Martinez-Ruiz, Carlos, Abbosh, Christopher, Nicholson, Andrew G, Le Quesne, John, Biswas, Dhruva, Rosenthal, Rachel, Puttick, Clare, Hessey, Sonya, Lee, Claudia, Prymas, Paulina, Toncheva, Antonia, Smith, Jon, Xing, Wei, Nicod, Jerome, Price, Gillian, Kerr, Keith M, Naidu, Babu, Middleton, Gary, Blyth, Kevin G, Fennell, Dean A, Forster, Martin D, Lee, Siow Ming, Falzon, Mary, Hewish, Madeleine, Shackcloth, Michael J, Lim, Eric, Benafif, Sarah, Russell, Peter, Boleti, Ekaterini, Krebs, Matthew G, Lester, Jason F, Papadatos-Pastos, Dionysis, Ahmad, Tanya, Thakrar, Ricky M, Lawrence, David, Navani, Neal, Janes, Sam M, Dive, Caroline, Blackhall, Fiona H, Summers, Yvonne, Cave, Judith, Marafioti, Teresa, Herrero, Javier, Quezada, Sergio A, Peggs, Karl S, Schwarz, Roland F, Van Loo, Peter, Miedema, Daniel M, Birkbak, Nicolai J, Hiley, Crispin T, Hackshaw, Allan, Zaccaria, Simone, Jamal-Hanjani, Mariam, McGranahan, Nicholas, Swanton, Charles, Bajaj, Amrita, Nakas, Apostolos, Sodha-Ramdeen, Azmina, Ang, Keng, Tufail, Mohamad, Chowdhry, Mohammed Fiyaz, Scotland, Molly, Boyles, Rebecca, Rathinam, Sridhar, Wilson, Claire, Marrone, Domenic, Dulloo, Sean, Matharu, Gurdeep, Shaw, Jacqui A, Riley, Joa, Primrose, Lindsay, Cheyne, Heather, Khalil, Mohammed, Richardson, Shirley, Cruickshank, Tracey, Gilbert, Kayleigh, Patel, Akshay J, Osman, Aya, Lacson, Christer, Langman, Gerald, Shackleford, Helen, Djearaman, Madava, Kadiri, Salma, Leek, Angela, Hodgkinson, Jack Davies, Totten, Nicola, Montero, Angeles, Smith, Elaine, Fontaine, Eustace, Granato, Felice, Doran, Helen, Novasio, Juliette, Rammohan, Kendadai, Joseph, Leena, Bishop, Paul, Shah, Rajesh, Moss, Stuart, Joshi, Vijay, Crosbie, Philip, Gomes, Fabio, Brown, Kate, Carter, Mathew, Chaturvedi, Anshuman, Priest, Lynsey, Oliveira, Pedro, Lindsay, Colin R, Clipson, Alexandra, Tugwood, Jonathan, Kerr, Alastair, Rothwell, Dominic G, Kilgour, Elaine, Aerts, Hugo JWL, Kaufmann, Tom L, Szallasi, Zoltan, Kisistok, Judit, Sokac, Mateo, Diossy, Miklos, Demeulemeester, Jonas, Stewart, Aengus, Magness, Alastair, Rowan, Andrew, Karamani, Angeliki, Chain, Benny, Campbell, Brittany B, Castignani, Carla, Weeden, Clare E, Richard, Corentin, Pearce, David R, Karagianni, Despoina, Levi, Dina, Hoxha, Elena, Larose Cadieux, Elizabeth, Nye, Emma, Gronroos, Eva, Galvez-Cancino, Felip, Athanasopoulou, Foteini, Gimeno-Valiente, Francisco, Kassiotis, George, Stavrou, Georgia, Mastrokalos, Gerasimos, Zhai, Haoran L, Lowe, Helen L, Matos, Ignacio, Goldman, Jacki, Reading, James L, Rane, Jayant K, Lam, Jie Min, Hartley, John A, Enfield, Katey SS, Selvaraju, Kayalvizhi, Litchfield, Kevin, Ng, Kevin W, Chen, Kezhong, Dijkstra, Krijn, Thakkar, Krupa, Ensell, Leah, Shah, Mansi, Vasquez, Marcos, Litovchenko, Maria, Werner Sunderland, Mariana, Leung, Michelle, Escudero, Mickael, Tanic, Miljana, Sivakumar, Monica, Kanu, Nnennaya, Chervova, Olga, Lucas, Olivia, Al-Sawaf, Othman, Hobson, Philip, Pawlik, Piotr, Stone, Richard Kevin, Hynds, Robert E, Vendramin, Roberto, Saghafinia, Sadegh, Lopez, Saioa, Gamble, Samuel, Ung, Seng Kuong Anakin, Vanloo, Sharon, Boeing, Stefan, Beck, Stephan, Bola, Supreet Kaur, Denner, Tamara, Mourikis, Thanos P, Spanswick, Victoria, Barbe, Vittorio, Lu, Wei-Ting, Hill, William, Wu, Yin, Naito, Yutaka, Ramsden, Zoe, Veiga, Catarina, Royle, Gary, Collins-Fekete, Charles-Antoine, Fraioli, Francesco, Ashford, Paul, Clark, Tristan, Borg, Elaine, Wilson, James, Procter, Alexander James, Ahmed, Asia, Taylor, Magali N, Nair, Arjun, Patrini, Davide, Martinoni Hoogenboom, Emilie, Monk, Fleur, Holding, James W, Choudhary, Junaid, Bhakhri, Kunal, Scarci, Marco, Hayward, Martin, Panagiotopoulos, Nikolaos, Gorman, Pat, Khiroya, Reena, Stephens, Robert CM, Wong, Yien Ning Sophia, Bandula, Steve, Sharp, Abigail, Smith, Sean, Gower, Nicole, Dhanda, Harjot Kaur, Chan, Kitty, Pilotti, Camilla, Leslie, Rachel, Grapa, Anca, Zhang, Hanyun, AbdulJabbar, Khalid, Pan, Xiaoxi, Yuan, Yinyin, Chuter, David, MacKenzie, Mairead, Chee, Serena, Alzetani, Aiman, Scarlett, Lydia, Richards, Jennifer, Ingram, Papawadee, Austin, Silvia, De Sousa, Paulo, Jordan, Simon, Rice, Alexandra, Raubenheimer, Hilgardt, Bhayani, Harshil, Ambrose, Lyn, Devaraj, Anand, Chavan, Hema, Begum, Sofina, Buderi, Silviu, Kaniu, Daniel, Malima, Mpho, Booth, Sarah, Fernandes, Nadia, Shah, Pratibha, Proli, Chiara, Danson, Sarah, Robinson, Lily, Dick, Craig, Kirk, Alan, Asif, Mo, Bilancia, Rocco, Kostoulas, Nikos, and Thomas, Mathew

Subjects
Lung Neoplasms, Treatment Outcome, DNA Copy Number Variations, Mutagenesis, Carcinoma, Non-Small-Cell Lung, Mutation, Smoking, Humans, Adenocarcinoma of Lung, Neoplasm Recurrence, Local, Phylogeny
Abstract

Lung cancer is the leading cause of cancer-associated mortality worldwide1. Here we analysed 1,644 tumour regions sampled at surgery or during follow-up from the first 421 patients with non-small cell lung cancer prospectively enrolled into the TRACERx study. This project aims to decipher lung cancer evolution and address the primary study endpoint: determining the relationship between intratumour heterogeneity and clinical outcome. In lung adenocarcinoma, mutations in 22 out of 40 common cancer genes were under significant subclonal selection, including classical tumour initiators such as TP53 and KRAS. We defined evolutionary dependencies between drivers, mutational processes and whole genome doubling (WGD) events. Despite patients having a history of smoking, 8% of lung adenocarcinomas lacked evidence of tobacco-induced mutagenesis. These tumours also had similar detection rates for EGFR mutations and for RET, ROS1, ALK and MET oncogenic isoforms compared with tumours in never-smokers, which suggests that they have a similar aetiology and pathogenesis. Large subclonal expansions were associated with positive subclonal selection. Patients with tumours harbouring recent subclonal expansions, on the terminus of a phylogenetic branch, had significantly shorter disease-free survival. Subclonal WGD was detected in 19% of tumours, and 10% of tumours harboured multiple subclonal WGDs in parallel. Subclonal, but not truncal, WGD was associated with shorter disease-free survival. Copy number heterogeneity was associated with extrathoracic relapse within 1 year after surgery. These data demonstrate the importance of clonal expansion, WGD and copy number instability in determining the timing and patterns of relapse in non-small cell lung cancer and provide a comprehensive clinical cancer evolutionary data resource. ispartof: NATURE vol:616 issue:7957 ispartof: location:England status: Published online

Published
2023

5. Evolutionary characterization of lung adenocarcinoma morphology in TRACERx

Author

Karasaki, Takahiro, Moore, David A, Veeriah, Selvaraju, Naceur-Lombardelli, Cristina, Toncheva, Antonia, Magno, Neil, Ward, Sophia, Al Bakir, Maise, Watkins, Thomas BK, Grigoriadis, Kristiana, Huebner, Ariana, Hill, Mark S, Frankell, Alexander M, Abbosh, Christopher, Puttick, Clare, Zhai, Haoran, Gimeno-Valiente, Francisco, Saghafinia, Sadegh, Kanu, Nnennaya, Dietzen, Michelle, Pich, Oriol, Lim, Emilia L, Martinez-Ruiz, Carlos, Black, James RM, Biswas, Dhruva, Campbell, Brittany B, Lee, Claudia, Colliver, Emma, Enfield, Katey SS, Hessey, Sonya, Hiley, Crispin T, Zaccaria, Simone, Litchfield, Kevin, Birkbak, Nicolai J, Cadieux, Elizabeth Larose, Demeulemeester, Jonas, Van Loo, Peter, Adusumilli, Prasad R, Tan, Kay See, Cheema, Waseem, Sanchez-Vega, Francisco, Jones, David R, Rekhtman, Natasha, Travis, William D, Hackshaw, Allan, Marafioti, Teresa, Salgado, Roberto, Le Quesne, John, Nicholson, Andrew G, McGranahan, Nicholas, Swanton, Charles, Jamal-Hanjani, Mariam, Lester, Jason F, Bajaj, Amrita, Nakas, Apostolos, Sodha-Ramdeen, Azmina, Ang, Keng, Tufail, Mohamad, Chowdhry, Mohammed Fiyaz, Scotland, Molly, Boyles, Rebecca, Rathinam, Sridhar, Wilson, Claire, Marrone, Domenic, Dulloo, Sean, Fennell, Dean A, Matharu, Gurdeep, Shaw, Jacqui A, Riley, Joan, Primrose, Lindsay, Boleti, Ekaterini, Cheyne, Heather, Khalil, Mohammed, Richardson, Shirley, Cruickshank, Tracey, Price, Gillian, Kerr, Keith M, Benafif, Sarah, Gilbert, Kayleigh, Naidu, Babu, Patel, Akshay J, Osman, Aya, Lacson, Christer, Langman, Gerald, Shackleford, Helen, Djearaman, Madava, Kadiri, Salma, Middleton, Gary, Leek, Angela, Hodgkinson, Jack Davies, Totten, Nicola, Montero, Angeles, Smith, Elaine, Fontaine, Eustace, Granato, Felice, Doran, Helen, Novasio, Juliette, Rammohan, Kendadai, Joseph, Leena, Bishop, Paul, Shah, Rajesh, Moss, Stuart, Joshi, Vijay, Crosbie, Philip, Gomes, Fabio, Brown, Kate, Carter, Mathew, Chaturvedi, Anshuman, Priest, Lynsey, Oliveira, Pedro, Lindsay, Colin R, Blackhall, Fiona H, Krebs, Matthew G, Summers, Yvonne, Clipson, Alexandra, Tugwood, Jonathan, Kerr, Alastair, Rothwell, Dominic G, Kilgour, Elaine, Dive, Caroline, Aerts, Hugo JWL, Schwarz, Roland F, Kaufmann, Tom L, Wilson, Gareth A, Rosenthal, Rachel, Szallasi, Zoltan, Kisistok, Judit, Sokac, Mateo, Diossy, Miklos, Bunkum, Abigail, Stewart, Aengus, Magness, Alastair, Rowan, Andrew, Karamani, Angeliki, Chain, Benny, Castignani, Carla, Bailey, Chris, Weeden, Clare E, Richard, Corentin, Pearce, David R, Karagianni, Despoina, Levi, Dina, Hoxha, Elena, Nye, Emma, Gronroos, Eva, Galvez-Cancino, Felip, Athanasopoulou, Foteini, Kassiotis, George, Stavrou, Georgia, Mastrokalos, Gerasimos, Lowe, Helen L, Matos, Ignacio Garcia, Goldman, Jacki, Reading, James L, Herrero, Javier, Rane, Jayant K, Nicod, Jerome, Lam, Jie Min, Hartley, John A, Peggs, Karl S, Selvaraju, Kayalvizhi, Thol, Kerstin, Ng, Kevin W, Chen, Kezhong, Dijkstra, Krijn, Thakkar, Krupa, Ensell, Leah, Shah, Mansi, Duran, Marcos Vasquez, Litovchenko, Maria, Sunderland, Mariana Werner, Leung, Michelle, Escudero, Mickael, Angelova, Mihaela, Tanic, Miljana, Sivakumar, Monica, Chervova, Olga, Lucas, Olivia, Al-Sawaf, Othman, Prymas, Paulina, Hobson, Philip, Pawlik, Piotr, Stone, Richard Kevin, Bentham, Robert, Hynds, Robert E, Vendramin, Roberto, Lopez, Saioa, Gamble, Samuel, Ung, Seng Kuong Anakin, Quezada, Sergio A, Vanloo, Sharon, Boeing, Stefan, Beck, Stephan, Bola, Supreet Kaur, Denner, Tamara, Mourikis, Thanos P, Spanswick, Victoria, Barbe, Vittorio, Lu, Wei-Ting, Hill, William, Liu, Wing Kin, Wu, Yin, Naito, Yutaka, Ramsden, Zoe, Veiga, Catarina, Royle, Gary, Collins-Fekete, Charles-Antoine, Fraioli, Francesco, Ashford, Paul, Clark, Tristan, Forster, Martin D, Lee, Siow Ming, Borg, Elaine, Falzon, Mary, Papadatos-Pastos, Dionysis, Wilson, James, Ahmad, Tanya, Procter, Alexander James, Ahmed, Asia, Taylor, Magali N, Nair, Arjun, Lawrence, David, Patrini, Davide, Navani, Neal, Thakrar, Ricky M, Janes, Sam M, Hoogenboom, Emilie Martinoni, Monk, Fleur, Holding, James W, Choudhary, Junaid, Bhakhri, Kunal, Scarci, Marco, Hayward, Martin, Panagiotopoulos, Nikolaos, Gorman, Pat, Khiroya, Reena, Stephens, Robert CM, Wong, Yien Ning Sophia, Bandula, Steve, Sharp, Abigail, Smith, Sean, Gower, Nicole, Dhanda, Harjot Kaur, Chan, Kitty, Pilotti, Camilla, Leslie, Rachel, Grapa, Anca, Zhang, Hanyun, AbdulJabbar, Khalid, Pan, Xiaoxi, Yuan, Yinyin, Chuter, David, MacKenzie, Mairead, Chee, Serena, Alzetani, Aiman, Cave, Judith, Scarlett, Lydia, Richards, Jennifer, Ingram, Papawadee, Austin, Silvia, Lim, Eric, De Sousa, Paulo, Jordan, Simon, Rice, Alexandra, Raubenheimer, Hilgardt, Bhayani, Harshil, Ambrose, Lyn, Devaraj, Anand, Chavan, Hema, Begum, Sofina, Buderi, Silviu, Kaniu, Daniel, Malima, Mpho, Booth, Sarah, Fernandes, Nadia, Shah, Pratibha, Proli, Chiara, Hewish, Madeleine, Danson, Sarah, Shackcloth, Michael J, Robinson, Lily, Russell, Peter, Blyth, Kevin G, Dick, Craig, Kirk, Alan, Asif, Mo, Bilancia, Rocco, Kostoulas, Nikos, and Thomas, Mathew

Subjects
TRACERx Consortium
Abstract

Lung adenocarcinomas (LUADs) display a broad histological spectrum from low-grade lepidic tumors through to mid-grade acinar and papillary and high-grade solid, cribriform and micropapillary tumors. How morphology reflects tumor evolution and disease progression is poorly understood. Whole-exome sequencing data generated from 805 primary tumor regions and 121 paired metastatic samples across 248 LUADs from the TRACERx 421 cohort, together with RNA-sequencing data from 463 primary tumor regions, were integrated with detailed whole-tumor and regional histopathological analysis. Tumors with predominantly high-grade patterns showed increased chromosomal complexity, with higher burden of loss of heterozygosity and subclonal somatic copy number alterations. Individual regions in predominantly high-grade pattern tumors exhibited higher proliferation and lower clonal diversity, potentially reflecting large recent subclonal expansions. Co-occurrence of truncal loss of chromosomes 3p and 3q was enriched in predominantly low-/mid-grade tumors, while purely undifferentiated solid-pattern tumors had a higher frequency of truncal arm or focal 3q gains and SMARCA4 gene alterations compared with mixed-pattern tumors with a solid component, suggesting distinct evolutionary trajectories. Clonal evolution analysis revealed that tumors tend to evolve toward higher-grade patterns. The presence of micropapillary pattern and 'tumor spread through air spaces' were associated with intrathoracic recurrence, in contrast to the presence of solid/cribriform patterns, necrosis and preoperative circulating tumor DNA detection, which were associated with extra-thoracic recurrence. These data provide insights into the relationship between LUAD morphology, the underlying evolutionary genomic landscape, and clinical and anatomical relapse risk. ispartof: NATURE MEDICINE vol:29 issue:4 ispartof: location:United States status: Published online

Published
2023

6. Prediction of Distant Metastases After Stereotactic Body Radiation Therapy for Early Stage NSCLC Development and External Validation of a Multi-Institutional Model: development and external validation of a multi-institutional model

Author

Gao, Sarah, Jin, Lan, Meadows, Hugh W, Shafman, Timothy, Gross, Cary P, Yu, James B, Aerts, Hugo Jwl, Miccio, Joseph A, Stahl, John M, Mak, Raymond H, Decker, Roy H, Kann, Benjamin H, Beeldvorming, MUMC+: DA BV Research (9), RS: Carim - B06 Imaging, and RS: GROW - R2 - Basic and Translational Cancer Biology

Abstract

OBJECTIVES: Distant metastases (DM) are the primary driver of mortality for patients with early stage non-small cell lung cancer (NSCLC) receiving stereotactic body radiation therapy (SBRT), yet patient-level risk is difficult to predict. We developed and validated a model to predict individualized risk of DM in this population. METHODS: We utilized a multi-institutional database of 1,280 patients with cT1-3N0M0 NSCLC treated with SBRT from 2006-2015 for model development and internal validation. A Fine and Gray (FG) regression model was built to predict 1-year DM risk and compared to a random survival forests (RSF) model. The higher performing model was evaluated on an external dataset of 130 patients from a separate institution. Discriminatory performance was evaluated using the time-dependent area under the curve (AUC). Calibration was assessed graphically and with Brier scores. RESULTS: The FG model yielded an AUC of 0.71 (95% CI: 0.57-0.86) compared to the RSF's AUC of 0.69 [95% CI: 0.63-0.85] in the internal test set and was selected for further testing. On external validation, the FG model yielded an AUC 0.70 (95% CI: 0.57-0.83) with good calibration (Brier score 0.08). The model identified a high-risk patient subgroup with greater 1-year DM rates in the internal test (20.0% (3/15) v 2.9% (7/241), P=0.001) and external validation 21.4% (3/15) v 7.8% (9/116), P= 0.095). A model nomogram and online application was made available. CONCLUSIONS: We developed and externally validated a practical model that predicts DM risk in NSCLC patients receiving SBRT that may help select patients for systemic therapy.

Published
2023

7. Natural language processing to automatically extract the presence and severity of esophagitis in notes of patients undergoing radiotherapy

Author

Chen, Shan, Guevara, Marco, Ramirez, Nicolas, Murray, Arpi, Warner, Jeremy L., Aerts, Hugo JWL, Miller, Timothy A., Savova, Guergana K., Mak, Raymond H., and Bitterman, Danielle S.

Subjects
FOS: Computer and information sciences, Computer Science - Computation and Language, Computation and Language (cs.CL)
Abstract

Radiotherapy (RT) toxicities can impair survival and quality-of-life, yet remain under-studied. Real-world evidence holds potential to improve our understanding of toxicities, but toxicity information is often only in clinical notes. We developed natural language processing (NLP) models to identify the presence and severity of esophagitis from notes of patients treated with thoracic RT. We fine-tuned statistical and pre-trained BERT-based models for three esophagitis classification tasks: Task 1) presence of esophagitis, Task 2) severe esophagitis or not, and Task 3) no esophagitis vs. grade 1 vs. grade 2-3. Transferability was tested on 345 notes from patients with esophageal cancer undergoing RT. Fine-tuning PubmedBERT yielded the best performance. The best macro-F1 was 0.92, 0.82, and 0.74 for Task 1, 2, and 3, respectively. Selecting the most informative note sections during fine-tuning improved macro-F1 by over 2% for all tasks. Silver-labeled data improved the macro-F1 by over 3% across all tasks. For the esophageal cancer notes, the best macro-F1 was 0.73, 0.74, and 0.65 for Task 1, 2, and 3, respectively, without additional fine-tuning. To our knowledge, this is the first effort to automatically extract esophagitis toxicity severity according to CTCAE guidelines from clinic notes. The promising performance provides proof-of-concept for NLP-based automated detailed toxicity monitoring in expanded domains., Comment: 17 pages, 6 tables, 1figure, submiting to JCO-CCI for review

Published
2023
Full Text
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8. MEDBERT.de: A Comprehensive German BERT Model for the Medical Domain

Author

Bressem, Keno K., Papaioannou, Jens-Michalis, Grundmann, Paul, Borchert, Florian, Adams, Lisa C., Liu, Leonhard, Busch, Felix, Xu, Lina, Loyen, Jan P., Niehues, Stefan M., Augustin, Moritz, Grosser, Lennart, Makowski, Marcus R., Aerts, Hugo JWL., and Löser, Alexander

Subjects
FOS: Computer and information sciences, Computer Science - Computation and Language, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computation and Language (cs.CL)
Abstract

This paper presents medBERTde, a pre-trained German BERT model specifically designed for the German medical domain. The model has been trained on a large corpus of 4.7 Million German medical documents and has been shown to achieve new state-of-the-art performance on eight different medical benchmarks covering a wide range of disciplines and medical document types. In addition to evaluating the overall performance of the model, this paper also conducts a more in-depth analysis of its capabilities. We investigate the impact of data deduplication on the model's performance, as well as the potential benefits of using more efficient tokenization methods. Our results indicate that domain-specific models such as medBERTde are particularly useful for longer texts, and that deduplication of training data does not necessarily lead to improved performance. Furthermore, we found that efficient tokenization plays only a minor role in improving model performance, and attribute most of the improved performance to the large amount of training data. To encourage further research, the pre-trained model weights and new benchmarks based on radiological data are made publicly available for use by the scientific community., Comment: Keno K. Bressem and Jens-Michalis Papaioannou and Paul Grundmann contributed equally

Published
2023
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9. Enrichment of the NLST and NSCLC-Radiomics computed tomography collections with AI-derived annotations

Author

Krishnaswamy, Deepa, Bontempi, Dennis, Thiriveedhi, Vamsi, Punzo, Davide, Clunie, David, Bridge, Christopher P, Aerts, Hugo JWL, Kikinis, Ron, and Fedorov, Andrey

Subjects
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition
Abstract

Public imaging datasets are critical for the development and evaluation of automated tools in cancer imaging. Unfortunately, many do not include annotations or image-derived features, complicating their downstream analysis. Artificial intelligence-based annotation tools have been shown to achieve acceptable performance and thus can be used to automatically annotate large datasets. As part of the effort to enrich public data available within NCI Imaging Data Commons (IDC), here we introduce AI-generated annotations for two collections of computed tomography images of the chest, NSCLC-Radiomics, and the National Lung Screening Trial. Using publicly available AI algorithms we derived volumetric annotations of thoracic organs at risk, their corresponding radiomics features, and slice-level annotations of anatomical landmarks and regions. The resulting annotations are publicly available within IDC, where the DICOM format is used to harmonize the data and achieve FAIR principles. The annotations are accompanied by cloud-enabled notebooks demonstrating their use. This study reinforces the need for large, publicly accessible curated datasets and demonstrates how AI can be used to aid in cancer imaging.

Published
2023
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10. ModelHub.AI: Dissemination Platform for Deep Learning Models

Author

Hosny, Ahmed, Schwier, Michael, Berger, Christoph, Örnek, Evin P, Turan, Mehmet, Tran, Phi V, Weninger, Leon, Isensee, Fabian, Maier-Hein, Klaus H, McKinley, Richard, Lu, Michael T, Hoffmann, Udo, Menze, Bjoern, Bakas, Spyridon, Fedorov, Andriy, and Aerts, Hugo JWL

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Image and Video Processing (eess.IV), FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Image and Video Processing, Machine Learning (cs.LG)
Abstract

Recent advances in artificial intelligence research have led to a profusion of studies that apply deep learning to problems in image analysis and natural language processing among others. Additionally, the availability of open-source computational frameworks has lowered the barriers to implementing state-of-the-art methods across multiple domains. Albeit leading to major performance breakthroughs in some tasks, effective dissemination of deep learning algorithms remains challenging, inhibiting reproducibility and benchmarking studies, impeding further validation, and ultimately hindering their effectiveness in the cumulative scientific progress. In developing a platform for sharing research outputs, we present ModelHub.AI (www.modelhub.ai), a community-driven container-based software engine and platform for the structured dissemination of deep learning models. For contributors, the engine controls data flow throughout the inference cycle, while the contributor-facing standard template exposes model-specific functions including inference, as well as pre- and post-processing. Python and RESTful Application programming interfaces (APIs) enable users to interact with models hosted on ModelHub.AI and allows both researchers and developers to utilize models out-of-the-box. ModelHub.AI is domain-, data-, and framework-agnostic, catering to different workflows and contributors' preferences.

Published
2019
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11. Imaging biomarker roadmap for cancer studies

Author

O'Connor, James PB, Aboagye, Eric O, Adams, Judith E, Aerts, Hugo JWL, Barrington, Sally F, Beer, Ambros J, Boellaard, Ronald, Bohndiek, Sarah E, Brady, Michael, Brown, Gina, Buckley, David L, Chenevert, Thomas L, Clarke, Laurence P, Collette, Sandra, Cook, Gary J, DeSouza, Nandita M, Dickson, John C, Dive, Caroline, Evelhoch, Jeffrey L, Faivre-Finn, Corinne, Gallagher, Ferdia A, Gilbert, Fiona J, Gillies, Robert J, Goh, Vicky, Griffiths, John R, Groves, Ashley M, Halligan, Steve, Harris, Adrian L, Hawkes, David J, Hoekstra, Otto S, Huang, Erich P, Hutton, Brian F, Jackson, Edward F, Jayson, Gordon C, Jones, Andrew, Koh, Dow-Mu, Lacombe, Denis, Lambin, Philippe, Lassau, Nathalie, Leach, Martin O, Lee, Ting-Yim, Leen, Edward L, Lewis, Jason S, Liu, Yan, Lythgoe, Mark F, Manoharan, Prakash, Maxwell, Ross J, Miles, Kenneth A, Morgan, Bruno, Morris, Steve, Ng, Tony, Padhani, Anwar R, Parker, Geoff JM, Partridge, Mike, Pathak, Arvind P, Peet, Andrew C, Punwani, Shonit, Reynolds, Andrew R, Robinson, Simon P, Shankar, Lalitha K, Sharma, Ricky A, Soloviev, Dmitry, Stroobants, Sigrid, Sullivan, Daniel C, Taylor, Stuart A, Tofts, Paul S, Tozer, Gillian M, Van Herk, Marcel, Walker-Samuel, Simon, Wason, James, Williams, Kaye J, Workman, Paul, Yankeelov, Thomas E, Brindle, Kevin M, McShane, Lisa M, Jackson, Alan, and Waterton, John C

Subjects
Cost-Benefit Analysis, Clinical Decision-Making, Reproducibility of Results, Organotechnetium Compounds, Prognosis, 3. Good health, Folic Acid, Fluorodeoxyglucose F18, Research Design, Neoplasms, Positron-Emission Tomography, Biomarkers, Tumor, Humans, Radiopharmaceuticals, Selection Bias
Abstract

Imaging biomarkers (IBs) are integral to the routine management of patients with cancer. IBs used daily in oncology include clinical TNM stage, objective response and left ventricular ejection fraction. Other CT, MRI, PET and ultrasonography biomarkers are used extensively in cancer research and drug development. New IBs need to be established either as useful tools for testing research hypotheses in clinical trials and research studies, or as clinical decision-making tools for use in healthcare, by crossing 'translational gaps' through validation and qualification. Important differences exist between IBs and biospecimen-derived biomarkers and, therefore, the development of IBs requires a tailored 'roadmap'. Recognizing this need, Cancer Research UK (CRUK) and the European Organisation for Research and Treatment of Cancer (EORTC) assembled experts to review, debate and summarize the challenges of IB validation and qualification. This consensus group has produced 14 key recommendations for accelerating the clinical translation of IBs, which highlight the role of parallel (rather than sequential) tracks of technical (assay) validation, biological/clinical validation and assessment of cost-effectiveness; the need for IB standardization and accreditation systems; the need to continually revisit IB precision; an alternative framework for biological/clinical validation of IBs; and the essential requirements for multicentre studies to qualify IBs for clinical use.

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