Pub Date : 2024-12-12DOI: 10.1016/s1470-2045(24)00661-2
Icro Meattini, Maria Carmen De Santis, Luca Visani, Marta Scorsetti, Alessandra Fozza, Bruno Meduri, Fiorenza De Rose, Elisabetta Bonzano, Agnese Prisco, Valeria Masiello, Eliana La Rocca, Ruggero Spoto, Carlotta Becherini, Gladys Blandino, Luca Moscetti, Riccardo Ray Colciago, Riccardo A Audisio, Etienne Brain, Saverio Caini, Marije Hamaker, Jure Verbancic
<h3>Background</h3>Optimal therapy following breast-conserving surgery in older adults with low-risk, early-stage breast cancer remains uncertain. The EUROPA trial aims to compare the effects of radiotherapy and endocrine therapy as single-modality treatments on health-related quality of life (HRQOL) and ipsilateral breast tumour recurrence (IBTR) outcomes in this population.<h3>Methods</h3>This non-inferiority, phase 3, randomised study was conducted at 18 academic hospitals across Italy (17 centres) and Slovenia (one centre). Eligible patients were women aged 70 years or older with histologically confirmed, stage I, luminal A-like breast cancer, who had undergone breast-conserving surgery and had an Eastern Cooperative Oncology Group performance status of 0 or 1. Patients were randomly assigned (1:1) to receive single-modality endocrine therapy or radiotherapy. Endocrine therapy consisted of daily oral aromatase inhibitors or tamoxifen, for a total planned duration of 5–10 years as per clinical discretion, while radiotherapy was administered as either whole breast or partial breast irradiation, delivered in 5–15 fractions. Randomisation was stratified by health status according to the Geriatric 8 (G8) screening tool and by age, with allocation concealed and no blinding. The co-primary endpoints were the change in HRQOL, assessed by the global health status (GHS) scale of the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire 30-item core module at 24 months, and 5-year IBTR rates (not reported here). This preplanned interim analysis was performed once at least 152 patients completed the 24-month GHS HRQOL assessment. The safety population comprised patients who received the study intervention at least once after randomisation. The study is registered with <span><span>ClinicalTrials.gov</span><svg aria-label="Opens in new window" focusable="false" height="20" viewbox="0 0 8 8"><path d="M1.12949 2.1072V1H7V6.85795H5.89111V2.90281L0.784057 8L0 7.21635L5.11902 2.1072H1.12949Z"></path></svg></span>, <span><span>NCT04134598</span><svg aria-label="Opens in new window" focusable="false" height="20" viewbox="0 0 8 8"><path d="M1.12949 2.1072V1H7V6.85795H5.89111V2.90281L0.784057 8L0 7.21635L5.11902 2.1072H1.12949Z"></path></svg></span>, and is ongoing and actively recruiting.<h3>Findings</h3>Between March 4, 2021, and June 14, 2024, 731 women were randomly assigned to receive radiotherapy (n=365) or endocrine therapy (n=366). This analysis included 104 patients in the radiotherapy group and 103 in the endocrine therapy group, with a median follow-up of 23·9 months (IQR 22·9–24·2). Patients were predominantly White (204 [99%] of 207) and the median age was 75·0 years (IQR 73·0–80·0) in the radiotherapy group and 74·0 years (72·0–80·0) in the endocrine therapy group. 86 patients in the radiotherapy group and 75 in the endocrine therapy group completed the 24-month HRQOL assessment. The mean baseline GHS score was 71·9
{"title":"Single-modality endocrine therapy versus radiotherapy after breast-conserving surgery in women aged 70 years and older with luminal A-like early breast cancer (EUROPA): a preplanned interim analysis of a phase 3, non-inferiority, randomised trial","authors":"Icro Meattini, Maria Carmen De Santis, Luca Visani, Marta Scorsetti, Alessandra Fozza, Bruno Meduri, Fiorenza De Rose, Elisabetta Bonzano, Agnese Prisco, Valeria Masiello, Eliana La Rocca, Ruggero Spoto, Carlotta Becherini, Gladys Blandino, Luca Moscetti, Riccardo Ray Colciago, Riccardo A Audisio, Etienne Brain, Saverio Caini, Marije Hamaker, Jure Verbancic","doi":"10.1016/s1470-2045(24)00661-2","DOIUrl":"https://doi.org/10.1016/s1470-2045(24)00661-2","url":null,"abstract":"<h3>Background</h3>Optimal therapy following breast-conserving surgery in older adults with low-risk, early-stage breast cancer remains uncertain. The EUROPA trial aims to compare the effects of radiotherapy and endocrine therapy as single-modality treatments on health-related quality of life (HRQOL) and ipsilateral breast tumour recurrence (IBTR) outcomes in this population.<h3>Methods</h3>This non-inferiority, phase 3, randomised study was conducted at 18 academic hospitals across Italy (17 centres) and Slovenia (one centre). Eligible patients were women aged 70 years or older with histologically confirmed, stage I, luminal A-like breast cancer, who had undergone breast-conserving surgery and had an Eastern Cooperative Oncology Group performance status of 0 or 1. Patients were randomly assigned (1:1) to receive single-modality endocrine therapy or radiotherapy. Endocrine therapy consisted of daily oral aromatase inhibitors or tamoxifen, for a total planned duration of 5–10 years as per clinical discretion, while radiotherapy was administered as either whole breast or partial breast irradiation, delivered in 5–15 fractions. Randomisation was stratified by health status according to the Geriatric 8 (G8) screening tool and by age, with allocation concealed and no blinding. The co-primary endpoints were the change in HRQOL, assessed by the global health status (GHS) scale of the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire 30-item core module at 24 months, and 5-year IBTR rates (not reported here). This preplanned interim analysis was performed once at least 152 patients completed the 24-month GHS HRQOL assessment. The safety population comprised patients who received the study intervention at least once after randomisation. The study is registered with <span><span>ClinicalTrials.gov</span><svg aria-label=\"Opens in new window\" focusable=\"false\" height=\"20\" viewbox=\"0 0 8 8\"><path d=\"M1.12949 2.1072V1H7V6.85795H5.89111V2.90281L0.784057 8L0 7.21635L5.11902 2.1072H1.12949Z\"></path></svg></span>, <span><span>NCT04134598</span><svg aria-label=\"Opens in new window\" focusable=\"false\" height=\"20\" viewbox=\"0 0 8 8\"><path d=\"M1.12949 2.1072V1H7V6.85795H5.89111V2.90281L0.784057 8L0 7.21635L5.11902 2.1072H1.12949Z\"></path></svg></span>, and is ongoing and actively recruiting.<h3>Findings</h3>Between March 4, 2021, and June 14, 2024, 731 women were randomly assigned to receive radiotherapy (n=365) or endocrine therapy (n=366). This analysis included 104 patients in the radiotherapy group and 103 in the endocrine therapy group, with a median follow-up of 23·9 months (IQR 22·9–24·2). Patients were predominantly White (204 [99%] of 207) and the median age was 75·0 years (IQR 73·0–80·0) in the radiotherapy group and 74·0 years (72·0–80·0) in the endocrine therapy group. 86 patients in the radiotherapy group and 75 in the endocrine therapy group completed the 24-month HRQOL assessment. The mean baseline GHS score was 71·9 ","PeriodicalId":22865,"journal":{"name":"The Lancet Oncology","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142809625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-12DOI: 10.1016/s1470-2045(24)00707-1
N Lynn Henry
No Abstract
{"title":"Optimising therapy and avoiding overtreatment in breast cancer","authors":"N Lynn Henry","doi":"10.1016/s1470-2045(24)00707-1","DOIUrl":"https://doi.org/10.1016/s1470-2045(24)00707-1","url":null,"abstract":"No Abstract","PeriodicalId":22865,"journal":{"name":"The Lancet Oncology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142809734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-06DOI: 10.1016/s1470-2045(24)00711-3
Munyaradzi Makoni
No Abstract
{"title":"Africa renews commitment to an accelerated plan to end cervical cancer by 2030","authors":"Munyaradzi Makoni","doi":"10.1016/s1470-2045(24)00711-3","DOIUrl":"https://doi.org/10.1016/s1470-2045(24)00711-3","url":null,"abstract":"No Abstract","PeriodicalId":22865,"journal":{"name":"The Lancet Oncology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-06DOI: 10.1016/s1470-2045(24)00599-0
Yu Zhao, Shan Xiong, Qin Ren, Jun Wang, Min Li, Lin Yang, Di Wu, Kejing Tang, Xiaojie Pan, Fengxia Chen, Wenxiang Wang, Shi Jin, Xianling Liu, Gen Lin, Wenxiu Yao, Linbo Cai, Yi Yang, Jixian Liu, Jingxun Wu, Wenfan Fu, Wenhua Liang
<h3>Background</h3>Accurate detection of driver gene mutations is crucial for treatment planning and predicting prognosis for patients with lung cancer. Conventional genomic testing requires high-quality tissue samples and is time-consuming and resource-consuming, and as a result, is not available for most patients, especially those in low-resource settings. We aimed to develop an annotation-free Deep learning-enabled artificial intelligence method to predict GEne Mutations (DeepGEM) from routinely acquired histological slides.<h3>Methods</h3>In this multicentre retrospective study, we collected data for patients with lung cancer who had a biopsy and multigene next-generation sequencing done at 16 hospitals in China (with no restrictions on age, sex, or histology type), to form a large multicentre dataset comprising paired pathological image and multiple gene mutation information. We also included patients from The Cancer Genome Atlas (TCGA) publicly available dataset. Our developed model is an instance-level and bag-level co-supervised multiple instance learning method with label disambiguation design. We trained and initially tested the DeepGEM model on the internal dataset (patients from the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China), and further evaluated it on the external dataset (patients from the remaining 15 centres) and the public TCGA dataset. Additionally, a dataset of patients from the same medical centre as the internal dataset, but without overlap, was used to evaluate the model's generalisation ability to biopsy samples from lymph node metastases. The primary objective was the performance of the DeepGEM model in predicting gene mutations (area under the curve [AUC] and accuracy) in the four prespecified groups (ie, the hold-out internal test set, multicentre external test set, TCGA set, and lymph node metastases set).<h3>Findings</h3>Assessable pathological images and multigene testing information were available for 3697 patients who had biopsy and multigene next-generation sequencing done between Jan 1, 2018, and March 31, 2022, at the 16 centres. We excluded 60 patients with low-quality images. We included 3767 images from 3637 consecutive patients (1978 [54·4%] men, 1514 [41·6%] women, 145 [4·0%] unknown; median age 60 years [IQR 52–67]), with 1716 patients in the internal dataset, 1718 patients in the external dataset, and 203 patients in the lymph node metastases dataset. The DeepGEM model showed robust performance in the internal dataset: for excisional biopsy samples, AUC values for gene mutation prediction ranged from 0·90 (95% CI 0·77–1·00) to 0·97 (0·93–1·00) and accuracy values ranged from 0·91 (0·85–0·98) to 0·97 (0·93–1·00); for aspiration biopsy samples, AUC values ranged from 0·85 (0·80–0·91) to 0·95 (0·86–1·00) and accuracy values ranged from 0·79 (0·74–0·85) to 0·99 (0·98–1·00). In the multicentre external dataset, for excisional biopsy samples, AUC values ranged from 0·80 (95% CI
{"title":"Deep learning using histological images for gene mutation prediction in lung cancer: a multicentre retrospective study","authors":"Yu Zhao, Shan Xiong, Qin Ren, Jun Wang, Min Li, Lin Yang, Di Wu, Kejing Tang, Xiaojie Pan, Fengxia Chen, Wenxiang Wang, Shi Jin, Xianling Liu, Gen Lin, Wenxiu Yao, Linbo Cai, Yi Yang, Jixian Liu, Jingxun Wu, Wenfan Fu, Wenhua Liang","doi":"10.1016/s1470-2045(24)00599-0","DOIUrl":"https://doi.org/10.1016/s1470-2045(24)00599-0","url":null,"abstract":"<h3>Background</h3>Accurate detection of driver gene mutations is crucial for treatment planning and predicting prognosis for patients with lung cancer. Conventional genomic testing requires high-quality tissue samples and is time-consuming and resource-consuming, and as a result, is not available for most patients, especially those in low-resource settings. We aimed to develop an annotation-free Deep learning-enabled artificial intelligence method to predict GEne Mutations (DeepGEM) from routinely acquired histological slides.<h3>Methods</h3>In this multicentre retrospective study, we collected data for patients with lung cancer who had a biopsy and multigene next-generation sequencing done at 16 hospitals in China (with no restrictions on age, sex, or histology type), to form a large multicentre dataset comprising paired pathological image and multiple gene mutation information. We also included patients from The Cancer Genome Atlas (TCGA) publicly available dataset. Our developed model is an instance-level and bag-level co-supervised multiple instance learning method with label disambiguation design. We trained and initially tested the DeepGEM model on the internal dataset (patients from the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China), and further evaluated it on the external dataset (patients from the remaining 15 centres) and the public TCGA dataset. Additionally, a dataset of patients from the same medical centre as the internal dataset, but without overlap, was used to evaluate the model's generalisation ability to biopsy samples from lymph node metastases. The primary objective was the performance of the DeepGEM model in predicting gene mutations (area under the curve [AUC] and accuracy) in the four prespecified groups (ie, the hold-out internal test set, multicentre external test set, TCGA set, and lymph node metastases set).<h3>Findings</h3>Assessable pathological images and multigene testing information were available for 3697 patients who had biopsy and multigene next-generation sequencing done between Jan 1, 2018, and March 31, 2022, at the 16 centres. We excluded 60 patients with low-quality images. We included 3767 images from 3637 consecutive patients (1978 [54·4%] men, 1514 [41·6%] women, 145 [4·0%] unknown; median age 60 years [IQR 52–67]), with 1716 patients in the internal dataset, 1718 patients in the external dataset, and 203 patients in the lymph node metastases dataset. The DeepGEM model showed robust performance in the internal dataset: for excisional biopsy samples, AUC values for gene mutation prediction ranged from 0·90 (95% CI 0·77–1·00) to 0·97 (0·93–1·00) and accuracy values ranged from 0·91 (0·85–0·98) to 0·97 (0·93–1·00); for aspiration biopsy samples, AUC values ranged from 0·85 (0·80–0·91) to 0·95 (0·86–1·00) and accuracy values ranged from 0·79 (0·74–0·85) to 0·99 (0·98–1·00). In the multicentre external dataset, for excisional biopsy samples, AUC values ranged from 0·80 (95% CI","PeriodicalId":22865,"journal":{"name":"The Lancet Oncology","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-05DOI: 10.1016/s1470-2045(24)00712-5
Tony Kirby
No Abstract
{"title":"Smoke-free generation a step closer in the UK","authors":"Tony Kirby","doi":"10.1016/s1470-2045(24)00712-5","DOIUrl":"https://doi.org/10.1016/s1470-2045(24)00712-5","url":null,"abstract":"No Abstract","PeriodicalId":22865,"journal":{"name":"The Lancet Oncology","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142782375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-02DOI: 10.1016/s1470-2045(24)00647-8
In April, 2021, The Lancet Oncology published results of the phase 2 BYLieve study on alpelisib plus fulvestrant in PIK3CA-mutated, hormone receptor-positive advanced breast cancer.1
{"title":"Retraction and republication—Alpelisib plus fulvestrant in PIK3CA-mutated, hormone receptor-positive advanced breast cancer after a CDK4/6 inhibitor (BYLieve): one cohort of a phase 2, multicentre, open-label, non-comparative study","authors":"","doi":"10.1016/s1470-2045(24)00647-8","DOIUrl":"https://doi.org/10.1016/s1470-2045(24)00647-8","url":null,"abstract":"In April, 2021, <em>The Lancet Oncology</em> published results of the phase 2 BYLieve study on alpelisib plus fulvestrant in <em>PIK3CA</em>-mutated, hormone receptor-positive advanced breast cancer.<span><span><sup>1</sup></span></span>","PeriodicalId":22865,"journal":{"name":"The Lancet Oncology","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-02DOI: 10.1016/s1470-2045(24)00650-8
Rha SY, Oh DY, Yañez P, et al. Pembrolizumab plus chemotherapy versus placebo plus chemotherapy for HER2-negative advanced gastric cancer (KEYNOTE-859): a multicentre, randomised, double-blind, phase 3 trial. Lancet Oncol 2023; 24: 1181–95—In figure 2D of this Article, the datapoint showing the hazard ratio for no liver metastases was omitted. This correction has been made to the online version as of Dec 2, 2024.
{"title":"Correction to Lancet Oncol 2023; 24: 1181–95","authors":"","doi":"10.1016/s1470-2045(24)00650-8","DOIUrl":"https://doi.org/10.1016/s1470-2045(24)00650-8","url":null,"abstract":"<em>Rha SY, Oh DY, Yañez P, et al. Pembrolizumab plus chemotherapy versus placebo plus chemotherapy for HER2-negative advanced gastric cancer (KEYNOTE-859): a multicentre, randomised, double-blind, phase 3 trial.</em> Lancet Oncol <em>2023; <strong>24:</strong> 1181–95</em>—In figure 2D of this Article, the datapoint showing the hazard ratio for no liver metastases was omitted. This correction has been made to the online version as of Dec 2, 2024.","PeriodicalId":22865,"journal":{"name":"The Lancet Oncology","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-02DOI: 10.1016/s1470-2045(24)00628-4
Khushboo Chauhan, Vishal Ramesh Raval
Section snippets
Contributors
VRR was involved in the management of the patient. KC drafted the initial manuscript and collected the information and patient's details. The manuscript was finalised by VRR. Written informed consent to publication was obtained from the patient and his parents.
Declaration of interests
We declare no competing interests.
{"title":"Acute lymphoblastic leukaemia relapse presenting as optic nerve infiltration","authors":"Khushboo Chauhan, Vishal Ramesh Raval","doi":"10.1016/s1470-2045(24)00628-4","DOIUrl":"https://doi.org/10.1016/s1470-2045(24)00628-4","url":null,"abstract":"<h2>Section snippets</h2><section><section><h2>Contributors</h2>VRR was involved in the management of the patient. KC drafted the initial manuscript and collected the information and patient's details. The manuscript was finalised by VRR. Written informed consent to publication was obtained from the patient and his parents.</section></section><section><section><h2>Declaration of interests</h2>We declare no competing interests.</section></section>","PeriodicalId":22865,"journal":{"name":"The Lancet Oncology","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-02DOI: 10.1016/s1470-2045(24)00693-4
Villanueva-Meyer JE, Bakas S, Tiwari P, et al. Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements. Lancet Oncol 2024; 25: e581–88—In this Policy Review, co-author Hamed Akbari was affiliated incorrectly to the University of Pennsylvania. He should only be affiliated to Santa Clara University. This correction has been made to the online version as of Dec 2, 2024.
{"title":"Correction to Lancet Oncol 2024; 25: e581–88","authors":"","doi":"10.1016/s1470-2045(24)00693-4","DOIUrl":"https://doi.org/10.1016/s1470-2045(24)00693-4","url":null,"abstract":"<em>Villanueva-Meyer JE, Bakas S, Tiwari P, et al. Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements.</em> Lancet Oncol <em>2024; <strong>25:</strong> e581–88</em>—In this Policy Review, co-author Hamed Akbari was affiliated incorrectly to the University of Pennsylvania. He should only be affiliated to Santa Clara University. This correction has been made to the online version as of Dec 2, 2024.","PeriodicalId":22865,"journal":{"name":"The Lancet Oncology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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