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2817 citation(s) found for "Y, Liu".

Evaluating the utility of DNA methylation signatures in tissue and biofluids for lung adenocarcinoma brain metastasis prediction and non-invasive detection.
Zuccato, Jeffrey , Nassiri, Farshad , Ajisebutu, Andrew, Suppiah, Suganth , Saarela, Olli , Aldape, Kenneth , Zadeh, Gelareh, Mamatjan, Yasin , Liu, Jeff , Voisin, Mathew , Tsao, Ming‐Sound , Patil, Vikas S.

Journal of Clinical Oncology | May 28, 2025 | Volume: 43 | Issue: 16_suppl | Pages: 2030-2030

10.1200/jco.2025.43.16_suppl.2030 Get It

2030 Background: Brain metastases (BM) are common and arise in 30% of lung adenocarcinoma (LUAD) patients. Patients with LUAD that develop BM have significantly poorer outcomes, with a 10-16 month median overall survival. Unfortunately, current clinical practice for BM prediction is limited and so BM are typically detected after they develop and grow to cause neurological symptoms. Once BM are detected, currently neurosurgical tumor biopsies are performed to enable BM diagnosis via neuropathological evaluation. The aims of this study were to develop DNA methylation-based models that predict LUAD BM and non-invasively detect BM in blood to enable early diagnosis and treatment. Methods: DNA methylomes were acquired from 402 tumor tissue and plasma samples in a cohort of 346 LUAD and BM patients. Machine learning models were built using DNA methylation signatures that stratify BM risk in tissue and detect BM in plasma. Models were evaluated in independent validation datasets. A predictive nomogram was developed using the BM prediction model together with clinical factors to provide composite patient-specific scores reflecting BM risk. Results: The methylation-based BM predictor accurately stratified BM risk in a univariable Cox model using validation set data (HR = 5.65, 95%CI 1.85–17.2, p = 0.0023). Model utility was independent of the predictive value of clinical factors in a multivariable Cox model using validation set data (Table 1: HR = 8.92, 95%CI 1.97–40.5, p = 0.0046). The 5-year model accuracy was 0.81 and significantly higher than a similarly built cancer stage-based model (0.65), demonstrating utility over current practice. The combinatorial clinical-methylomic predictive nomogram had enhanced utility with an accuracy of 0.82 univariable Cox HR of 17.2 (95%CI 4.13–71.3, p < 0.0001), demonstrating comprehensive patient-specificity. The plasma-based model accurately classified BM from gliomas and lymphomas (AUROC=0.80), as typical clinical differential diagnoses, in validation set data. The models were validated further in additional external data. Conclusions: DNA methylation-based modeling of BM can accurately predict LUAD patients at risk for BM development and can non-invasively detect BM that develop. Future treatment approaches may tailor initial LUAD treatment and ongoing cancer surveillance to a patient’s BM risk, allowing for the potential to prevent and treat BM early. DNA methylation-based BM prediction is independent of clinical factors in a multivariable Cox proportional hazards model. Variable HR 95% CI p Methylome risk score 8.92 1.97–40.5 0.005 Age Years 0.96 0.92–1.02 0.177 Smoking Pack-years 0.99 0.95–1.03 0.496 EGFR Mutant vs wildtype 0.92 0.25–3.34 0.895 T T2 vs T1 1.58 0.41–6.04 0.505 T3−4 vs T1 1.49 0.28–7.98 0.642 N N1 vs N0 1.05 0.31–3.58 0.943 N2−3 vs N0 1.00 0.27–3.69 0.995 M M1 vs M0 145 12.2–1730 <0.001
Lung cancer combined with interstitial lung disease: Controversial role of preexisting hypertension and emphysema observed in a fully followed 20-year patient cohort.
Liu, Zhaoheng , Zong, Nansu , Swanson, Karen L., Wang, Zhichao , Wampfler, Jason A. , Yang, Ping

Journal of Clinical Oncology | May 28, 2025 | Volume: 43 | Issue: 16_suppl | Pages: 10556-10556

10.1200/jco.2025.43.16_suppl.10556 Get It

10556 Background: The coexistence of interstitial lung diseases (ILD) and lung cancer (LC) presents a clinical challenge. ILD, including idiopathic pulmonary fibrosis (IPF) and non-IPF entities, is associated with an elevated risk of LC. However, the clinical features, treatment patterns, and survival outcomes of ILD-LC, especially IPF-LC, remain underexplored. We aimed to comprehensively evaluate survival outcomes and risk factors for mortality of ILD-LC patients based on a real-world long-term clinical cohort. Methods: A total of 1,341 eligible patients for this study were drawn from the Epidemiology and Genetics of Lung Cancer research program database (Mayo Clinic) including 96 IPF-LC, 111 Non-IPF ILD-LC, 99 ILD-only, and 1,035 propensity-matched LC-only patients (Controls). Data were collected on demographics, comorbidities, tumor features, treatment, and patient outcomes. Survival analyses used Kaplan-Meier methods; risk factors were assessed via Cox Proportional Hazards models. All comparisons reported below were statistically significant with p-values≤0.001 unless specified. Results: Among the 20,470 primary LC patients who were diagnosed from 1997 through 2016 and followed to 2021, 207 (1.0%) had ILD-LC, of which 96 (46.4%) had IPF. Comparing to Controls, ILD-LC patients were 4 years older at the time of LC diagnosis, 63.8% (vs. 52.8%) male, had a higher rate of former smokers (60.4% vs. 47.3%), a lower proportion of adenocarcinoma (37.7% vs. 57.5%), and a higher proportion of squamous cell (28.0% vs. 20.3%) and small cell carcinoma (13.5% vs. 8.4%). Although no significant differences in either LC stage or grade, ILD-LC patients had a significantly higher mortality rate (90.3% vs. 78.8%), particularly among IPF-LC patients (97.9% vs. 83.8%). ILD-LC patients were less likely to undergo surgery or receiving pharmacological treatments (73.9% vs. 93.5%). The median 5-year overall survival rate (OS, months) were 49.5 for Controls, 35.2 for Non-IPF ILD-LC patients, and 15.9 for IPF-LC patients. Analysis of six most common comorbidities revealed that pulmonary hypertension was associated with worse OS in ILD-LC patients compared to those without it (11.9% vs. 20.1%, P < 0.05); however unexpectedly, hypertension in ILD-LC patients was associated with better OS (21.3% vs. 14.4%) and emphysema in IPF-LC patients with better OS (7.9% vs. 3.4%, P < 0.05) when compared to those without the two comorbidities, respectively. Conclusions: This study highlights the poorer survival outcomes in ILD-LC patients, particularly IPF-LC patients, emphasizing the need for further investigation into optimized therapies and comorbidity management. Our findings also call for attention to two unexpected associations of hypertension and emphysema, warranting in-depth research on the possible biological and physiological mechanisms.
Utilization of tumor-informed ctDNA to de-escalate treatment or monitoring in metastatic breast cancer exceptional responders.
Janda, Guneet, Haddad, Tufia C. , Peethambaram, Prema P. , Leon‐Ferre, Roberto A. , Calkins, Hannah, Rodriguez, Angel , Liu, Minetta C. , Goetz, Matthew P. , Scott, Nicole, Giridhar, Karthik V.

Journal of Clinical Oncology | May 28, 2025 | Volume: 43 | Issue: 16_suppl | Pages: No page available

10.1200/jco.2025.43.16_suppl.e13042 Get It

e13042 Background: The conventional treatment paradigm for metastatic breast cancer (MBC) is to continue systemic therapy until disease progression. With improved systemic therapy options, some patients (pts) with MBC attain radiographic remission. Tumor-informed circulating tumor (ct)DNA detects molecular disease burden and may guide decision-making. We report on an institutional cohort of pts with MBC in which tumor-informed ctDNA testing was utilized prospectively to guide decisions on systemic therapy or surveillance imaging frequency. Methods: Utilizing an IRB-approved protocol, we retrospectively identified pts monitored with tumor-informed ctDNA using the Signatera™ mPCR-NGS assay (Natera, Inc) as part of routine clinical care in Mayo Clinic, Rochester. We then identified a subset with biopsy-proven MBC who attained radiographic remission of disease following systemic therapy, deemed by the treating medical oncologist as “exceptional responders”. Real-time ctDNA testing was used to inform a decision to either discontinue systemic therapy, treatment de-escalation (remove a component of multi-drug therapy), increase the time interval between restaging scans, or stay off therapy previously stopped due to toxicity . Data on breast cancer characteristics, imaging results, ctDNA test results, and outcomes were collected. Descriptive statistics were used to analyze the data. Results: 10 pts met our study criteria. The median age at MBC diagnosis was 42 years (range 28-61); 5 pts had TNBC, 3 pts had HER2+, and 2 pts with ER+/HER2- MBC. Most pts (60%) were on first or second line therapy (range 1-5). The median duration of the most recent regimen was 27.5 months (range 10-70 months). All pts were without radiographic evidence of active cancer at the time of ctDNA testing with negative results (0 MTM/mL) at the time of clinical decision-making. 4 pts stopped all systemic therapy and proceeded with observation (1 trastuzumab, 1 trastuzumab deruxtecan, 1 pembrolizumab, and 1 talazoparib). 2 pts discontinued a targeted therapy (1 olaparib, 1 abemaciclib) while continuing endocrine therapy. 1 patient stopped targeted therapy (abemaciclib) due to a blood clot, and ctDNA testing guided the decision to not start another CDK4/6 inhibitor while continuing endocrine therapy. In 3 pts the frequency of restaging scans was decreased to either annually or semi-annually. The median follow-up after a ctDNA-informed decision was 11 months (range 1-41 months). At this time, no patient has converted to ctDNA-positivity, experienced resurgence or progression of disease, needed to change systemic therapy or increase imaging frequency. Conclusions: Tumor-informed ctDNA may offer additional confidence to oncologists and pts to deescalate systemic therapy or decrease the interval of imaging frequency in exceptional responders with MBC. Additional data to support this approach is needed.
A retrospective cohort study of real-world clinical outcomes in patients with CLL/SLL previously treated with covalent BTK inhibitor (cBTKi) and BCL2 inhibitor (BCL2i) regimens.
Prescott, Jennifer , Parrinello, Christina M. , Sawas, Ahmed , Nigris, Enrico De, Yang, Jing , Shrivastava, Indu, Liu, Xinyue , Farooqui, Mohammed Z.H. , Bloomquist, Erik W. , Shao, Changxia , Parikh, Sameer A.

Journal of Clinical Oncology | May 28, 2025 | Volume: 43 | Issue: 16_suppl | Pages: No page available

10.1200/jco.2025.43.16_suppl.e19025 Get It

e19025 Background: Recent studies suggest that patients with CLL/SLL (herein, CLL) relapsed/refractory (R/R) to both cBTKi and BCL2i (double R/R [DR]) may have poorer outcomes than the broader group of double-exposed (DE) patients. This study evaluated real-world response (rwR) and time-to-event (TTE) outcomes of a CLL DE cohort and DR subcohort in the US. Methods: This retrospective study used the nationwide Flatiron Health electronic health record-derived deidentified database to include patients with CLL treated with sequential or combination cBTKi and BCL2i in any line of therapy (LOT) on or after January 1, 2011, and a subsequent LOT (ie, index LOT) on or before November 30, 2022 (DE cohort). A subset with DR CLL (DR subcohort) was analyzed separately. Descriptive statistics were generated for demographic and clinical characteristics. Outcomes included clinician-confirmed rwR rate (rwRR) and median TTE outcomes (real-world duration of response [rwDOR], progression-free survival [rwPFS], time to next treatment [rwTTNT], and overall survival [rwOS]; Kaplan Meier, follow-up to May 31, 2023). Results: The DE cohort included 264 patients, 107 of whom were in the DR subcohort. Patients were mostly male (64%), White (75%), aged ≥65 years at first index LOT (72%) after prior cBTKi and BCL2i treatment, and treated in the community setting (71%). Approximately one-third of patients received a subsequent treatment after each index LOT. Most patients were re-treated with a cBTKi and/or BCL2i in the first index LOT (69% DE; 54% DR), and the percentage treated with chemotherapy increased with each subsequent index LOT. Confirmed rwRR was similar between DE and DR for all index LOTs (Table). More relevant are the median TTE outcomes, which were shorter for DR than DE, especially for the first index LOT, and shorter in subsequent index LOTs for both groups. Conclusions: The high percentage of patients re-treated with cBTKi and BCL2i therapies and increasing use of chemotherapy regimens with each subsequent index LOT indicate a lack of standard of care and options. Our results suggest a high unmet need in the DE CLL population, particularly in patients DR to cBTKi and BCL2i therapy, who have worse outcomes. Efficacious and durable therapy options are needed to address this unmet need. Double Exposed Index LOT Double R/R Index LOT 1st 2nd 3rd 1st 2nd 3rd N 264 90 35 107 41 12 rwRR, % (95% CI) 39(33-45) 42(32-53) 40(24-58) 36(27-45) 34(21-51) 42(16-71) Median TTE outcomes, months (95% CI) rwDOR 20.3 (13.5-32.6) 6.0(5.5-NR) 3.1(2.7-NR) 11.5 (7.9-23.5) 6.0(3.6-NR) 2.9(2.7-NR) rwPFS 8.8(7.5-10.6) 5.1(4.0-7.3) 4.3(2.9-6.3) 5.0 (4.0-7.3) 3.6(2.6-5.7) 4.7(2.7-NR) rwTTNT 13.8 (10.3-18.3) 7.9(5.7-11.3) 5.7(4.2-14.6) 8.0 (6.2-11.5) 5.9(4.4-11.4) 5.0(4.2-NR) rwOS 25.2 (22.1-37.1) 15.2(11.4-22.6) 6.6(5.7-NR) 14.7 (12.3-22.6) 11.4(5.9-NR) 6.6(5.0-NR)
Lipid metabolism in health and disease: Mechanistic and therapeutic insights for Parkinson’s disease
Qin, Bingqing , Fu, Yuan , Kong, Shuangyu, Li, Han , Liu, Chunfeng, Zhao, Jing , Raulin, Ana‐Caroline , Kong, Shuangyu, Liu, Junyi

Chinese Medical Journal | May 26, 2025 | Volume: 138 | Issue: 12 | Pages: 1411-1423

10.1097/cm9.0000000000003627 | PMID 40419446 Get It

Abstract Parkinson’s disease (PD) is a neurodegenerative disorder characterized by the progressive loss of dopaminergic neurons and the accumulation of Lewy bodies, leading to motor and nonmotor symptoms. While both genetic and environmental factors contribute to PD, recent studies highlight the crucial role of lipid metabolism disturbances in disease progression. Altered lipid homeostasis promotes protein aggregation and oxidative stress, with significant changes in lipid classes such as sphingolipids and glycerolipids observed in patients with PD. These disturbances are involved in key pathological processes, such as α-synuclein aggregation, organelle dysfunction, lipid-mediated neuroinflammation, and impaired lipid homeostasis. This review examines the relationship between lipid species and PD progression, focusing on the physiological roles of lipids in the central nervous system. It explores the mechanistic links between lipid metabolism and PD pathology, along with lipid-related genetic risk factors. Furthermore, this review discusses lipid-targeting therapeutic strategies to mitigate PD progression, emphasizing the potential of lipid modulation for effective treatment development.
Ubiquitin–proteasome system in the different stages of dominantly inherited Alzheimer's disease
Hassenstab, Jason , Sung, Yun Ju, Daniels, Alisha , Xiong, Chengjie , Perrin, Richard J. , Day, Gregory S. , Supnet, Charlene , Berman, Sarah , Chhatwal, Jasmeer P. , Huey, Edward D. , Ryan, Natalie S. , Laske, Christoph , Xiong, Chengjie , Roh, Jee Hoon , Lopera, Francisco , team, For DIAN study, McDade, Eric M , Day, Gregory S , Xiong, Chengjie , Hassenstab, Jason , Roh, Jee Hoon , Supnet-Bell, Charlene , Berman, Sarah , Liu, Haiyan , Bui, Quoc Van Phu , Gordon, Brian A. , Benzinger, Tammie L.S. , Timsina, Jigyasha , Sung, Yun Ju, Karch, Celeste M. , Renton, Alan , Morris, John C. , Ibanez, Laura , Llibre‐Guerra, Jorge J. , Xu, Xiong, Ikeuchi, Takeshi , Kasuga, Kensaku , Niimi, Yoshiki , Schofield, Peter R. , Brooks, William S. , Jucker, Mathias , Vöglein, Jonathan , Lopera, Francisco Javier , Bateman, Randall J. , Cruchaga, Carlos , McDade, Eric , team, For DIAN study

Alzheimer's & Dementia | May 24, 2025 | Volume: 21 | Issue: 5 | Pages: No page available

10.1002/alz.70243 | PMID 40411302 Get It

AbstractINTRODUCTIONThis study investigated the role of the ubiquitin–proteasome system (UPS) in dominantly inherited Alzheimer's disease (DIAD) by examining cerebrospinal fluid (CSF) levels of UPS proteins.METHODThe SOMAscan assay was used to detect changes in UPS proteins in mutation carriers (MCs) relative to disease progression; imaging and CSF biomarkers of amyloid, tau, and neurodegeneration measures; and Clinical Dementia Rating scale.RESULTSSubtle increases in specific ubiquitin enzymes were detected in MCs up to two decades before symptom onset, with more pronounced elevations in UPS‐activating enzymes near symptom onset. Significant correlations were found between UPS proteins and Alzheimer's disease (AD) biomarkers, especially between autophagy markers and late‐stage tau biomarkers, microglia, and axonal degeneration.DISCUSSIONThe rise in UPS proteins alongside tau‐related markers suggests UPS involvement in tau neurofibrillary tangles. Elevated CSF UPS proteins in DIAD MCs may serve as indicators of disease progression, and may support the UPS as a therapeutic target in AD.Highlights This study investigates the ubiquitin‐proteasome system (UPS) in Dominantly Inherited Alzheimer's Disease (DIAD), highlighting early molecular changes linked to disease progression. Using SOMAscan proteomics, we identified significant UPS protein alterations in cerebrospinal fluid of mutation carriers, notably up to 20 years before clinical symptom onset. Correlations between UPS protein levels and Alzheimer's biomarkers, particularly tau and neurodegeneration markers, suggest a strong association between UPS dysregulation and tau pathology in DIAD. Dynamic UPS changes align with A/T biological staging: UPS proteins were shown to increase across Aβ/tau (A/T) groups, with largest increases in the A+/T+ group, reinforcing their role in late‐stage tau pathology and disease progression. These findings underscore the potential of UPS proteins as early biomarkers for Alzheimer's disease progression and as novel therapeutic targets, especially in tau‐pathology‐driven neurodegeneration. This work contributes to understanding AD pathogenesis, by emphasizing the importance of protein quality control systems and by offering avenues for future biomarker discovery and therapeutic development in Alzheimer's disease.
Engineered exosome-based treatment for peripheral nerve regeneration: a narrative review of clinical prospects
Yao, Xiaomin, Zhou, Yuxiang , Wan, Xue , Yang, Pengxiang , Liu, Yingyu , Jie, Jing

Advanced Technology in Neuroscience | May 23, 2025 | Volume: 2 | Issue: 3 | Pages: 135-143

10.4103/atn.atn-d-25-00009 Get It

With their unique therapeutic properties and amenability to engineering modifications, exosomes are considered a promising tool for treating peripheral nerve injuries. Through genetic engineering or physicochemical modifications, the targeting ability and functionality of exosomes can be further enhanced, enabling efficient delivery of therapeutic molecules and significantly boosting their therapeutic potential. This review aims to explore exosome-based therapies in peripheral nerve repair and their mechanisms and summarizes recent advances in the roles of exosomes in neuroprotection, axonal regeneration, vascular regeneration, and inflammatory modulation. Exosomes act as natural messengers between cells, carrying genetic materials, cytokines, and growth factors that can modulate the local microenvironment and foster peripheral nerve regeneration. Studies have shown that exosomes derived from Schwann cells, macrophages, neurons, and mesenchymal stem cells can all promote axonal regeneration and modulate inflammatory responses through various mechanisms. Compared with cell therapy, exosome therapy offers greater stability and safety, with the ability to traverse biological barriers, presenting new possibilities for regenerative medicine and precision therapy. However, challenges such as the heterogeneity and complexity of exosomes, uncertainty of in vivo distribution and metabolic pathways, and the limitations of targeting remain to be addressed. Future research will focus on the engineering modifications of exosomes to enhance their therapeutic efficiency and targeting precision.
Global, Regional, and National Burden of Nontraumatic Subarachnoid Hemorrhage
Collaborators, GBD 2021 Global Subarachnoid Hemorrhage Risk Factors, Rautalin, Ilari, Stark, Benjamin, Johnson, Catherine O., Kaprio, Jaakko , Korja, Miikka, Krishnamurthi, Rita , Nair, Balakrishnan , Vergouwen, Mervyn D. I. , Abdelkader, Atef , Abdullahi, Auwal , Abualruz, Hasan , Abukhadijah, Hana J , Adamu, Lawan Hassan, Adegboye, Oyelola A. , Adzigbli, Leticia Akua, Agyemang‐Duah, Williams , Ahinkorah, Bright Opoku, Ahmad, Aqeel , Ahmad, Danish , Ahmadzade, Amir Mahmoud , Akkaif, Mohammed Ahmed , Ali, Mohammed Usman, Alif, Sheikh Mohammad , Alimohamadi, Yousef, Alsabri, Mohammed , Altaf, Awais , Alvis‐Guzmán, Nelson , Alwafi, Hassan , Al‐Wardat, Mohammad , Alyahya, Mohammad S. , Amin, Tarek Tawfik , Amindarolzarbi, Alireza , Amusa, Ganiyu , Angappan, Dhanalakshmi , Anuoluwa, Boluwatife Stephen, Anwar, Saleha, Anyasodor, Anayochukwu Edward , Apostol, Geminn Louis Carace, Areda, Demelash, Artamonov, Anton A, Artanti, Kurnia Dwi , Ashemo, Mubarek Yesse , Athar, Mohammad , Aujayeb, Avinash , Awotidebe, Adedapo Wasiu , Azadnajafabad, Sina , Aziz, Shahkaar , Bagheri, Nasser , Taqanaki, Pegah Bahrami , Bahramian, Saeed , Bai, Ruhai , Baig, Atif Amin , Baltatu, Ovidiu Constantin , Bam, Kiran, Barker‐Collo, Suzanne , Bärnighausen, Till , Bastan, Mohammad‐Mahdi , Basu, Sanjay , Bell, Shelly L, Benseñor, Isabela M , Berhie, Alemshet Yirga , Bhagavathula, Akshaya Srikanth , Bhaskar, Sonu , Bhat, Ajay Nagesh, Bhat, Vivek , Birhan, Mekuriaw Mesfin , Borhany, Hamed, Breitner, Susanne , Bulamu, Norma B. , Butt, Zahid A , Cabral, Lucas Scotta , Santos, Florentino Luciano Caetano dos , Cámera, Luis Alberto , Capodici, Angelo , Chadwick, Joshua , Chakraborty, Promit Ananyo , Chanie, Gashaw Sisay , Chattu, Vijay Kumar , Chi, Gerald, Chichagi, Fatemeh , Columbus, Alyssa , Criqui, Michael H, Ohadi, Mohammad Amin Dabbagh , Dadras, Omid , Dalli, Lachlan L. , Debopadhaya, Shayom , Dervišević, Emina , Devanbu, Vinoth Gnana Chellaiyan, Dewan, Syed Masudur Rahman , Dhane, Amol S. , Chi, Thanh, Phuong, Thao Huynh, Dohare, Sushil , Докова, Клара , Dowou, Robert Kokou , Dsouza, Haneil Larson , Dutta, Siddhartha , Dziedzic, Arkadiusz , E’mar, Abdel Rahman, Edvardsson, David , Efendi, Ferry, Nahas, Nevine El , Elgendy, Islam Y. , Eltaha, Chadi, Emeto, Theophilus I. , Fakhradiyev, Ildar , Faris, Pawan , Fauk, Nelsensius Klau , Fazylov, Timur, Fekadu, Ginenus , Fetensa, Getahun, Fischer, Florian, Foschi, Matteo , Fridayani, Ni Kadek Yuni, Gaipov, Abduzhappar , Gajjar, Avi A. , Ganesan, Balasankar , Gebregergis, Miglas Welay, Gebremeskel, Teferi Gebru , Getie, Molla , Ghadirian, Fataneh , Ghasemzadeh, Afsaneh , Gholamalizadeh, Maryam , Gil, Artyom , Goleij, Pouya , Goulart, Alessandra C. , Goulart, Bárbara Niegia Garcia de , Guan, Shi‐Yang , Gupta, Sapna , Habibzadeh, Farrokh , Hadi, Najah R, Hankey, Graeme J. , Harlianto, Netanja I. , Hasani, Hamidreza, Hasnain, Md Saquib , Haubold, Johannes , Havmoeller, Rasmus, Hay, Simon I , Hbid, Youssef , Heidari, Golnaz , Hiraike, Yuta, Hoan, Nguyen Quoc , Hosseinzadeh, Mehdi , Hwang, Bing‐Fang , Ibitoye, Segun Emmanuel , Ikiroma, Adalia , Isola, Gaetano , Iwagami, Masao , Jacob, Louis, Jain, Akhil, Jakovljević, Mihajlo , Jatau, Abubakar Ibrahim , Jawaid, Talha, Jayapal, Sathish Kumar , Jonas, Jost B, Joseph, Nitin , Jürisson, Mikk , Kadashetti, Vidya, Kalani, Rizwan, Kamireddy, Arun , Kandel, Himal , Behnagh, Arman Karimi , Kashoo, Faizan , Kayode, Gbenga A , Kazemi, Foad , Khader, Yousef , Khaing, Inn Kynn , Khan, Fayaz Ahmad , Khan, Mohammad Jobair , Kashani, Hamid Reza Khayat , Khosla, Atulya Aman , Kim, Kwanghyun , Kim, Yun Jin , Korzh, Oleksii , Krishan, Kewal , Krishna, Varun, Krishnamoorthy, Vijay , Kuddus, Mohammed , Kulimbet, Mukhtar , Lahariya, Chandrakant, Lai, Daphne Teck Ching , Lai, Hanpeng , Laksono, Tri, Lallukka, Tea , Latifinaibin, Kaveh, Le, Nhi Huu Hanh, Le, Thao T. , Lee, Munjae , Lee, Yo Han , Lenzi, Jacopo , Leonardi, Matilde, Li, Ming‐Chieh , Li, Xiaopan , Lim, Stephen S , Lin, Jialing , Saute, Ricardo Lutzky , Malik, Ahmad Azam , Marateb, Hamid Reza , Maroufi, Seyed Farzad , Maugeri, Andrea , McPhail, Steven , Mehndiratta, Man Mohan, Mestrovic, Tomislav, Mettananda, Chamila Dinushi Kukulege , Miazgowski, Tomasz , Minervini, Giuseppe , Mithra, Prasanna , Mohamed, Abdalla Z. , Mohammad, Ameen Mosa , Mohammed, Shafiu , Moni, Mohammad Ali , Moradi, Yousef, Moraga, Paula , Morawska, Lídia , Munjal, Kavita , Murray, Christopher J L , Nagarajan, Ahamarshan Jayaraman , Naik, Ganesh R. , Najdaghi, Soroush , Davani, Delaram Narimani , DELETE-ME, Nawsherwan, Nazri-Panjaki, Athare , Negoi, Ruxandra Irina , Nematollahi, Soroush , Nguyen, Hau Thi Hien, Nguyen, Hau Thi Hien, Nguyen, Phat Tuan , Nguyễn, Văn Thành , Niazi, Robina Khan, Nikoobar, Ali , Sá, Antonio Tolentino Nogueira de , Nomura, Shuhei , Nzoputam, Chimezie Igwegbe , Oancea, Bogdan , Ojo-Akosile, Tolulope R, Oliveira, Gláucia Maria Moraes de , Olorukooba, Abdulhakeem Abayomi , Osuagwu, Uchechukwu Levi , Ouyahia, Amel, A, Mahesh Padukudru P, Panagiotakos, Demosthenes B. , Parikh, Romil R., Park, Seoyeon , Pedersini, Paolo , Peprah, Emmanuel , Perico, Norberto, Petermann-Rocha, Fanny Emily, Plotnikov, Evgenii , Полибин, Р. В. , Postma, Maarten J. , Prasad, Manya , Qian, Gangzhen , Rahmanian, Mohammad , Rajaa, Sathish , Rajput, Prashant , Ramadan, Mahmoud M. , Rane, Amey, Rashidi, Mohammad‐Mahdi , Razo, Christian , Reddy, Murali Mohan Rama Krishna, Remuzzi, Giuseppe, Rezaei, Negar , Rezaeian, Mohsen , Rocha, Hermano Alexandre Lima , Rodriguez, Jefferson Antonio Buendia, Roever, Leonardo , Romoli, Michele, Romozzi, Marina, Roy, Nitai , Saddik, Basema Ahmad, Sahoo, Pragyan Monalisa , Salaroli, Luciane Bresciani , Samuel, Vijaya Paul, Samy, Abdallah M , Santric-Milicevic, Milena M., Saravanan, Aswini, Schlaich, Markus P. , Sethi, Yashendra , Seylani, Allen, Shaaban, Ahmed Nabil , Shafie, Mahan , Shaikh, Summaiya Zareen , Shamsutdinova, Alfiya, Sharifan, Amin , Rad, Javad Sharifi, Sharma, Vishal, Shashamo, Bereket Beyene , Shetty, Premalatha K, Shigematsu, Mika, Shorofi, Seyed Afshin, Singh, Paramdeep , Singh, Puneetpal , Singh, Surjit , Sobia, Farrukh , Solanki, Shipra, Soraneh, Soroush , Spartalis, Michael, Srinivasamurthy, Suresh Kumar , Stanikzai, Muhammad Haroon , Sun, Zhong , Swain, Chandan Kumar , Damavandi, Payam Tabaee , Tabatabaeizadeh, Seyed-Amir, Tabche, Celine , Taiba, Jabeen , Tamuzi, Jacques Lukenze , Tan, Ker‐Kan , Teramoto, Masayuki , Thakur, Ramna, Thankappan, Kavumpurathu Raman , Thayakaran, Rasiah , Thirunavukkarasu, Sathish, Ticoalu, Jansje Henny Vera, Tiwari, Krishna Raj , Tonelli, Marcello , Topór-Mądry, Roman , Tovani-Palone, Marcos Roberto Roberto, Tran, Jasmine, Tran, Thang Huu , Duc, Nguyen Tran Minh , Truelsen, Thomas Clement, Unim, Brigid, Vahdati, Sanaz, Vaithinathan, Asokan Govindaraj , Eynde, Jef Van den , Vladimirov, Sergey Konstantinovitch , Waheed, Yasir, Wahood, Waseem , Walde, Mandaras Tariku, Wang, Yanzhong , Wickramasinghe, Nuwan Darshana , Wolfe, Charles D. A., Wubie, Yihun Miskir, Xiao, Hong , Xu, Xiaoyue , Yano, Yuichiro , Yarahmadi, Amir , Ye, Pengpeng, Yon, Dong Keon , Zanghì, Aurora , Zare, Iman, Zastrozhin, Michael, Zhang, Zhi-Jiang , Zhang, Zhiqiang , Zhang, Zhiqiang, Zhao, Hanqing , Zhou, Shang Cheng, Zhumagaliuly, Abzal , Zia, Hafsa, Roth, Gregory A., Topór‐Mądry, Roman , Oliveira, Arão Belitardo , Rahim, Fakher , Saddik, Basema , Waheed, Yasir , Liu, Xuefeng , Zhang, Yunquan , Ozair, Ahmad , Buendía, Jefferson Antonio , Korja, Miikka , Baig, Atif Amin , Shanawaz, Mohd , Elgendy, Islam Y , Hankey, Graeme J , Alif, Sheikh M , Rezaei, Negar , Bhagavathula, Akshaya Srikanth , Van den Eynde, Jef , Ozair, Ahmad , Collaborators, GBD 2021 Global Subarachnoid Hemorrhage Risk Factors, Rautalin, Ilari Matias , Volovici, Victor , Ranta, Annemarei , Rinkel, Gabriël J.E. , Abate, Yohannes Habtegiorgis, Abbastabar, Hedayat , Abd-Allah, Foad , Abdi, Parsa , Abdollahi, Arash , Abiodun, Olugbenga Olusola , Aboagye, Richard Gyan , Abouzid, Mohamed , Abtahi, Dariush , Abu‐Rumeileh, Samir , Abualhasan, Ahmed , Abu‐Zaid, Ahmed , Addo, Isaac Yeboah , Adedoyin, Rufus Adesoji , Adra, Saryia , Ahinkorah, Bright Opoku , Ahmed, Ali , Ahmed, Haroon , Ahmed, Syed Anees , Aji, Budi , Al‐Ajlouni, Yazan A. , Al‐Aly, Ziyad , ALBashtawy, Mohammed , Aljunid, Syed Mohamed , Alomari, Mahmoud A. , Alrawashdeh, Ahmad , Alsabri, Mohammed A., Salman, Rustam Al‐Shahi , Al‐Tammemi, Ala’a B. , Al‐Worafi, Yaser Mohammed , Aly, Hany Z. , Alzoubi, Karem Hasan , Amani, Reza, Anderlini, Deanna , Anil, Abhishek , Arabloo, Jalal , Ärnlöv, Johan , Arumugam, Ashokan , Aryan, Zahra , Jafarabadi, Mohammad Asghari , Ashraf, Tahira , Athari, Seyyed Shamsadin , Azzam, Ahmed Y , Babu, Giridhara R. , Bako, Abdulaziz Tijjani , Banach, Maciej , Bandyopadhyay, Soham , Banik, Biswajit , Bardhan, Mainak , Barqawi, Hiba Jawdat , Barua, Lingkan , Beyene, Kebede Abera , Bhat, Vivek, Bhatti, Gurjit Kaur , Bhatti, Jasvinder Singh , Bijani, Ali , Bikbov, Boris , Birhanu, Mulugeta Molla , Bitra, Veera Raghavulu , Boloor, Archith , Brenner, Hermann , Bugiardini, Raffaele , Călina, Daniela Cornelia , Campos, Luciana Aparecida , Campos‐Nonato, Ismael , Carvalho, Félix , Castañeda-Orjuela, Carlos A , Catapano, Alberico Luigi , Cegolon, Luca , Chakraborty, Chiranjib , Chakraborty, Sandip , Chandika, Rama Mohan , Chaudhary, Anis Ahmad , Chi, Gerald , Ching, Patrick R , Chopra, Hitesh , Choudhari, Sonali Gajanan , Chowdhury, E. , Chu, Dinh‐Toi , Chung, Sheng‐Chia , Silva, Alanna Gomes da , Dai, Xiaochen , Dalal, Koustuv , D’Amico, Emanuele , Dashti, Mohsen , Davletov, Kairat , Cruz-Góngora, Vanessa Vianey De la , Delgado‐Enciso, Iván , Devanbu, Vinoth Gnana Chellaiyan, Dibas, Mahmoud , Doheim, Mohamed Fahmy , Dongarwar, Deepa , D’Oria, Mario , Doshi, Ojas Prakashbhai , Doshi, Rajkumar , Efendi, Defi , Efendi, Ferry , Elhadi, Muhammed , Eltahir, Mohd. Elmagzoub , Fabin, Natalia , Fagbamigbe, Adeniyi Francis , Fahim, Ayesha , Fares, Jawad , Ferreira, Nuno Bravo , Gandhi, Aravind P. , Garg, Ravindra Kumar , Gebrehiwot, Mesfin , Ghadimi, Delaram J. , Ghahramani, Sulmaz , Ghazy, Ramy Mohamed , Ghozy, Sherief , Gilani, Jaleed Ahmed , Gnedovskaya, Elena V. , Hadei, Mostafa , Hamidi, Samer , Hanifi, Nasrin , Haro, Josep María , Hasan, Faizul , Tabatabaei, Mahgol Sadat Hassan Zadeh , Heidari, Mohammad , Hemmati, Mehdi , Holla, Ramesh , Hostiuc, Sorin , Huang, Junjie , Huynh, Hong-Han , Ikeda, Nayu , Ilaghi, Mehran , Ilesanmi, Olayinka Stephen , Ilić, Irena , Ilić, Milena , Islam, Md. Rabiul , Ismail, Nahlah Elkudssiah , Iso, Hiroyasu , Jafarzadeh, Abdollah , Jairoun, Ammar Abdulrahman , Kalani, Rizwan, Kamal, Vineet Kumar , Kanchan, Tanuj , Karami, Jafar , Karaye, Ibraheem M. , Karimi, Yeganeh , Kesse‐Guyot, Emmanuelle , Khan, Fayaz, Khatatbeh, Haitham , Khatatbeh, Moawiah Mohammad , Kheirallah, Khalid Ahmed , Khidri, Feriha Fatima , Khormali, Moein , Kısa, Adnan , Kısa, Sezer , Kivimäki, Mika , Kolahi, Ali‐Asghar , Kompani, Farzad , Kostev, Karel , Kothari, Nikhil , Kunutsor, Setor K. , Kurniasari, Maria Dyah , Kusuma, Dian Palupi , Kytö, Ville , Vecchia, Carlo La , Lahariya, Chandrakant , Latief, Kamaluddin , Lee, Seung Won , Lee, Wei‐Chen , Leonardi, Matilde, Liu, Xuefeng , Lohner, Valerie , Lorenzovici, László , Lotufo, Paulo A. , Lucchetti, Giancarlo , Lusk, Jay B. , Amin, Hawraz Ibrahim M. , Malhotra, Armaan Kush , Malhotra, Kashish , Malta, Déborah Carvalho , Mansournia, Mohammad Alì , Mantovani, LG , Manu, Emmanuel, Marino, Mirko , Martínez, Ramón , Martini, Santi , Martorell, Miquel , Marzo, Roy Rillera , Mathangasinghe, Yasith , Mathews, Elezebeth , Mehmood, Asim , Mehrabani-Zeinabad, Kamran , Menezes, Ritesh G. , Meo, Sultan A , Meretoja, Atte , Meštrović, Tomislav , Sá, Ana Carolina Micheletti Gomide Nogueira de , Minh, Le Huu Nhat , Mirică, Andreea , Мirrakhimov, Erkin М , Mirza‐Aghazadeh‐Attari, Mohammad , Mishra, Ajay Kumar , Mohamed, Ahmed Ismail , Mohammadi, Soheil , Mohammadian-Hafshejani, Abdollah , Mokdad, Ali H. , Molinaro, Sabrina , Momani, Shaher , Ghalibaf, AmirAli Moodi , Moradi, Maryam , Msherghi, Ahmed A.A. , Ansari, Noureddin Nakhostin , Nargus, Shumaila , Natto, Zuhair S. , Nauman, Javaid , DELETE-ME, Nawsherwan, Nayak, Vinod C , Newton, Charles R. , Nigatu, Yeshambel T. , Noubiap, Jean Jacques , Nugen, Fred , Oduro, Michael Safo , Okati‐Aliabad, Hassan , Okeke, Sylvester Reuben , Okekunle, Akinkunmi Paul , Olagunju, Andrew T , Olaiya, Muideen Tunbosun , Olufadewa, Isaac Iyinoluwa , Ornello, Raffaele , Ortiz‐Prado, Esteban , Ouyahia, Amel , Owolabi, Mayowa , Padrón‐Monedero, Alicia , Padubidri, Jagadish Rao , Panos, Leonidas D , Pantazopoulos, Ioannis N. , Parikh, Romil R., Patel, Jay , Patel, Urvish , Patoulias, Dimitrios , Pereira, Gavin F , Perianayagam, Arokiasamy , Perna, Simone , Petermann‐Rocha, Fanny , Philip, Anil K. , Пирадов, М. А. , Pradhan, Jalandhar , Puvvula, Jagadeesh , Qasim, Nameer Hashim , Raggi, Alberto , Rahimi‐Movaghar, Vafa , Rahman, Muhammad Aziz , Rahmani, Amir Masoud , Sanati, Ali Rajabpour , Ramasamy, Shakthi Kumaran , Ramazanu, Sheena , Rathish, Devarajan , Rawaf, Salman , Redwan, Elrashdy M , Remuzzi, Giuseppe , Romoli, Michele, Romozzi, Marina , Ross, Allen Guy , Rout, Himanshu Sekhar , Roy, Priyanka , Saad, Aly M A , Saadatian, Zahra , Sabour, Siamak , Sacco, Simona , Sadeghi, Erfan , Saeed, Usman, Sharif‐Askari, Fatemeh Saheb , Sahebkar, Amirhossein , Sajib, Md Refat Uz Zaman , Saleh, Mohamed A. , Samodra, Yoseph Leonardo , MİLİCEVİC, Milena Santric, Saravanan, Aswini , Sarkar, Tanmay, Sarode, Sachin Chakradhar , Sartorius, Benn , Satpathy, Maheswar , Schneider, Ione Jayce Ceola , Schuermans, Art , Selvaraj, Siddharthan , Senthilkumaran, Subramanian , Sepanlou, Sadaf Ghajarieh , Shahwan, Moyad Jamal , Shamim, Muhammad Aaqib , Shamsi, Anas , Shannawaz, Mohammed , Rad, Javad Sharifi, Sharma, Vishal , Shittu, Aminu , Shiue, Ivy , Shlobin, Nathan A. , Siddig, Emmanuel Edwar , Spartalis, Michael , Stanaway, Jeffrey D , Starodubova, Antonina Vladimirovna , Szarpak, Łukasz , Tabatabaeizadeh, Seyed‐Amir , Tabatabaeizadeh, Seyed-Amir, Talaat, Iman Mamdouh , Temsah, Mohamad‐Hani , Thakur, Ramna , Thirunavukkarasu, Sathish, Tiwari, Krishna, Truyen, Thien Tan Tri Tai , Tsai, Daniel Hsiang‐Te , Ullah, Atta , Unnikrishnan, Bhaskaran , Unsworth, Carolyn Anne, Usman, Jibrin Sammani , Valizadeh, Rohollah , Varghese, Joe , Vasankari, Tommi Juhani , Venketasubramanian, Narayanaswamy , Vervoort, Dominique , Villafañe, Jorge Hugo , Vinayak, Manish , Wafa, Hatem A , Walde, Mandaras Tariku, Willeit, Peter , Wolfe, Charles D.A. , Wolfe, Charles D. A., Yamagishi, Kazumasa , Yaribeygi, Habib , Yaya, Sanni , Ye, Pengpeng, Yonemoto, Naohiro , Yu, Chuanhua , Zastrozhin, Michael, Zhang, Chen , Zhang, Zhiqiang, Zielińska, Magdalena , Zyoud, Samer H. , Roth, Gregory A. , Feigin, Valery L. , Kulimbet, Mukhtar , Rahim, Fakher

JAMA Neurology | May 23, 2025 | Volume: 82 | Issue: 8 | Pages: 765

10.1001/jamaneurol.2025.1522 | PMID 40406922 Get It

Importance Nontraumatic subarachnoid hemorrhage (SAH) represents the third most common stroke type with unique etiologies, risk factors, diagnostics, and treatments. Nevertheless, epidemiological studies often cluster SAH with other stroke types leaving its distinct burden estimates obscure. Objective To estimate the worldwide burden of SAH. Design, Setting, and Participants Based on the repeated cross-sectional Global Burden of Disease (GBD) 2021 study, the global burden of SAH in 1990 to 2021 was estimated. Moreover, the SAH burden was compared with other diseases, and its associations with 14 individual risk factors were investigated with available data in the GBD 2021 study. The GBD study included the burden estimates of nontraumatic SAH among all ages in 204 countries and territories between 1990 and 2021. Exposures SAH and 14 modifiable risk factors. Main Outcomes and Measures Absolute numbers and age-standardized rates with 95% uncertainty intervals (UIs) of SAH incidence, prevalence, mortality, and disability-adjusted life-years (DALYs) as well as risk factor–specific population attributable fractions (PAFs). Results In 2021, the global age-standardized SAH incidence was 8.3 (95% UI, 7.3-9.5), prevalence was 92.2 (95% UI, 84.1-100.6), mortality was 4.2 (95% UI, 3.7-4.8), and DALY rate was 125.2 (95% UI, 110.5-142.6) per 100 000 people. The highest burden estimates were found in Latin America, the Caribbean, Oceania, and high-income Asia Pacific. Although the absolute number of SAH cases increased, especially in regions with a low sociodemographic index, all age-standardized burden rates decreased between 1990 and 2021: the incidence by 28.8% (95% UI, 25.7%-31.6%), prevalence by 16.1% (95% UI, 14.8%-17.7%), mortality by 56.1% (95% UI, 40.7%-64.3%), and DALY rate by 54.6% (95% UI, 42.8%-61.9%). Of 300 diseases, SAH ranked as the 36th most common cause of death and 59th most common cause of DALY in the world. Of all worldwide SAH-related DALYs, 71.6% (95% UI, 63.8%-78.6%) were associated with the 14 modeled risk factors of which high systolic blood pressure (population attributable fraction [PAF] = 51.6%; 95% UI, 38.0%-62.6%) and smoking (PAF = 14.4%; 95% UI, 12.4%-16.5%) had the highest attribution. Conclusions and Relevance Although the global age-standardized burden rates of SAH more than halved over the last 3 decades, SAH remained one of the most common cardiovascular and neurological causes of death and disabilities in the world, with increasing absolute case numbers. These findings suggest evidence for the potential health benefits of proactive public health planning and resource allocation toward the prevention of SAH.
Streamlining Thoracic Radiotherapy Quality assurance: One-Class Classification for Automated OAR Contour Assessment
Zhao, Yihao , Yuan, Cuiyun, Liang, Ying , Li, Yang , Li, Chunxia , Zhao, Man, Hu, Jun , Zhong, Ningze , Liu, Wei , Liu, Chenbin , Liu, Wei

Technology in Cancer Research & Treatment | May 22, 2025 | Volume: 24 | Issue: No issue available | Pages: No page available

10.1177/15330338251345895 | PMID 40400421 Get It

Purpose Automating quality assurance (QA) for contours generated by automatic algorithms is critical in radiotherapy treatment planning. Manual QA is tedious, time-consuming, and prone to subjective experiences. Automatic segmentation reduces physician workload and improves consistency. However, an effective QA process for these automatic contours remains an unmet need in clinical practice. Materials and Methods The patient data used in this study was derived from the AAPM Thoracic Auto-Segmentation Challenge dataset, including left and right lungs, heart, esophagus, and spinal cord. Two groups of organ-at-risk (OAR) were generated. A ResNet-152 network was used as a feature extractor, and a one-class support vector machine (OC-SVM) was employed to classify contours as ‘high’ or ‘low’ quality. To evaluate the generalizability, we generated low-quality contours using translation and resizing techniques and assessed correlations between detection limits and metrics such as volume, Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and mean surface distance (MSD). Results The proposed OC-SVM model outperformed binary classifiers n metrics such as balanced accuracy and area under the receiver operating characteristic curve (AUC) . It demonstrated superior performance in detecting various types of contour errors while maintaining high interpretability. Strong correlations were observed between detection limits and contour metrics. Conclusion Our proposed model integrates an attention mechanism with a one-class classification framework to automate QA for OAR delineations. This approach effectively detects diverse types of contour errors with high accuracy, significantly reducing the burden on physicians during radiotherapy planning.
Novel Benchmark for Robotic Liver Resection – Bridging Tradition with Innovation
Pfister, Matthias , Ma, Yong , Yang, Guangchao, Li, Linqiang , Song, Xuan , Venezia, Raphael, Jeddou, Heithem , Floc’h, Bastien Le, Valverde, A. , Péru, N. , Pessaux, Patrick, Salloum, Chady , Blanc, P., Scatton, Olivier , Tzedakis, Stylianos , Fuks, David , Chaoui, Ismaël, Silva, Silvia Gomes da, Sobral, M., Centonze, Leonardo , Zhu, Zhu , Ratti, Francesca, Rompianesi, Gianluca , Shah, Jimmy, Wakabayashi, Taiga , Coelho, Fabrício Ferreira , Castillo‐Flores, Samy , Viti, Virginia, Boggi, Ugo , Han, Ho-Seong, Panaro, Fabrizio , Wakabayashi, Go, Ielpo, Benedetto , Mejia, Alejandro , Troisi, Roberto I., Choi, Gi Hong , Liu, Rong , Sucandy, Iswanto, Rela, Mohamed , D’Hondt, Mathieu , Bröering, Dieter C. , Clavien, Pierre‐Alain , Li, Zhihao , Dong, Yawen , Clavien, Pierre-Alain , Centonze, Leonardo , Li, Zhihao , Li, Zhihao, Raptis, Dimitri , Song, Xuan, Millet, Guillaume, Truant, Stéphanie , Venezia, Raphael, Pessaux, Patrick , Giannone, Fabio , Mabrut, Jean‐Yves , Mohkam, Kayvan , Cunha, António Sá , Roy, Bertrand Le , Goumard, Claire , Fuks, David , Rammohan, Ashwin , Silva, Silvia Gomes da, López‐López, Víctor , Ratti, Francesca, Marino, Rebecca , Müller, Philip C , Lee, Bo Ram , Kato, Yutaro , Polanco, Patricio Marcelo , Goh, Brian K P , Cheung, Tan To , Efanov, Mikhail G. , Machado, Marcel Cerqueira César , Herman, Paulo , Müller-Stich, Beat Peter , Starlinger, Patrick , Martinie, John B. , Aldrighetti, Luca , Chen, Guodong , Robles, R , Benedetto, Fabrizio Di , Marques, Hugo P. , Sucandy, Iswanto , Brustia, Raffaele , Soubrane, Olivier

Annals of Surgery | May 20, 2025 | Volume: No volume available | Issue: No issue available | Pages: No page available

10.1097/sla.0000000000006759 | PMID 40391433 Get It

Objective: To establish benchmark cutoffs for robotic liver resection (R-LR), encompassing both major and minor resections, and to determine the impact of patient selection on outcomes. Background: R-LR is a key advancement in minimally invasive liver surgery but lacks standardized benchmarks, especially for minor resections. While guidelines endorse R-LR, its role in optimizing outcomes remains unclear. This study establishes the first benchmarks for R-LR, enabling comparisons across surgical modalities and refining patient selection. Methods: This retrospective, multicenter study analyzed consecutive adult patients undergoing R-LR at 30 international centers (2020–2023). Benchmark centers had an annual case volume of ≥15 R-LR. Benchmark criteria included ASA ≤2, no major comorbidities, no prior liver resections, and Child-Pugh A status. Benchmark cutoffs for 14 key outcomes were set at the 50th and 75th percentiles of median values across benchmark centers. Multivariable logistic regression identified predictors of textbook outcomes. Results: Eighteen high-volume centers contributed 4028 cases with 2632 (65.3%) meeting benchmark criteria. Malignancy was the indication in 29.6%, most commonly hepatocellular carcinoma followed by colorectal liver metastases. Major liver resection was performed in 42.6%. The distribution of Iwate difficulty scores was low (25.4%), intermediate (53.9%), advanced (15.9%), and expert (4.9%). Benchmark cutoffs were established for minor and major resection, and stratified by Iwate low (0-3), intermediate (4-6) and high (7-12): Open conversion (≤6.5% minor, ≤10.5% major), major complications within 90 days (≤5.2% minor, ≤16.7% major), R1 resection (≤9.2% minor, ≤6.7% major). Benchmark cases performed at low-volume centers were able to achieve outcomes within the corresponding benchmark cutoffs. In fact, patient selection reflected by the proportion of benchmark patients, rather than case volume, was associated with textbook outcomes. Conclusions: This study defines R-LR benchmarks, emphasizing patient selection over center volume for optimal outcomes. Benchmark cutoffs guide training and support the expansion of R-LR.