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Jiang Q, Hu H, Liao J, Li ZH, Tan J. Development and validation of a nomogram for breast cancer-related lymphedema. Sci Rep 2024; 14:15602. [PMID: 38971880 PMCID: PMC11227568 DOI: 10.1038/s41598-024-66573-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Accepted: 07/02/2024] [Indexed: 07/08/2024] Open
Abstract
To establish and validate a predictive model for breast cancer-related lymphedema (BCRL) among Chinese patients to facilitate individualized risk assessment. We retrospectively analyzed data from breast cancer patients treated at a major single-center breast hospital in China. From 2020 to 2022, we identified risk factors for BCRL through logistic regression and developed and validated a nomogram using R software (version 4.1.2). Model validation was achieved through the application of receiver operating characteristic curve (ROC), a calibration plot, and decision curve analysis (DCA), with further evaluated by internal validation. Among 1485 patients analyzed, 360 developed lymphedema (24.2%). The nomogram incorporated body mass index, operative time, lymph node count, axillary dissection level, surgical site infection, and radiotherapy as predictors. The AUCs for training (N = 1038) and validation (N = 447) cohorts were 0.779 and 0.724, respectively, indicating good discriminative ability. Calibration and decision curve analysis confirmed the model's clinical utility. Our nomogram provides an accurate tool for predicting BCRL risk, with potential to enhance personalized management in breast cancer survivors. Further prospective validation across multiple centers is warranted.
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Affiliation(s)
- Qihua Jiang
- Department of Breast Surgery, Third Hospital of Nanchang, No. 2, Xiangshan South Road, Xi Hu District, Nanchang City, 330008, Jiangxi Province, China
| | - Hai Hu
- Department of General Surgery, Third Hospital of Nanchang, No. 2, Xiangshan South Road, Xi Hu District, Nanchang City, 330008, Jiangxi Province, China
| | - Jing Liao
- Department of Breast Surgery, Third Hospital of Nanchang, No. 2, Xiangshan South Road, Xi Hu District, Nanchang City, 330008, Jiangxi Province, China
| | - Zhi-Hua Li
- Department of Breast Surgery, Third Hospital of Nanchang, No. 2, Xiangshan South Road, Xi Hu District, Nanchang City, 330008, Jiangxi Province, China.
- Jiangxi Province Key Laboratory of Breast Diseases, Third Hospital of Nanchang, No. 2, Xiangshan South Road, Xihu District, Nanchang City, 330008, Jiangxi Province, China.
| | - Juntao Tan
- Department of Breast Surgery, Third Hospital of Nanchang, No. 2, Xiangshan South Road, Xi Hu District, Nanchang City, 330008, Jiangxi Province, China.
- Jiangxi Province Key Laboratory of Breast Diseases, Third Hospital of Nanchang, No. 2, Xiangshan South Road, Xihu District, Nanchang City, 330008, Jiangxi Province, China.
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Miaskowski C, Conley YP, Cooper BA, Paul SM, Smoot BJ, Hammer MJ, Fu M, Levine JD. Identification Of A Higher Risk Lymphedema Phenotype And Associations With Cytokine Gene Polymorphisms. J Pain Symptom Manage 2024; 67:375-383.e3. [PMID: 38307372 DOI: 10.1016/j.jpainsymman.2024.01.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 01/21/2024] [Accepted: 01/24/2024] [Indexed: 02/04/2024]
Abstract
CONTEXT Breast cancer-related lymphedema (BCRL) is chronic condition that occurs in 5% to 75% of women following treatment for breast cancer. However, little is known about the risk factors and mechanisms associated with a worse BCRL profile. OBJECTIVES Identify distinct BCRL profiles in women with the condition (i.e., lower vs. higher risk phenotype) and evaluate for associations with pro- and anti-inflammatory genes. METHODS Latent class profile analysis (LCPA) was used to identify the BCRL profiles using phenotypic characteristics evaluated prior to surgery. Candidate gene analyses were done to identify cytokine genes associated with the two BCRL profiles. RESULTS Of the 155 patients evaluated, 35.5% (n = 55) were in the Lower and 64.5% (n = 100) were in the Higher Risk classes. Risk factors for membership in the Higher class included: lower functional status, having sentinel lymph node biopsy, axillary lymph node dissection, mastectomy, higher number of positive lymph nodes, and receipt of chemotherapy. Polymorphisms for interleukin (IL)1-beta and IL6 were associated with membership in the Higher Risk class. CONCLUSION The readily available and clinically relevant phenotypic characteristics associated with a worse BCRL profile can be used by clinicians to identify higher risk patients. If confirmed, these characteristics can be tested in predictive risk models. In addition, the candidate gene findings may guide the development of mechanistically-based interventions to decrease the risk of BCRL.
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Affiliation(s)
- Christine Miaskowski
- School of Nursing, University of California, San Francisco, CA, USA; School of Medicine, University of California, San Francisco, CA, USA.
| | | | - Bruce A Cooper
- School of Nursing, University of California, San Francisco, CA, USA
| | - Steven M Paul
- School of Nursing, University of California, San Francisco, CA, USA
| | - Betty J Smoot
- School of Medicine, University of California, San Francisco, CA, USA
| | | | - Mei Fu
- School of Nursing and Health Studies, University of Missouri, Kansas City, MO, USA
| | - Jon D Levine
- School of Medicine, University of California, San Francisco, CA, USA
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Lawlor A, Lin C, Gómez Rivas J, Ibáñez L, Abad López P, Willemse PP, Imran Omar M, Remmers S, Cornford P, Rajwa P, Nicoletti R, Gandaglia G, Yuen-Chun Teoh J, Moreno Sierra J, Golozar A, Bjartell A, Evans-Axelsson S, N'Dow J, Zong J, Ribal MJ, Roobol MJ, Van Hemelrijck M, Beyer K. Predictive Models for Assessing Patients' Response to Treatment in Metastatic Prostate Cancer: A Systematic Review. EUR UROL SUPPL 2024; 63:126-135. [PMID: 38596781 PMCID: PMC11001619 DOI: 10.1016/j.euros.2024.03.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 02/05/2024] [Accepted: 03/20/2024] [Indexed: 04/11/2024] Open
Abstract
Background and objective The treatment landscape of metastatic prostate cancer (mPCa) has evolved significantly over the past two decades. Despite this, the optimal therapy for patients with mPCa has not been determined. This systematic review identifies available predictive models that assess mPCa patients' response to treatment. Methods We critically reviewed MEDLINE and CENTRAL in December 2022 according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses statement. Only quantitative studies in English were included with no time restrictions. The quality of the included studies was assessed using the PROBAST tool. Data were extracted following the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews criteria. Key findings and limitations The search identified 616 citations, of which 15 studies were included in our review. Nine of the included studies were validated internally or externally. Only one study had a low risk of bias and a low risk concerning applicability. Many studies failed to detail model performance adequately, resulting in a high risk of bias. Where reported, the models indicated good or excellent performance. Conclusions and clinical implications Most of the identified predictive models require additional evaluation and validation in properly designed studies before these can be implemented in clinical practice to assist with treatment decision-making for men with mPCa. Patient summary In this review, we evaluate studies that predict which treatments will work best for which metastatic prostate cancer patients. We found that existing studies need further improvement before these can be used by health care professionals.
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Affiliation(s)
- Ailbhe Lawlor
- Translational Oncology and Urology Research (TOUR), King’s College London, London, UK
| | - Carol Lin
- Department of Urology, Erasmus MC Cancer Institute, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Juan Gómez Rivas
- Department of Urology, Health Research Institute, Hospital Clinico San Carlos, Madrid, Spain
| | - Laura Ibáñez
- Department of Urology, Health Research Institute, Hospital Clinico San Carlos, Madrid, Spain
| | - Pablo Abad López
- Department of Urology, Hospital Universitario La Paz, Madrid, Spain
| | - Peter-Paul Willemse
- Department of Oncological Urology, University Medical Center, Utrecht Cancer Center, Utrecht, The Netherlands
| | | | - Sebastiaan Remmers
- Department of Urology, Erasmus MC Cancer Institute, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | | | - Pawel Rajwa
- Department of Urology, Medical University of Silesia, Zabrze, Poland
| | - Rossella Nicoletti
- Department of Experimental and Clinical Biomedical Science, University of Florence, Florence, Italy
- S.H. Ho Urology Centre, Department of Surgery, The Chinese University of Hong Kong, Hong Kong, China
| | - Giorgio Gandaglia
- Department of Urology and Division of Experimental Oncology, Urological Research Institute, IRCCS San Raffaele Hospital, Milan, Italy
- OHDSI Center, Northeastern University, Boston, MA, USA
| | - Jeremy Yuen-Chun Teoh
- S.H. Ho Urology Centre, Department of Surgery, The Chinese University of Hong Kong, Hong Kong, China
| | - Jesús Moreno Sierra
- Department of Urology, Health Research Institute, Hospital Clinico San Carlos, Madrid, Spain
| | - Asieh Golozar
- OHDSI Center, Northeastern University, Boston, MA, USA
- Odysseus Data Services, New York, NY, USA
| | - Anders Bjartell
- Department of Translational Medicine, Lund University, Malmö, Sweden
| | | | - James N'Dow
- European Association of Urology, Guidelines Office, Arnhem, The Netherlands
| | - Jihong Zong
- Bayer Healthcare, Global Medical Affairs Oncology, Whippany, NJ, USA
| | - Maria J. Ribal
- European Association of Urology, Guidelines Office, Arnhem, The Netherlands
| | - Monique J. Roobol
- Department of Urology, Erasmus MC Cancer Institute, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Mieke Van Hemelrijck
- Translational Oncology and Urology Research (TOUR), King’s College London, London, UK
| | - Katharina Beyer
- Department of Urology, Erasmus MC Cancer Institute, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - on behalf of the PIONEER Consortium
- Translational Oncology and Urology Research (TOUR), King’s College London, London, UK
- Department of Urology, Erasmus MC Cancer Institute, Erasmus University Medical Centre, Rotterdam, The Netherlands
- Department of Urology, Health Research Institute, Hospital Clinico San Carlos, Madrid, Spain
- Department of Urology, Hospital Universitario La Paz, Madrid, Spain
- Department of Oncological Urology, University Medical Center, Utrecht Cancer Center, Utrecht, The Netherlands
- Academic Urology Unit, University of Aberdeen, Aberdeen, UK
- Liverpool University Hospitals NHS Trust, Liverpool, UK
- Department of Urology, Medical University of Silesia, Zabrze, Poland
- Department of Experimental and Clinical Biomedical Science, University of Florence, Florence, Italy
- S.H. Ho Urology Centre, Department of Surgery, The Chinese University of Hong Kong, Hong Kong, China
- Department of Urology and Division of Experimental Oncology, Urological Research Institute, IRCCS San Raffaele Hospital, Milan, Italy
- OHDSI Center, Northeastern University, Boston, MA, USA
- Odysseus Data Services, New York, NY, USA
- Department of Translational Medicine, Lund University, Malmö, Sweden
- Bayer AB, Medical Affairs Oncology, Stockholm, Sweden
- European Association of Urology, Guidelines Office, Arnhem, The Netherlands
- Bayer Healthcare, Global Medical Affairs Oncology, Whippany, NJ, USA
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Rochlin DH, Barrio AV, McLaughlin S, Van Zee KJ, Woods JF, Dayan JH, Coriddi MR, McGrath LA, Bloomfield EA, Boe L, Mehrara BJ. Feasibility and Clinical Utility of Prediction Models for Breast Cancer-Related Lymphedema Incorporating Racial Differences in Disease Incidence. JAMA Surg 2023; 158:954-964. [PMID: 37436762 PMCID: PMC10339225 DOI: 10.1001/jamasurg.2023.2414] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 03/31/2023] [Indexed: 07/13/2023]
Abstract
Importance Breast cancer-related lymphedema (BCRL) is a common complication of axillary lymph node dissection (ALND) but can also develop after sentinel lymph node biopsy (SLNB). Several models have been developed to predict the risk of disease development before and after surgery; however, these models have shortcomings that include the omission of race, inclusion of variables that are not readily available to patients, low sensitivity or specificity, and lack of risk assessment for patients treated with SLNB. Objective To create simple and accurate prediction models for BCRL that can be used to estimate preoperative or postoperative risk. Design, Setting, and Participants In this prognostic study, women with breast cancer who underwent ALND or SLNB from 1999 to 2020 at Memorial Sloan Kettering Cancer Center and the Mayo Clinic were included. Data were analyzed from September to December 2022. Main Outcomes and Measures Diagnosis of lymphedema based on measurements. Two predictive models were formulated via logistic regression: a preoperative model (model 1) and a postoperative model (model 2). Model 1 was externally validated using a cohort of 34 438 patients with an International Classification of Diseases diagnosis of breast cancer. Results Of 1882 included patients, all were female, and the mean (SD) age was 55.6 (12.2) years; 80 patients (4.3%) were Asian, 190 (10.1%) were Black, 1558 (82.8%) were White, and 54 (2.9%) were another race (including American Indian and Alaska Native, other race, patient refused to disclose, or unknown). A total of 218 patients (11.6%) were diagnosed with BCRL at a mean (SD) follow-up of 3.9 (1.8) years. The BCRL rate was significantly higher among Black women (42 of 190 [22.1%]) compared with all other races (Asian, 10 of 80 [12.5%]; White, 158 of 1558 [10.1%]; other race, 8 of 54 [14.8%]; P < .001). Model 1 included age, weight, height, race, ALND/SLNB status, any radiation therapy, and any chemotherapy. Model 2 included age, weight, race, ALND/SLNB status, any chemotherapy, and patient-reported arm swelling. Accuracy was 73.0% for model 1 (sensitivity, 76.6%; specificity, 72.5%; area under the receiver operating characteristic curve [AUC], 0.78; 95% CI, 0.75-0.81) at a cutoff of 0.18, and accuracy was 81.1% for model 2 (sensitivity, 78.0%; specificity, 81.5%; AUC, 0.86; 95% CI, 0.83-0.88) at a cutoff of 0.10. Both models demonstrated high AUCs on external (model 1: 0.75; 95% CI, 0.74-0.76) or internal (model 2: 0.82; 95% CI, 0.79-0.85) validation. Conclusions and Relevance In this study, preoperative and postoperative prediction models for BCRL were highly accurate and clinically relevant tools comprised of accessible inputs and underscored the effects of racial differences on BCRL risk. The preoperative model identified high-risk patients who require close monitoring or preventative measures. The postoperative model can be used for screening of high-risk patients, thus decreasing the need for frequent clinic visits and arm volume measurements.
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Affiliation(s)
- Danielle H. Rochlin
- Plastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Andrea V. Barrio
- Breast Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Sarah McLaughlin
- Breast Clinic, Department of Surgery, Mayo Clinic, Jacksonville, Florida
| | - Kimberly J. Van Zee
- Breast Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Jack F. Woods
- Plastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Joseph H. Dayan
- Plastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Michelle R. Coriddi
- Plastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Leslie A. McGrath
- Plastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Emily A. Bloomfield
- Plastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Lillian Boe
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Babak J. Mehrara
- Plastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
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Shen A, Wei X, Zhu F, Sun M, Ke S, Qiang W, Lu Q. Risk prediction models for breast cancer-related lymphedema: A systematic review and meta-analysis. Eur J Oncol Nurs 2023; 64:102326. [PMID: 37137249 DOI: 10.1016/j.ejon.2023.102326] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 03/10/2023] [Accepted: 03/18/2023] [Indexed: 03/31/2023]
Abstract
PURPOSE To review and critically evaluate currently available risk prediction models for breast cancer-related lymphedema (BCRL). METHODS PubMed, Embase, CINAHL, Scopus, Web of Science, the Cochrane Library, CNKI, SinoMed, WangFang Data, VIP Database were searched from inception to April 1, 2022, and updated on November 8, 2022. Study selection, data extraction and quality assessment were conducted by two independent reviewers. The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias and applicability. Meta-analysis of AUC values of model external validations was performed using Stata 17.0. RESULTS Twenty-one studies were included, reporting twenty-two prediction models, with the AUC or C-index ranging from 0.601 to 0.965. Only two models were externally validated, with the pooled AUC of 0.70 (n = 3, 95%CI: 0.67 to 0.74), and 0.80 (n = 3, 95%CI: 0.75 to 0.86), respectively. Most models were developed using classical regression methods, with two studies using machine learning. Predictors most frequently used in included models were radiotherapy, body mass index before surgery, number of lymph nodes dissected, and chemotherapy. All studies were judged as high overall risk of bias and poorly reported. CONCLUSIONS Current models for predicting BCRL showed moderate to good predictive performance. However, all models were at high risk of bias and poorly reported, and their performance is probably optimistic. None of these models is suitable for recommendation in clinical practice. Future research should focus on validating, optimizing, or developing new models in well-designed and reported studies, following the methodology guidance and reporting guidelines.
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Blei F. Update December 2022. Lymphat Res Biol 2022; 20:671-694. [PMID: 36537708 DOI: 10.1089/lrb.2022.29133.fb] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Affiliation(s)
- Francine Blei
- Hemangioma and Vascular Malformations Program, Hassenfeld Children's Hospital at NYU Langone, Grossman School of Medicine, New York, New York, USA
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