Background
In the United States, approximately 87,000 adolescent and young adults (AYAs; age 15–39 years) are diagnosed with cancer each year.1 The spectrum of cancers diagnosed in this age group is unique compared with the younger and older populations, with some cancer types, such as Hodgkin lymphoma (HL) and osteosarcoma, displaying distinct incidence peaks in this age group.2 Although worldwide, cancer is the most common disease-related cause of death for AYAs,3,4 the overall 5-year survival rate for AYAs with cancer has increased in recent decades to >85%,1 creating a large and growing cancer survivorship population. Despite this, AYA cancer patients and survivors remain an understudied and underserved population.
Patient-reported outcomes (PROs) are being strongly recommended by regulatory agencies as a superior method to capture patient experience metrics directly from the patient without the need for potentially biased, subjective interpretation.5,6 There is also an increasing appreciation for their value within the AYA cancer patient population.7 Health-related quality of life (HRQoL) is a specific, multidimensional type of PRO that includes the patient’s perception of the impact of their disease on their daily life and on their physical, mental, and social well-being. Previous studies have shown that both physical and psychosocial components of HRQoL are poorer in AYAs with cancer compared with their healthy peers8,9 due to the impact of a cancer diagnosis and treatment on physical functioning8; development of self-identity, autonomy, body image, and sexuality10; ability to form social relationships; worry regarding potential negative effects on education and employment11; and financial well-being.12 Furthermore, increased psychological distress has been noted in AYAs at the time of initial cancer diagnosis,13 and the need to assess HRQoL in AYA cancer patients and survivors with long-term follow-up has been highlighted.14 The overall landscape of HRQoL in this underserved patient population with regard to age at initial cancer diagnosis, gender, race/ethnicity, and tumor type has not been thoroughly investigated. Furthermore, physical and psychosocial HRQoL measures have been shown to predict overall survival (OS) in adult patients.15–18 Patient-reported HRQoL is also a significant prognostic factor within the clinical trial setting for adult cancers,19 suggesting that HRQoL may be of value in the AYA cancer population. However, data are lacking on how well HRQoL at time of cancer diagnosis can prognosticate long-term survival in AYA cancers.
Our goal was to characterize HRQoL at diagnosis among AYAs with cancer; identify the impact of tumor type, age at diagnosis, gender, and race/ethnicity on HRQoL; and determine the impact of HRQoL on OS. This study used a large cohort of AYA patients with cancer who completed the SF-12 PRO within 6 months of diagnosis. The SF-12 is a validated and effective general health PRO measure that is not disease- or health status–specific, consisting of 12 items derived from the longer SF-36 questionnaire.20,21 The SF-12 has been used in multiple studies in patients with cancer,22 and measures 8 domains of general HRQoL.
Patients and Methods
AYA Cancer Survivor Population
The AYA cancer survivor population was generated from a retrospective search of the MD Anderson Cancer Center’s Tumor Registry for those who were evaluated at the institution between 2000 and 2016 and diagnosed with a primary cancer during the AYA age range (15–39 years). As part of routine hospital registration practice for all patients evaluated at MD Anderson Cancer Center between 1999 and 2017, these AYAs completed a patient health intake questionnaire that included assessment of HRQoL using the validated Short-Form 12 Health Survey, Version 1 (SF-12v1) questionnaire.20 The population was restricted to those who completed the full questionnaire to enable calculation of the SF-12v1 summary scores within 6 months of cancer diagnosis. A total of 7,030 AYA patients with cancer were identified. Patients who did not complete the full questionnaire to enable calculation of the SF-12 summary scores (n=2,098) and those completing the questionnaire >6 months after cancer diagnosis (n=1,435) were removed from the analysis, generating the final population of 3,497 AYA patients. The median time from diagnosis to questionnaire completion was 1.1 months (IQR, 1.0 month). Nearly 80% of the population completed the questionnaire within 2 months of diagnosis (n=2,771). Data abstracted from the Tumor Registry included cancer diagnosis, date of diagnosis, date of birth, race/ethnicity, gender, vital status, and date of last follow-up. Patients with Asian, Native Hawaiian/Pacific Islander, American Indian/Alaska Native, or unknown race/ethnicity classification were grouped as “other.” Written informed consent was provided by all participants and the study was approved by MD Anderson’s Institutional Review Board.
SF-12 HRQoL Questionnaire and Scoring
The SF-12v1 questionnaire consists of 12 items that generate 2 composite summary scores: the physical component summary (PCS) and mental component summary (MCS).20 Scoring for the pain interference question of the SF-12 was slightly modified in the questionnaire, with responses on a scale of 0 to 10 instead of the SF-12 reported on a scale of 0 to 5. Therefore, the scores were adjusted to match the SF-12 scoring range. A norm-based scoring system was used in which the MCS and PCS scores were normalized to a mean [SD] score of 50 [10] based on SF-12 data obtained from the US general population. Higher PCS and MCS scores represent better HRQoL. Additionally, a high MCS or PCS (≥50) is indicative of a better HRQoL compared with the general population, whereas a low MCS or PCS (<50) is indicative of a poor HRQoL compared with the general population. The recall period was 4 weeks.
Statistical Methods
Violin plots were used to visualize the distribution and probability density of PCS and MCS scores by tumor type, age at diagnosis, gender, and race/ethnicity. Differences in mean MCS and PCS scores by these characteristics were assessed using independent samples t tests or analysis of variance (ANOVA) for binomial or multinomial independent variables, respectively. Survival was defined as date of diagnosis to date of death or last follow-up, with ascertainment of follow-up through 2022. Survival estimates were calculated by Kaplan-Meier method with corresponding log-rank P values. An overall multivariable Cox proportional hazards model was created to identify independent risk factors associated with OS, and included PCS, MCS, gender, race/ethnicity, age at diagnosis, tumor type, and year of diagnosis.
Results
Study Population
A total of 3,497 AYA patients with cancer were included in the analysis (Table 1). The distribution of patient variables for those included in the analysis compared with those excluded (n=3,533) were comparable, with only slight increases in White patients and those with more favorable diagnoses (eg, breast cancer and HL) in the final patient population compared with those who were removed during the selection process. The median age at diagnosis across the AYA age range was 32 years (IQR, 10 years). Breast cancer was the most frequent diagnosis (20.4%), followed by sarcoma (12.8%) and leukemia (10.5%). Our patient population was diverse, including 35.7% of participants who self-identified as Black, Hispanic, or other. The median follow-up time was 9.2 years (IQR, 11.7 years), with 1,402 deaths recorded during this period.
Baseline Characteristics
PCS and MCS Score Distribution and OS
The distributions of PCS and MCS scores and impact on OS are shown in Figure 1. PCS scores in the overall cohort ranged from 11.3 to 70 with a mean [SD] of 43.6 [11.9] (Figure 1A). The mean [SD] MCS score was slightly higher at 46.7 [10.7] (range, 10–69; Figure 1C). AYA cancer survivors with poor PCS (<50) at time of cancer diagnosis had lower OS durations than their counterparts with PCS ≥50 (log-rank P<.001), resulting in a >20% reduction in the 5-year survival rate from 78.9% to 56.0% (Figure 1B). Poor PCS was associated with increased risk of death (hazard ratio [HR], 1.95; 95% CI, 1.72–2.21; P<.001). Poor MCS (<50) at diagnosis compared with MCS ≥50 was associated with decreased survival (log-rank P<.001) (Figure 1D) and increased risk of death (HR, 1.26; 95% CI, 1.13–1.40; P<.001).
(A) Distribution of PCS scores, (B) survival curves stratified by PCS ±50, (C) distribution of MCS scores, and (D) survival curves stratified by MCS ±50 in AYA patients with cancer.
Abbreviations: AYA, adolescent and young adult; MCS, mental component summary; PCS, physical component summary.
Citation: Journal of the National Comprehensive Cancer Network 22, 6; 10.6004/jnccn.2024.7012
PCS and MCS by Tumor Type
Mean PCS (P<.001) and MCS (P<.001) scores varied by tumor type (Table 1). Patients with breast cancer reported the most favorable PCS (50.8) at diagnosis, yet among the lowest MCS (46.3). The distributions of PCS and MCS scores were unique for each tumor type (Figure S1A in the supplementary material, available online with this article). Apart from breast cancer, all tumor types had median PCS scores <50, and the proportions of PCS scores <40 were pronounced for cervical cancer, colorectal cancer, leukemia, and sarcoma. Combined, patients with solid tumors had more favorable PCS compared with those diagnosed with heme malignancies (P<.001). There was less variation in MCS by tumor type compared with PCS scores. The distributions of MCS scores tended to be more unimodal across all 10 tumor types analyzed, with only patients with cervical cancer reporting a mean MCS score of <45 (Table 1).
OS by tumor type is shown in Figure 2A. Patients diagnosed with central nervous system (CNS) tumors, colorectal cancer, leukemia, sarcoma, and the other tumors group had 5-year survival rates <60%. When stratified by tumor type, poor PCS at diagnosis was associated with inferior survival for patients with breast cancer, CNS tumors, colorectal cancer, HL, leukemia, sarcoma, and other tumors (Figure 2B). Poor MCS was associated with lower survival in patients with HL (log-rank P=.0093), germ cell tumors (log-rank P=.038), and other tumor types (log-rank P=.002; Figure 2C).
(A) Overall survival by tumor type, and survival curves stratified by (B) PCS ±50 and (C) MCS ±50 in AYA patients.
Solid line: PCS/MCS ≥50; dotted line: PCS/MCS <50.
Abbreviations: AYA, adolescent and young adult; CNS, central nervous system; CRC, colorectal cancer; GCT, germ cell tumor; HL, Hodgkin lymphoma; MCS, mental component summary; NHL, non-Hodgkin lymphoma; PCS, physical component summary.
Citation: Journal of the National Comprehensive Cancer Network 22, 6; 10.6004/jnccn.2024.7012
PCS and MCS by Race/Ethnicity
Marginalized racial and ethnic AYA patients with cancer had significantly worse PCS than White AYA patients with cancer (P=.0023). Black AYA patients with cancer reported the lowest PCS at diagnosis, with 67.8% reporting PCS <50 compared with 60.9% of Hispanic and 58.0% of White patients. No significant differences were observed for MCS by race/ethnicity (Table 1) and the distributions were similar across groups (Supplementary Figure S1B).
Black patients had a 5-year survival rate of 53.2% that was nearly 10% lower than White and Hispanic patients (64% for each; Figure 3A). Survival differed by race/ethnicity (log-rank P<.001). When stratified by race/ethnicity, a significant reduction in survival by poor PCS was observed for Black (log-rank P=.0026), Hispanic (log-rank P<.001), White (log-rank P<.001), and other race/ethnicity (log-rank P=.0021) (Figure 3B). MCS did not differ by race/ethnicity (P=.26; Table 1), yet the effect of poor MCS on shorter survival was observed in the Hispanic (log-rank P=.024) and White (log-rank P=.0019) AYA cancer patient populations (Figure 3C).
(A) Overall survival by race/ethnicity, and survival curves stratified by (B) PCS ±50 and (C) MCS ±50 in AYA patients.
Solid line: PCS/MCS ≥50; dotted line: PCS/MCS <50.
Abbreviations: AYA, adolescent and young adult; MCS, mental component summary; PCS, physical component summary.
Citation: Journal of the National Comprehensive Cancer Network 22, 6; 10.6004/jnccn.2024.7012
PCS and MCS by Age at Diagnosis
Older age at diagnosis was associated with a higher mean PCS; however, a lower mean MCS (P<.001 for each; Table 1). The distribution of PCS and MCS scores stratified by diagnosis age categories show similar features (Supplementary Figure S1C)—a greater proportion of poor PCS scores was observed for adolescent patients (age 15–18 years) and lower MCS scores for young adult patients (age 26–39 years). Survival also differed by age category, with adolescents having a higher 5-year survival rate compared with both emerging adults (age 19–25 years) and young adults (Supplementary Figure S2A). When stratified by age categories, PCS was a significant prognostic feature for all 3 age categories (Supplementary Figure S2B), yet MCS was prognostic for only emerging and young adults (Supplementary Figure S2C).
PCS and MCS by Gender
Female AYA survivors had significantly higher PCS, yet lower MCS compared with male AYA patients (both P<.001; Table 1, Supplementary Figure S1D). Males had an increased risk of death compared with females (log-rank P=.0031; Supplementary Figure S3A). The negative effect of poor PCS on survival was evident in both male and female AYA survivors (both log-rank P<.0001; Supplementary Figure S3B). MCS was also associated with survival in males (log-rank P<.0006) and females (log-rank P=.0051; Supplementary Figure S3C).
HRQoL as a Predictor of OS
Low PCS (HR, 1.91; 95% CI, 1.69–2.17; P<.001) and low MCS (HR, 1.18; 95% CI, 1.06–1.32; P<.001) at diagnosis were independent predictors of diminished OS in multivariable Cox proportional hazards regression model that included gender, diagnosis age, race/ethnicity, tumor type, and year of diagnosis (Table 2).
Multivariable Cox Proportional Hazards Model for Overall Survival
Discussion
In this large, diverse cohort of AYA patients with cancer, HRQoL at diagnosis was impacted by tumor type, age at diagnosis, race/ethnicity, and gender. Low physical (PCS) and mental (MCS) HRQoL at diagnosis were independent predictors of diminished survival among AYAs with cancer. To the best of our knowledge, this study is the first to show that patient-reported HRQoL at diagnosis is predictive of lower OS in AYAs with cancer.
The present study identified distinctive distributions of PCS and MCS scores at diagnosis across the most common tumor types diagnosed in the AYA population, such as CNS tumors, HL, non-Hodgkin lymphoma, leukemia, breast cancer, and cervical cancers, for which data on PCS and MCS in AYAs are often lacking. Our physical HRQoL results across individual tumor types extends findings by Smith et al,8 which found poorer PCS in AYA patients with sarcoma compared with germ cell tumors. However, our finding of differences in physical HRQoL by tumor type contrasts with results from Siembida et al,23 which showed that physical HRQoL did not differ by whether patients had hematologic or solid tumors in their sample of 572 AYAs. The finding of an association between poor PCS at diagnosis and reduced OS in AYA patients, particularly those with sarcoma, colorectal cancer, CNS tumors, breast cancer, HL, and leukemia, could potentially guide the identification of those in greatest need of supportive interventions to improve overall health and well-being long-term.
Age at diagnosis was an important factor modulating the variation observed in PCS and MCS. The finding that younger AYAs (adolescents and emerging adults) with cancer had worse PCS than older AYAs (young adults) may be due to younger AYAs potentially having more aggressive tumors than older AYAs. For example, in a study of breast cancer, Murphy et al24 found that AYAs aged 15 to 29 years were more likely to have more advanced stages of disease and HER2-positive or triple-negative breast cancer than AYAs aged 30 to 39 years. However, in the analysis by Siembida et al,23 older, off-treatment AYA patients had a higher burden of poor HRQoL than patients aged 15 to 17 years. The superior MCS scores in younger AYAs compared with older AYAs in the present study may be explained, in part, by younger AYAs having less financial toxicity than older AYAs,25 or perhaps by younger AYAs still enjoying greater active parental support and still living with their parents, whereas older AYAs may also experience greater psychosocial pressures from less tangible family support and having to live independently, possibly starting a career, and perhaps bearing greater worry/responsibility regarding their own future and the future of their possibly young families/children.
Few studies have reported on the relationship between race/ethnicity and HRQoL in the AYA cancer patient population. Keegan et al26 previously reported that AYA patients from marginalized racial and ethnic groups are more likely to be diagnosed with cancer at a later stage of disease than non-Hispanic White AYAs. In an analysis of HRQoL in adult patients with colorectal cancer from MD Anderson, the relationship between poor HRQoL and survival was evident in patients with stage III and IV disease, while attenuated for those with stages I and II at diagnosis.15 Data on stage at diagnosis were not available for this analysis to identify whether this was a factor in our race/ethnicity and poor HRQoL relationship. The comparatively lower survival rates of Black AYA survivors in this study were striking and follow survival disparities documented in previous cohort studies.27,28
The association between poor mental HRQoL and female gender parallels a similar finding of greater distress in female AYAs with cancer compared with their male counterparts.29 Poor HRQoL in female patients with cancer has also been observed in a previous analysis of adult patients with colorectal cancer.15 In a survey of the US general population, females had a higher percentage of individuals who reported fair and poor health compared with men.30 This suggests the possibility that the decreased MCS observed in female AYA patients with cancer is not specific to the AYA age group, yet that female gender is a more universal risk factor for poor mental well-being following a cancer diagnosis.
Our finding that patient-reported poor physical functioning (PCS) at diagnosis is predictive of lower survival in AYA cancer survivors extends previous findings. Previously, our group and others have shown that physical HRQoL is predictive of OS in adults for multiple tumor types,15–17 in adult patients in randomized controlled trials,19 and in patients receiving anticancer treatment in phase III clinical trials.31 The mechanism(s) for this relationship between poor physical functioning and OS remain unclear, although it has been hypothesized to be due to cancer aggressiveness or to possibly serve as a surrogate marker for undefined underlying biologic dysfunction.32
The prognostic value of PROs is of interest because it is a cost-effective alternative when compared with laboratory-based markers of prognosis. It also facilitates access to specific potential interventions (ie, targeting physical functioning and/or mental health) that could improve the patient’s well-being and thus the possibility of altering patients’ long-term prognoses. With the increasing interest in PROs, studies focusing on potential interventions to improve outcomes are emerging. Kent et al33 used PROs related to pain and pain-related interference with daily activities to determine patient eligibility for a randomized intervention trial. Intervention included cognitive functional therapy with or without wearable-generated movement sensor biofeedback. This intervention produced significant and durable improvements in patient-reported activity limitations and pain scores among this low HRQoL population. In a phase II randomized, controlled trial, fatigue scores from a symptom assessment scale were used as an eligibility criterion to identify participants with cancer-related fatigue who were subsequently randomized to a successful multimodal intervention of physical activity and dexamethasone aimed at alleviating cancer-related fatigue.34 Several physical activity interventions utilizing varying characteristics, including individualized physical activity prescriptions, supervised structure, goal setting, and wearable devices, have been reported to improve HRQoL, anxiety, and depression among AYA cancer survivors.35
Potential limitations include that this study encompasses a single, albeit large, tertiary comprehensive cancer center, making it unclear whether these findings are generalizable to other caner care settings, such as community practices. HRQoL was assessed within 6 months of diagnosis, with a median time to questionnaire completion of 1.1 months. This approach was taken to minimize the impact of treatment on HRQoL, but a portion of the participants may have been in active treatment at the time of assessment, potentially inflating the levels of “baseline” HRQoL. Availability of specific treatment and stage information would have been informative in our analyses of HRQoL and OS. The lack of information on how HRQoL changed longitudinally between baseline and the first 2 years of survival is a potential limitation as well, given that a study by Husson et al9 has shown that HRQoL can improve significantly in AYAs between the time of initial cancer diagnosis and 1 year postdiagnosis.
Conclusions
Our findings suggest that patient-reported HRQoL in AYAs diagnosed with cancer varies by cancer type and patient demographics, and that poor HRQoL at diagnosis can be used as a potential biomarker of poor prognosis. Furthermore, our study highlights the importance of collecting HRQoL among AYAs at diagnosis, so that they can be offered appropriate interventions to improve their HRQoL and physical and psychological functioning. Further studies are needed to identify these targeted interventions to improve outcomes for AYAs with cancer.
References
- 1.↑
Howlader N, Noone AM, Krapcho M, et al. SEER cancer statistics review, 1975–2018. Accessed April 1, 2024. Available at: https://seer.cancer.gov/archive/csr/1975_2018/index.html
- 2.↑
Bleyer A, Barr R, Hayes-Lattin B, et al. The distinctive biology of cancer in adolescents and young adults. Nat Rev Cancer 2008;8:288–298.
- 3.↑
Barr RD, Ferrari A, Ries L, et al. Cancer in adolescents and young adults: a narrative review of the current status and a view of the future. JAMA Pediatr 2016;170:495–501.
- 4.↑
Sender L, Zabokrtsky KB. Adolescent and young adult patients with cancer: a milieu of unique features. Nat Rev Clin Oncol 2015;12:465–480.
- 5.↑
US Food and Drug Administration. Core patient-reported outcomes in cancer clinical trials: guidance for industry. Accessed April 1, 2024. Available at: https://www.fda.gov/media/149994/download
- 6.↑
Taylor VR. Measuring healthy days: population assessment of health-related quality of life. Accessed April 1, 2024. Available at: https://stacks.cdc.gov/view/cdc/6406
- 7.↑
Roth ME, Parsons SK, Ganz PA, et al. Inclusion of a core patient-reported outcomes battery in adolescent and young adult cancer clinical trials. J Natl Cancer Inst 2023;115:21–28.
- 8.↑
Smith AW, Bellizzi KM, Keegan TH, et al. Health-related quality of life of adolescent and young adult patients with cancer in the United States: the Adolescent and Young Adult Health Outcomes and Patient Experience study. J Clin Oncol 2013;31:2136–2145.
- 9.↑
Husson O, Zebrack BJ, Block R, et al. Health-related quality of life in adolescent and young adult patients with cancer: a longitudinal study. J Clin Oncol 2017;35:652–659.
- 10.↑
Zebrack BJ, Isaacson S. Psychosocial care of adolescent and young adult patients with cancer and survivors. J Clin Oncol 2012;30:1221–1226.
- 11.↑
Bradford NK, Chan RJ. Health promotion and psychological interventions for adolescent and young adult cancer survivors: a systematic literature review. Cancer Treat Rev 2017;55:57–70.
- 12.↑
Lu AD, Zheng Z, Han X, et al. Medical financial hardship in survivors of adolescent and young adult cancer in the United States. J Natl Cancer Inst 2021;113:997–1004.
- 13.↑
Kwak M, Zebrack BJ, Meeske KA, et al. Trajectories of psychological distress in adolescent and young adult patients with cancer: a 1-year longitudinal study. J Clin Oncol 2013;31:2160–2166.
- 14.↑
Husson O, Reeve BB, Darlington AS, et al. Next step for global adolescent and young adult oncology: a core patient-centered outcome set. J Natl Cancer Inst 2022;114:496–502.
- 15.↑
Reyes ME, Ye Y, Zhou Y, et al. Predictors of health-related quality of life and association with survival may identify colorectal cancer patients at high risk of poor prognosis. Qual Life Res 2017;26:319–330.
- 16.↑
Pierzynski JA, Ye Y, Lippman SM, et al. Socio-demographic, clinical, and genetic determinants of quality of life in lung cancer patients. Sci Rep 2018;8:10640.
- 17.↑
van Kleef JJ, Dijksterhuis WPM, van den Boorn HG, et al. Prognostic value of patient-reported quality of life for survival in oesophagogastric cancer: analysis from the population-based POCOP study. Gastric Cancer 2021;24:1203–1212.
- 18.↑
Peipert JD, Efficace F, Pierson R, et al. Patient-reported outcomes predict overall survival in older patients with acute myeloid leukemia. J Geriatr Oncol 2021;13:935–939.
- 19.↑
Mierzynska J, Piccinin C, Pe M, et al. Prognostic value of patient-reported outcomes from international randomised clinical trials on cancer: a systematic review. Lancet Oncol 2019;20:e685–698.
- 20.↑
Ware J Jr, Kosinski M, Keller SD. A 12-item short-form health survey: construction of scales and preliminary tests of reliability and validity. Med Care 1996;34:220–233.
- 21.↑
Ware J Jr, Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med Care 1992;30:473–483.
- 22.↑
Bhandari NR, Kathe N, Hayes C, et al. Reliability and validity of SF-12v2 among adults with self-reported cancer. Res Social Adm Pharm 2018;14:1080–1084.
- 23.↑
Siembida EJ, Reeve BB, Zebrack BJ, et al. Measuring health-related quality of life in adolescent and young adult cancer survivors with the National Institutes of Health Patient-Reported Outcomes Measurement Information System: comparing adolescent, emerging adult, and young adult survivor perspectives. Psychooncology 2021;30:303–311.
- 24.↑
Murphy BL, Day CN, Hoskin TL, et al. Adolescents and young adults with breast cancer have more aggressive disease and treatment than patients in their forties. Ann Surg Oncol 2019;26:3920–3930.
- 25.↑
Kaddas HK, Pannier ST, Mann K, et al. Age-related differences in financial toxicity and unmet resource needs among adolescent and young adult cancer patients. J Adolesc Young Adult Oncol 2020;9:105–110.
- 26.↑
Keegan THM, Parsons HM, Chen Y, et al. Impact of health insurance on stage at cancer diagnosis among adolescents and young adults. J Natl Cancer Inst 2019;111:1152–1160.
- 27.↑
Murphy CC, Lupo PJ, Roth ME, et al. Disparities in cancer survival among adolescents and young adults: a population-based study of 88 000 patients. J Natl Cancer Inst 2021;113:1074–1083.
- 28.↑
Berkman AM, Andersen CR, Puthenpura V, et al. Impact of race, ethnicity, and socioeconomic status over time on the long-term survival of adolescent and young adult Hodgkin lymphoma survivors. Cancer Epidemiol Biomarkers Prev 2021;30:1717–1725.
- 29.↑
Miroshnychenko A, Rae C, Tsangaris E, et al. Clinical and demographic factors associated with distress in adolescent and young adults with cancer. J Adolesc Young Adult Oncol 2021;10:682–689.
- 30.↑
Zahran HS, Kobau R, Moriarty DG, et al. Health-related quality of life surveillance--United States, 1993–2002. MMWR Surveill Summ 2005;54:1–35.
- 31.↑
Modi ND, Danell NO, Perry RNA, et al. Patient-reported outcomes predict survival and adverse events following anticancer treatment initiation in advanced HER2-positive breast cancer. ESMO Open 2022;7:100475.
- 32.↑
Montazeri A. Quality of life data as prognostic indicators of survival in cancer patients: an overview of the literature from 1982 to 2008. Health Qual Life Outcomes 2009;7:102.
- 33.↑
Kent P, Haines T, O’Sullivan P, et al. Cognitive functional therapy with or without movement sensor biofeedback versus usual care for chronic, disabling low back pain (RESTORE): a randomised, controlled, three-arm, parallel group, phase 3, clinical trial. Lancet 2023;401:1866–1877.
- 34.↑
Yennurajalingam S, Valero V, Lu Z, et al. Combination therapy of physical activity and dexamethasone for cancer-related fatigue: a phase II randomized double-blind controlled trial. J Natl Compr Canc Netw 2021;20:235–243.
- 35.↑
Hoover RL, Xu J, Conklin JL, et al. Physical activity intervention characteristics and effects on behavioral and health-related outcomes among adolescents and young adults living with and beyond cancer: a systematic review. J Adolesc Young Adult Oncol 2024;13:55–79.
