Improving Survival Prognostication in Patients With Metastatic Cancer Through Clinical Judgment

Kao, Johnny; Zucker, Amanda; Urso, M.; Karwowski, Pawel; Jain, Neha; Jain, S.; Bustamante, G.; Lugo, Deborah; Rowland, Luann

doi:10.21873/anticanres.15609

Cited by 3 publications

(6 citation statements)

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“…The CNS holds promise in effectively delivering primary palliative and supportive care of patients with metastatic disease (53). Recent data from Good Samaritan University Hospital demonstrates that experienced oncology nurses are able to predict survival in patients with metastatic cancer at least as well as an attending radiation oncologist (54). This study highlights the complementary and often nonoverlapping strengths physicians and nurses in evaluating patients with advanced cancer.…”

Section: Towards a Comprehensive Survivorship Program After Radiation...mentioning

confidence: 90%

Personalized Palliative and Survivorship Care for Patients With Metastatic Cancer Treated With Radiation Therapy

et al. 2023

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While the benefits of early palliative care for patients with metastatic cancer are well established, cancer survivorship remains inadequately integrated into the care of patients with distant metastases. Moreover, the optimal model of care delivery is poorly defined. A prognostic model previously developed and validated at Good Samaritan University Hospital identified four groups of patients with metastatic solid tumor malignancy having very favorable, favorable, standard or unfavorable prognoses with median survival of 31, 14, 4 and 1 month, respectively. This framework holds promise for the personalized delivery of supportive, palliative and survivorship care services in the context of radiation therapy. We review the published literature providing the rationale for a novel multidisciplinary care model where the radiation oncology Clinical Nurse Specialist identifies and coordinates interventions to address unmet physical and emotional issues faced by survivors with metastatic cancer with the goal of improving quality of life and overall survival.

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Section: Towards a Comprehensive Survivorship Program After Radiation...mentioning

confidence: 90%

Personalized Palliative and Survivorship Care for Patients With Metastatic Cancer Treated With Radiation Therapy

et al. 2023

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“…The majority of studies focused on two or more tumour sites (n=8), [18][19][20][21][22][23][24][25] with a particular emphasis on breast (n=8) 18-20 23 24 26 27 and gastrointestinal cancer (n=8). 18-24 28 Other cancer types included prostate (n=5), 20 22 25 29 30 gynaecological (n=4) 20 21 23 24 as well as thyroid and head and neck (n=5).…”

Section: Tumour Type Location Type Of Studiesmentioning

confidence: 99%

“…22 23 The Number of active tumors ("N"), Eastern Cooperative Oncology Group performance status ("E"), albumin ("A") and primary tumor site ("T") (NEAT) model provided better accurate prognostic predictions which was statistically significant, compared with experienced oncology physicians and nurses. 22 However, its generalisability is limited due to it being single-site study. 22 On the contrary, the other study, Manz et al demonstrated the feasibility and clinical validation of a machine learning for real-time short-term prognosis of patients with cancer across 18 centres under a single academic health system.…”

Section: Survivalmentioning

confidence: 99%

“…22 However, its generalisability is limited due to it being single-site study. 22 On the contrary, the other study, Manz et al demonstrated the feasibility and clinical validation of a machine learning for real-time short-term prognosis of patients with cancer across 18 centres under a single academic health system. 23 Treatment Four studies assessed AI in radiotherapy and brachytherapy.…”

Section: Survivalmentioning

confidence: 99%

“…Half of the non-randomised studies of intervention were rated as having low overall risk of bias, meaning their risk is comparable to that of a well-conducted randomised trial. 22 28 30 32 The overall risk of bias of Manz et al was deemed serious due to missing data and subsequent exclusion of patients with missing data from analysis. 23 24 Hong et al was judged to have serious risk of bias, as the baseline characteristics of their cohorts were not adjusted to confounders, such as comorbidities, cancer diagnosis, age, gender and other relevant prognostic factors, which could significantly impact their outcomes, specifically emergency care attendance and hospital admission during cancer treatment.…”

Section: Reviewmentioning

confidence: 99%

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Prospective evaluation of artificial intelligence (AI) applications for use in cancer pathways following diagnosis: a systematic review

Macheka,

Ng,

Ginsburg

et al. 2024

bmjonc

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The role of artificial intelligence (AI) in cancer care has evolved in the face of ageing population, workforce shortages and technological advancement. Despite recent uptake in AI research and adoption, the extent to which it improves quality, efficiency and equity of care beyond cancer diagnostics is uncertain to date. Henceforth, the objective of our systematic review is to assess the clinical readiness and deployability of AI through evaluation of prospective studies of AI in cancer care following diagnosis.We undertook a systematic review to determine the types of AI involved and their respective outcomes. A PubMed and Web of Science search between 1 January 2013 and 1 May 2023 identified 15 articles detailing prospective evaluation of AI in postdiagnostic cancer pathway. We appraised all studies using Risk of Bias Assessment of Randomised Controlled Trials and Risk of Bias In Non-randomised Studies-of Interventions quality assessment tools, as well as implementational analysis concerning time, cost and resource, to ascertain the quality of clinical evidence and real-world feasibility of AI.The results revealed that the majority of AI oncological research remained experimental without prospective clinical validation or deployment. Most studies failed to establish clinical validity and to translate measured AI efficacy into beneficial clinical outcomes. AI research are limited by lack of research standardisation and health system interoperability. Furthermore, implementational analysis and equity considerations of AI were largely missing.To overcome the triad of low-level clinical evidence, efficacy-outcome gap and incompatible research ecosystem for AI, future work should focus on multicollaborative AI implementation research designed and conducted in accordance with up-to-date research standards and local health systems.

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Long-term disease-free survival following comprehensive involved site radiotherapy for oligometastases

Kao,

Sahagian,

Gupta

et al. 2023

Front. Oncol.

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IntroductionDespite recent advances in drug development, durable complete remissions with systemic therapy alone for metastatic cancers remain infrequent. With the development of advanced radiation technologies capable of selectively sparing normal tissues, patients with oligometastases are often amenable to comprehensive involved site radiotherapy with curative intent. This study reports the long-term outcomes and patterns of failure for patients treated with total metastatic ablation often in combination with systemic therapy.Materials and methodsConsecutive adult patients with oligometastases from solid tumor malignancy treated by a single high volume radiation oncologist between 2014 and 2021 were retrospectively analyzed. Oligometastases were defined as 5 or fewer metastatic lesions where all sites of active disease are amenable to local treatment. Comprehensive involved site radiotherapy consisted of stereotactic radiotherapy to a median dose of 27 Gy in 3 fractions and intensity modulated radiation therapy to a median dose of 50 Gy in 15 fractions. This study analyzed overall survival, progression-free survival, patterns of failure and toxicity.ResultsA total of 130 patients with 209 treated distant metastases were treated with a median follow-up of 36 months. The 4-year overall survival, progression-free survival, local control and distant control was 41%, 23%, 86% and 29%. Patterns of failure include 23% alive and free of disease (NED), 52% distant failure only, 9% NED but death from comorbid illness, 7% both local and distant failure, 4% NED but lost to follow-up, 4% referred to hospice before restaging, 1% local only failure, 1% alive with second primary cancer. Late grade 3+ toxicities occurred in 4% of patients, most commonly radionecrosis.ConclusionInvolved site radiotherapy to all areas of known disease can safely achieve durable complete remissions in patients with oligometastases treated in the real world setting. Distant failures account for the majority of treatment failures and isolated local failures are exceedingly uncommon. Oligometastases represents a promising setting to investigate novel therapeutics targeting minimal residual disease.

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Improving Survival Prognostication in Patients With Metastatic Cancer Through Clinical Judgment

Cited by 3 publications

References 0 publications

Personalized Palliative and Survivorship Care for Patients With Metastatic Cancer Treated With Radiation Therapy

Personalized Palliative and Survivorship Care for Patients With Metastatic Cancer Treated With Radiation Therapy

Prospective evaluation of artificial intelligence (AI) applications for use in cancer pathways following diagnosis: a systematic review

Long-term disease-free survival following comprehensive involved site radiotherapy for oligometastases

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