Purpose: Rectal toxicity remains a major threat to quality of life of patients, who receive brachytherapy to the abdominal pelvic area. Estimating the risk of toxicity development is essential to maximize therapeutic benefit without impairing rectal function. This study aimed to abstract and evaluate studies, which have developed prediction models for rectal toxicity after brachytherapy (BT) in patients with pelvic cancers. Material and methods: To identify relevant studies since 1995, MEDLINE database was searched on August 31, 2021, using terms related to “pelvic cancers”, “brachytherapy”, “prediction models”, and “rectal toxicity”. Papers were excluded if model specifications were not reported. Risk of bias was assessed using prediction model risk of bias assessment tool. Results: Thirty models (n = 16 cervical cancer, n = 13 prostate cancer, and n = 1 rectal cancer), including 60 distinct predictors were published. Rectal toxicity varied significantly between studies (median, 25.4% for cervix, and median, 8.8% for prostate cancer). High-, low-, and pulsed-dose-rate BT were applied in 15 (50%), 13 (43%), and 1 (3%) studies, respectively. Most common predictors that retained in final models were age (n = 5, 17%), EBRT (n = 5, 17%), V100% rectum (BT) (n = 5, 17%), and dose at rectal point (n = 3, 10%). None of the studies were considered to be at low-risk of bias due to deficiencies in the analysis domain. Conclusions: Existing models have limited clinical application due to poor quality of methodology. The following key issues should be considered in future studies: 1) Measuring patient-reported outcomes to address underestimation of true frequencies of rectal toxicity events; 2) Giving higher priority to reliable dose-volume parameters; 3) Avoiding overfitting by considering an event per candidate predictor rate ≥ 20; 4) Calculating detailed performance measures.

Prediction models for brachytherapy-induced rectal toxicity in patients with locally advanced pelvic cancers: a systematic review / Tohidinezhad, F., Willems, Y., Berbee, M., Van Limbergen, E., Verhaegen, F., Dekker, A., Traverso, A.. - In: JOURNAL OF CONTEMPORARY BRACHYTHERAPY. - ISSN 1689-832X. - 14:4(2022), pp. 411-422. [10.5114/jcb.2022.119427]

Prediction models for brachytherapy-induced rectal toxicity in patients with locally advanced pelvic cancers: a systematic review

Traverso A.
Ultimo
2022-01-01

Abstract

Purpose: Rectal toxicity remains a major threat to quality of life of patients, who receive brachytherapy to the abdominal pelvic area. Estimating the risk of toxicity development is essential to maximize therapeutic benefit without impairing rectal function. This study aimed to abstract and evaluate studies, which have developed prediction models for rectal toxicity after brachytherapy (BT) in patients with pelvic cancers. Material and methods: To identify relevant studies since 1995, MEDLINE database was searched on August 31, 2021, using terms related to “pelvic cancers”, “brachytherapy”, “prediction models”, and “rectal toxicity”. Papers were excluded if model specifications were not reported. Risk of bias was assessed using prediction model risk of bias assessment tool. Results: Thirty models (n = 16 cervical cancer, n = 13 prostate cancer, and n = 1 rectal cancer), including 60 distinct predictors were published. Rectal toxicity varied significantly between studies (median, 25.4% for cervix, and median, 8.8% for prostate cancer). High-, low-, and pulsed-dose-rate BT were applied in 15 (50%), 13 (43%), and 1 (3%) studies, respectively. Most common predictors that retained in final models were age (n = 5, 17%), EBRT (n = 5, 17%), V100% rectum (BT) (n = 5, 17%), and dose at rectal point (n = 3, 10%). None of the studies were considered to be at low-risk of bias due to deficiencies in the analysis domain. Conclusions: Existing models have limited clinical application due to poor quality of methodology. The following key issues should be considered in future studies: 1) Measuring patient-reported outcomes to address underestimation of true frequencies of rectal toxicity events; 2) Giving higher priority to reliable dose-volume parameters; 3) Avoiding overfitting by considering an event per candidate predictor rate ≥ 20; 4) Calculating detailed performance measures.
2022
machine learning
prostatic neoplasms
radiation injuries
rectal neoplasms
uterine cervical neoplasms
File in questo prodotto:
File Dimensione Formato  
JCB_Art_47783-10.pdf

accesso aperto

Tipologia: PDF editoriale (versione pubblicata dall'editore)
Licenza: Creative commons
Dimensione 735.83 kB
Formato Adobe PDF
735.83 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11768/207496
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact