title: A practical decision-tree model to predict complexity of reconstructive surgery after periocular basal cell carcinoma excision.
abstract: BACKGROUND
  Periocular basal cell carcinomas (pBCC) have unpredictable growth. The authors seek to derive a decision rule for predicting surgical complexity in pBCC.
  
  
  METHODS
  This study was conducted at two centres in New Zealand from September 2010 to November 2015. Baseline demographic information and an initial assessment of operative complexity (a four-point grading scale) were collected. Assessment of operative complexity was repeated at the time of reconstruction. Univariate analysis was applied to identify the associative factors and supervised machine learning was used to determine the best predictive models to construct a clinical decision rule.
  
  
  RESULTS
  A total of 156 patients and 156 periocular BCC were analysed. Univariate analysis revealed that older age, recurrent skin cancer, large tumour size, being a public patient and high complexity at pre-operative assessment were associated with high actual operative complexity. Tumour histology was not associated with more complex surgery. Machine learning analyses revealed that Naive Bayesian classifier was able to distinguish surgical complexity with an average area under the receiver operating characteristic curve (AUC) of 0.854 (95% CI: 0.762-0.946) whereas a simpler, alternating decision tree (ADT) that used only three clinical variables achieved an AUC of 0.853 (95% CI: 0.739-0.931). The ADT model was 10.1 times more likely to correctly identify a high complexity case. The three predictive variables were pre-operative assessment of complexity (high vs. low), surgical delays [early (<75 days) or delayed (≥75 days)], and tumour size [small (<14 mm), or large (≥14 mm)]. For the subgroup with large tumours but low initial assessed complexity, late surgery was associated with a 6.7-fold increase in risk of high-risk surgery.
  
  
  CONCLUSIONS
  A simple, three-variable risk stratification system was able to predict the operative complexity of pBCC.
authors.affiliation: Western Skin Institute, St. Albans, Melbourne, Vic., Australia.
authors.forename: E
authors.initials: E
authors.lastname: Tan
authors.affiliation: The Kinghorn Cancer Centre & Garvan Institute for Medical Research, Darlinghurst, Sydney, NSW, Australia.|Department of Medical Oncology, St Vincent's Hospital, Darlinghurst, Sydney, NSW, Australia.
authors.forename: F
authors.initials: F
authors.lastname: Lin
authors.affiliation: Department of Ophthalmology, Waikato Hospital, Hamilton, New Zealand.
authors.forename: L
authors.initials: L
authors.lastname: Sheck
authors.affiliation: Dermatologic Surgery Unit, Skin Cancer Institute, Tauranga, Bay of Plenty, New Zealand.
authors.forename: P
authors.initials: P
authors.lastname: Salmon
authors.affiliation: Department of Ophthalmology, Waikato Hospital, Hamilton, New Zealand.
authors.forename: S
authors.initials: S
authors.lastname: Ng
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D002280:Basal Cell Carcinoma / Q000473:pathology / Q000601:surgery*; D003663:Decision Trees; D005142:Eyelid Neoplasms / Q000473:pathology / Q000601:surgery*; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D015580:Mohs Surgery / Q000009:adverse effects; D010347:Patient Care Planning; D011237:Predictive Value of Tests; D011446:Prospective Studies; D012878:Skin Neoplasms / Q000473:pathology / Q000601:surgery*; D016038:Skin Transplantation; D013524:Surgical Flaps; D000072836:Surgical Wound / Q000209:etiology / Q000601:surgery*; D047368:Tumor Burden; D058106:Wound Closure Techniques