Integrated prediction model of patient factors, resectability scores and surgical complexity to predict cytoreductive outcome and guide treatment plan in advanced ovarian cancer

Piedimonte, Sabrina; Bernardini, Marcus Q.; Ding, Avrilynn; Laframboise, Stéphane; Ferguson, Sarah E.; Bouchard-Fortier, Geneviève; Cybulska, Paulina; Avery, Lisa; May, Taymaa; Hogen, Liat

doi:10.1016/j.ygyno.2022.06.026

Cited by 5 publications

(7 citation statements)

References 34 publications

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“…Several objective tools have previously been developed and validated but have focused on one or two elements of patient care. Our recently published Integrated Prediction Model algorithm found that triaging patients with stage IVb, patient factors >2, surgical resectability score >5, and surgical complexity score >9 to neoadjuvant chemotherapy had a high sensitivity and specificity for optimal cytoreduction in primary surgery patients 6…”

Section: Discussionmentioning

confidence: 99%

“…These were used to guide decisions on primary cytoreductive surgery versus neoadjuvant chemotherapy within a multidisciplinary cancer conference and documented in a database. Based on this, we previously developed the Integrated Prediction Model algorithm, which was designed to select patients for primary surgery versus neoadjuvant chemotherapy and not intended to select patients for interval cytoreductive surgery 6. The model was created using a sequential clinical flow of the following parameters:…”

Section: Methodsmentioning

confidence: 99%

“…However, patients in this cohort did not have imaging reviewed during ovarian rounds. We used the standardized radiologic reporting document devised in our previous Integrated Prediction Model development study, which was based on a modified surgical resectability score published by Suidan et al to note the location of disease from the expert radiology reports 1 6. The radiology reports were retrospectively reviewed by one of the authors, and sites of disease were documented using the standardized radiologic reporting document (SP.…”

Section: Methodsmentioning

confidence: 99%

“…Our group recently developed the Integrated Prediction Model, which is a four-step algorithm combining the following factors: unresectable stage IVB, patient factors, surgical resectability score, and surgical complexity score, to predict optimal cytoreduction in advanced ovarian cancer based on patient factors, surgical resectability, and surgical complexity 6. We found that a model triaging patients with unresectable stage IVB, patient factors >2, surgical resectability score >5, and surgical complexity score >9 to neoadjuvant chemotherapy had an 85% sensitivity, 75% specificity, and 85% accuracy for outcome of optimal <1 cm cytoreduction in primary surgery patients.…”

Section: Introductionmentioning

confidence: 99%

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Validation of the Integrated Prediction Model algorithm for outcome of cytoreduction in advanced ovarian cancer

Piedimonte

Bernardini

Ding

et al. 2023

Int J Gynecol Cancer

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BackgroundWe previously developed the Integrated Prediction Model using a 4-step algorithm of unresectable stage IVB, patient factors, surgical resectability, and surgical complexity to predict outcome of <1 cm cytoreduction in advanced epithelial ovarian cancer, and triaged patients to neoadjuvant chemotherapy or primary cytoreductive surgery.ObjectiveTo validate the Integrated Prediction Model on a retrospective cohort of patients.MethodsA retrospective cohort study of 107 patients with advanced ovarian cancer treated between January 2017 and September 2018 was carried out. The above mentioned 4-step algorithm determined cut-off points using the Youden Index. This validation study reports sensitivity, specificity, negative and positive predictive value on an external cohort.ResultsAmong 107 patients, 61 had primary surgery and 46 had neoadjuvant chemotherapy. Compared with primary surgery, patients treated with neoadjuvant chemotherapy were significantly older (63.5 vs 61, p=0.037), more likely to have stage IV disease (52% vs 18%, p<0.001), Eastern Cooperative Oncology Group (ECOG) score >1 (30% vs 11%, 0.045), lower pre-operative albumin (37 vs 40, p<0.001), and higher CA-125 (970 vs 227.5, p<0.001). They also had higher patient factors (2 vs 0, p=0.013), surgical resectability (4 vs 1, p<0.001), and anticipated surgical complexity (8 vs 5, p<0.001). There was no significant difference in outcome of cytoreduction (<1 cm residual disease: 85% for primary surgery vs 87% interval surgery, p=0.12)In this validation cohort, triaging patients with patient factors ≤2, surgical resectability score ≤5, and surgical complexity score ≤9 to primary surgery had a sensitivity of 91% for optimal cytoreduction <1 cm and a specificity of 81%. The positive predictive value, negative predictive value, and accuracy were 83%, 90%, and 86%, respectively. Application of the Integrated Prediction Model would have prevented five patients from receiving suboptimal cytoreduction and triaged them to neoadjuvant chemotherapy.ConclusionsWe validated the proposal that a triage algorithm integrating patient factors, surgical complexity, and surgical resectability in advanced ovarian cancer had high sensitivity and specificity to predict optimal cytoreduction <1 cm.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Validation of the Integrated Prediction Model algorithm for outcome of cytoreduction in advanced ovarian cancer

Piedimonte

Bernardini

Ding

et al. 2023

Int J Gynecol Cancer

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“…If millimetric RD or significant surgery comorbidities are predicted after PDS, NACT-IDS may be superior due to similar PFS, less complex surgery, and fewer perioperative complications [30,40]. A few preoperative and intraoperative predictive models for complete or optimal cytoreduction have been developed for advanced ovarian cancer, including clinical factors, CA125 levels, radiological examination, laparoscopy, and laparotomy-based evaluation [29,[41][42][43][44][45]. The valid tumor tissue-based biomarkers predicting postoperative RD have not been identified [46].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of serum CA125-Tn glycoform in peritoneal dissemination and surgical completeness of high-grade serous ovarian cancer

Jin

Wang

et al. 2022

J Ovarian Res

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Background Peritoneal dissemination is the predominant feature of malignant progression in ovarian cancer and is a major cause of poor surgical outcomes and clinical prognoses. Abnormal glycosylation of carbohydrate antigen 125 (CA125) may be involved in peritoneal implantation and metastasis. Here, we evaluated the clinical relevance of CA125-Tn glycoform in the assessment of high-grade serous ovarian cancer (HGSOC). Methods A total of 72 patients diagnosed with HGSOC were included. Pre-treatment serum CA125-Tn levels were measured using an antibody-lectin enzyme-linked immunosorbent assay. The association of CA125-Tn with clinical factors was analyzed in all cases, whereas its association with peritoneal dissemination, residual disease, and progression-free survival was analyzed in stage III–IV cases. Results Pre-treatment serum CA125-Tn levels were significantly higher in advanced-stage HGSOC patients than in early-stage patients (P = 0.029). In advanced-stage patients, the pre-treatment CA125-Tn level increased with an increase in Fagotti’s score (P = 0.004) and with the extension of peritoneal dissemination (P = 0.011). The pre-treatment CA125-Tn level increased with the volume of residual disease (P = 0.005). The association between CA125-Tn level and suboptimal surgery remained significant even after adjustment for treatment type and stage. Pre-treatment CA125-Tn levels were also related to disease recurrence. Conclusion Serum CA125-Tn level could be a novel biomarker for peritoneal dissemination and a promising predictor of surgical completeness in ovarian cancer. Patients with lower CA125-Tn levels were more likely to have no residual disease. CA125-Tn could help surgeons to adopt optimized treatment strategies for patients with advanced ovarian cancer as a pre-treatment evaluator.

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Using a machine learning algorithm to predict outcome of primary cytoreductive surgery in advanced ovarian cancer

Piedimonte

Erdman

et al. 2022

Journal of Surgical Oncology

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Objective To develop a machine learning (ML) algorithm to predict outcome of primary cytoreductive surgery (PCS) in patients with advanced ovarian cancer (AOC) Methods This retrospective cohort study included patients with AOC undergoing PCS between January 2017 and February 2021. Using radiologic criteria, patient factors (age, CA‐125, performance status, BRCA) and surgical complexity scores, we trained a random forest model to predict the dichotomous outcome of optimal cytoreduction (<1 cm) and no gross residual (RD = 0 mm) using JMP‐Pro 15 (SAS). This model is available at https://ipm-ml.ccm.sickkids.ca. Results One hundred and fifty‐one patients underwent PCS and randomly assigned to train (n = 92), validate (n = 30), or test (n = 29) the model. The median age was 58 (27–83). Patients with suboptimal cytoreduction were more likely to have an Eastern Cooperative Oncology Group 3–4 (11% vs. 0.75%, p = 0.004), lower albumin (38 vs. 41, p = 0.02), and higher CA125 (1126 vs. 388, p = 0.012) than patients with optimal cytoreduction (n = 133). There were no significant differences in age, histology, stage, or BRCA status between groups. The bootstrap random forest model had AUCs of 99.8% (training), 89.6%(validation), and 89.0% (test). The top five contributors were CA125, albumin, diaphragmatic disease, age, and ascites. For RD = 0 mm, the AUCs were 94.4%, 52%, and 84%, respectively. Conclusion Our ML algorithm demonstrated high accuracy in predicting optimal cytoreduction in patients with AOC selected for PCS and may assist decision‐making.

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Integrated prediction model of patient factors, resectability scores and surgical complexity to predict cytoreductive outcome and guide treatment plan in advanced ovarian cancer

Cited by 5 publications

References 34 publications

Validation of the Integrated Prediction Model algorithm for outcome of cytoreduction in advanced ovarian cancer

Validation of the Integrated Prediction Model algorithm for outcome of cytoreduction in advanced ovarian cancer

Evaluation of serum CA125-Tn glycoform in peritoneal dissemination and surgical completeness of high-grade serous ovarian cancer

Using a machine learning algorithm to predict outcome of primary cytoreductive surgery in advanced ovarian cancer

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