Tozzi classification of diaphragmatic surgery in patients with stage IIIC-IV ovarian cancer based on surgical findings and complexity.

OBJECTIVE: To introduce a systematic classification of diaphragmatic surgery in patients with ovarian cancer based on disease spread and surgical complexity. METHODS: For all consecutive patients who underwent diaphragmatic surgery during Visceral-Peritoneal debulking (VPD) in the period 2009-2017, we extracted: initial surgical finding, extent of liver mobilization and type of procedure. Combining these features, we aimed to classify the surgical procedures necessary to tackle different presentation of diaphragmatic disease. We also report histology, intra- and post-operative specific complication rate based on the classification. RESULTS: A total of 170 patients were included in this study, 110 (64.7%) had a peritonectomy, while 60 (35.3%) had a full thickness resection with pleurectomy. We identified 3 types of surgical procedures. Type I treated 28 out of 170 patients (16.5%) who only had anterior diaphragm disease, needed no liver mobilization, included peritonectomy and had no morbidity recorded. Type II pertained to 105 out of 170 patients (61.7%) who had anterior and posterior disease, needed partial and sometimes full liver mobilization, had a mix of peritonectomy and full thickness resection, and experienced 10% specific morbidity. Type III included 37 out of 170 patients (21.7%) who needed full mobilization of the liver, always had full thickness resection, and suffered 30% specific morbidity. CONCLUSION: Diaphragmatic surgery can be classified in 3 types. The adoption of this classification can facilitate standardization of the surgery, comparison of data and define the expertise required. Finally, this classification can be a benchmark to establish the training required to treat diaphragmatic disease.

The NICE classification for 'Ultra-radical (extensive) surgery for advanced ovarian cancer' guidance does not meaningfully predict postoperative complications: a cohort study.

OBJECTIVE: To determine which descriptors of cytoreductive surgical extent in advanced ovarian cancer (AOC) best predict postoperative morbidity. DESIGN: Retrospective notes review. SETTING: A gynaecological cancer centre in the UK. POPULATION: Six hundred and eight women operated on for AOC over a period of 114 months at a tertiary cancer centre, between 16 August 2007 and 16 February 2017. METHODS: Outcome data were analysed by six approaches to classify the extent of surgery: standard/ultra-radical surgery; standard/radical/supra-radical surgery; presence/absence of gastrointestinal resections; low/intermediate/high surgical complexity score (SCS); presence of bowel anastomoses and/or diaphragmatic surgery; and the presence/absence of multiple bowel resections. MAIN OUTCOME MEASURES: Major (grades 3-5) postoperative morbidity and mortality. RESULTS: Forty-three (7.1%) patients experienced major complications. Grade-5 complications occurred in six patients (1.0%). Patients who underwent multiple bowel resections had a relative risk (RR) of 7.73 (95% confidence interval, 95% CI 3.92-15.26), patients with a high SCS had an RR of 6.12 (95% CI 3.25-11.52), patients with diaphragmatic surgery and gastrointestinal anastomosis had an RR of 5.57 (95% CI 2.65-11.72), patients with 'any gastrointestinal resection' had an RR of 4.69 (95% CI 2.66-8.24), patients with ultra-radical surgery had an RR of 4.65 (95% CI 2.26-8.79), and patients with supra-radical surgery had an RR of 4.20 (95% CI 2.35-7.51) of grades 3-5 morbidity, compared with patients undergoing standard surgery as defined by the National Institute for Health and Care Excellence (NICE) in the UK. No significant difference was seen in the rate of major morbidity between standard (6/59, 10.2%) and ultra-radical (9/81, 11.1%) surgery within the cohort who had intermediate complex surgery (P > 0.05). CONCLUSIONS: The numbers of procedures performed significantly correlate with major morbidity. The number of procedures performed better predicted major postoperative morbidity than the performance of certain 'high risk' procedures. We recommend using SCS to define a higher risk operation. NICE should re-evaluate the use of the term 'ultra-radical' surgery. TWEETABLE ABSTRACT: Multiple bowel resection is the best predictor of morbidity and is more predictive than 'ultra-radical surgery'.

Classification of Ovarian Cancer Surgery Facilitates Treatment Decisions in a Gynecological Multidisciplinary Team.

OBJECTIVE: Proper planning of intervention and care of ovarian cancer surgery is of outmost importance and involves a wide range of personnel at the departments involved. The aim of this study is to evaluate the introduction of an ovarian surgery classification (COVA) system for facilitating multidisciplinary team (MDT) decisions. MATERIALS AND METHODS: Four hundred eighteen women diagnosed with ovarian cancers (n = 351) or borderline tumors (n = 66) were selected for primary debulking surgery from January 2008 to July 2013. At an MDT meeting, women were allocated into 3 groups named "pre-COVA" 1 to 3 classifying the expected extent of the primary surgery and need for postoperative care. On the basis of the operative procedures performed, women were allocated into 1 of the 3 corresponding COVA 1 to 3 groups. The outcome measure was the predictive value of the pre-COVA score compared with the actual COVA performed. RESULTS: The MDT meeting allocated 213 women (51%) to pre-COVA 1, 136 (33%) to pre-COVA 2, and 52 (12%) to pre-COVA 3. At the end of surgery, 168 (40%) were classified as COVA 1, 158 (38%) were classified as COVA 2, and 28 (7%) were classified as COVA 3. Traced individually, 212 (51%) patients were correctly preclassified at the MDT meeting and distributed into 110 (52%) COVA 1, 71 (52%) COVA 2, and 17 (32%) COVA 3. Analyzing the subgroup of patients with cancer, 164 (47%) were correctly preclassified. Regarding the International Federation of Gynecology and Obstetrics (FIGO) stages, the pre-COVA classification predicted the actual COVA group in 79 (49%) FIGO stages I to IIIB and in 85 (45%) FIGO stages IIIC to IV. CONCLUSIONS: The COVA classification system is a simple and useful tool in the MDT setting where specialists make treatment decisions based on advanced technology. The use of pre-COVA classification facilitates well-organized patient care-relevant procedures to be undertaken. Pre-COVA accurately predicts the final COVA in 51% classified women.

The Use of "Optimal Cytoreduction" Nomenclature in Ovarian Cancer Literature: Can We Move Toward a More Optimal Classification System?

OBJECTIVES: The objective of this study is to explore how cytoreductive surgical outcomes such as residual disease (RD) and use of the term "optimal cytoreduction" (OCR) have changed over time in the ovarian cancer literature. METHODS: We identified all English-language publications referring to ovarian cancer cytoreduction for a 12-year period. Publications were evaluated for how the diameter of RD was categorized and whether OCR was defined. In addition, the use of RD and OCR terminology trends over time and associations between terminology and the region of corresponding author, study type, and journal impact factor were explored. RESULTS: Of the 772 publications meeting inclusion criteria, the RD stratification points used to demarcate patient groups were as follows: 0 mm (45%), 5 mm (3.6%), 10 mm (65%), and 20 mm (24%). The use of 0-mm RD (odds ratio [OR], 1.1; 95% confidence interval, 1.05-1.15) and 10-mm RD (OR, 1.1; 95% confidence interval, 1.09-1.20) to delineate patient outcomes increased over time. The use of OCR terminology did not change over time but was more commonly used in clinical studies as well as those from North America. Many studies (70%) defined OCR as less than or equal to 10-mm RD, whereas 30% defined OCR differently or not at all. CONCLUSIONS: Optimal cytoreduction terminology remains ambiguous and inconsistently used in the ovarian cancer surgical literature. On the basis of this literature review, we propose a novel classification system to categorize RD without reference to OCR while accurately and succinctly identifying meaningful clinical subgroups and minimizing bias.