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Cochrane Database of Systematic Reviews Protocol - Intervention

Robotic assisted surgery for gynaecological cancer

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Abstract

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:

To evaluate the evidence for and against robotic assisted surgery in gynaecological cancers (vulval, vaginal, cervical, uterine, ovarian and fallopian cancers).

Background

Description of the condition

Gynaecological cancer consists of vulval, vaginal, cervical, uterine, ovarian and fallopian cancers, and accounts for 10% to 15% of cancers in women with differing incidence and prognoses often dependent on geographical locations (Kehoe 2006). Cervical cancer is the second most common cancer in women, and approximately 80% of these cancers occur in developing countries (Boyle 2003). It is the most prevalent cancer in women living in sub‐Saharan Africa, Central and South America, and south‐east Asia. Ovarian cancer, the sixth most frequent cancer in women, contributes to the most frequent cause of death in gynaecological malignancies in the western world (Boyle 2003). The incidence differs worldwide, with the highest in Scandinavian countries (at over 20/100,000), and lowest in Japan (3/100,000) (Kehoe 2006). Cancer Research UK reported that the incidence of ovarian cancer over a woman's lifetime is one in 50 (Cancer Research UK 2009). According to the National Cancer Institute, uterine cancer is the most common type of gynaecologic cancer; in the United States, approximately 42,000 cases were diagnosed and 7780 women died from the disease in 2009 (American Cancer Society 2009).

Description of the intervention

New technologies such as a high intensity light source, flexible hand instruments and electrosurgical devices have led to modern day laparoscopy. Technological advances continue to grow rapidly in the area of minimally invasive gynaecologic surgery. Studies have clearly shown that laparoscopic surgery leads to faster recovery with shorter hospital stay, improved cosmesis, decreased blood loss, and reduced postoperative pain (Boike 1993; Lo 1999; Raju 1994; Yuen 1997). Robotic surgery is the latest innovation in the field of minimally invasive surgery. A robotic surgical system is not a "robot", because it does not perform a surgical procedure on its own and does not involve any artificial intelligence. Therefore, surgery with the "robot" is best described as "computer‐assisted laparoscopic surgery". Nevertheless, the terms, "robotic surgery", "robot‐assisted surgery" or "robot‐assisted laparoscopic surgery" have all been widely accepted.

One of the first applications of robotic‐assisted technology to gynaecologic surgery was a voice‐activated robotic arm known as Aesop (Computer Motion Inc., Goleta, CA). The primary role of Aesop was to operate the camera during laparoscopic surgery by voice. Mettler 1998 compared Aesop to a surgical assistant holding the laparoscope during gynaecologic surgery, and found that the operation time was faster with Aesop because it improved efficiency by allowing the two surgeons to use both hands for surgery.

Another predecessor to the current platform of surgical robots was Zeus (Computer Motion Inc., Goleta, CA). This system consists of three remotely controlled robotic arms that are attached to the surgical table and a workstation called a robotic console, which has all the instrument controls for operation, and three‐dimensional vision is obtained with the aid of special spectacles. The two operating arms with interchangeable "micro‐wrist" instruments are designed to mimic the movements of the human wrist. A third arm incorporates the Aesop, which provides the surgeon with magnified, rock‐steady visualisation of the internal operative field. This early robotic system represents a significant paradigm shift which allows the surgeon to step away from the operating table. Early studies reported Zeus' successful application to fallopian tube reanastomosis (procedures to reunite the fallopian tubes in order to reverse the effect of reproductive sterilisation and to regain fertility) with a pregnancy rate of 50% at one year (Falcone 2000).

There is currently only one robotic surgical platform commercially available, with FDA approval for performing gynaecologic procedures. This is the da Vinci surgical system (Intuitive Surgical Inc., Sunnyvale, CA, USA). It consists of three components. The first component is a surgeon‐operated console where the surgeon controls the robotic system remotely. A stereoscopic viewer as well as hand and foot controls are housed in this unit. The second component is the InSite Vision System (Intuitive Surgical Inc., Sunnyvale, CA), which provides a three‐dimensional stereoscopic imaging through a 12‐mm endoscope. The third component is a patient‐side cart with Endowrist instruments (Intuitive Surgical, Sunnyvale, CA) and three or four robotic arms. The robotic instruments have a wrist‐like mechanism that enables intra‐abdominal articulation, thereby providing seven degrees of freedom compared to the five degrees of freedom of traditional laparoscopy (Holloway 2009a; Oehler 2009).

Robotic surgery has significant technical advantages compared to conventional laparoscopy (a type of minimally invasive surgery in which a small incision is made in the abdominal wall through which an instrument called a laparoscope is inserted to allow structures within the abdomen and pelvis to be seen), including the availability of three‐dimensional vision, instrument freedom, greater precision, easier suturing and knot tying, and a shorter learning process (Cho 2009). The only technical disadvantage of the robotic surgical system when compared to laparotomy (an operation to open the abdomen) is the lack of tactile perception, although the importance of tactile perception in most gynaecological procedures is currently unknown (Moy 2010). Another major downside of robotic surgery is cost, which is difficult to evaluate in a new technology. Furthermore, the expenses for disposable and limited‐use robotic instruments add significantly to these costs.

How the intervention might work

Robotic assisted surgery has been reported in endometrial cancer and cervical cancer. At present, no publication has been found with robotic assisted surgery in ovarian cancer.

Standard treatment for endometrial cancer involves hysterectomy with bilateral salpingo‐oophorectomy (resection of bilateral fallopian tubes and ovaries).The role of routine pelvic and para‐aortic lymphadenectomy (surgical removal of a lymph node or nodes) remains controversial. For many institutions, robotic surgery has become an alternative for patients with endometrial cancer, which includes hysterectomy and lymphadenectomy. An increasing number of studies describe excellent results in robotic‐assisted hysterectomy with lymphadenectomy for endometrial cancer (Bell 2008; Cardenas‐Goicoechea 2010; DeNardis 2008; Field 2007; Gehrig 2008; Holloway 2009b; Lambaudie 2008; Reynolds 2005; Seamon 2009; Shafer 2008; Veljovich 2008; ), including good lymph node yield, low blood loss, reasonable operative time, with short hospital stays. Gehrig 2008 reported 49 obese women undergoing robotic endometrial cancer staging and found that robotic surgery was more favourable in obese women compared with conventional laparoscopy.

Radical hysterectomy with lymphadenectomy is the standard surgical treatment for stage 1A2‐1B uterine cervical cancer. Magrina 2008 compared robotic radical hysterectomy, laparoscopic radical hysterectomy and laparotomy for radical hysterectomy. The patients who underwent robotic surgery had significantly less blood loss and a shorter hospital stay than the other two groups. Complications in this series of patients were minor. Smaller studies describing the use of robotic surgery in radical hysterectomy showed similar success (Boggess 2008; Fanning 2008; Kim 2008; Lowe 2009; Maggioni 2009; Nezhat 2008;; Persson 2009). Recently, a proposal for a randomised controlled trial (RCT) has been published to compare robotic/laparoscopic radical hysterectomy with laparotomy in cervical cancer. The proposal plan is to enrol 740 patients in total (Obermair 2008). Various radical trachelectomy (surgical removal of the cervix, the lower portion of the uterus that protrudes into the vagina) results suggest that fertility‐sparing treatment is a viable option in early stage cervical cancer (Diaz 2008; Plante 2008). Persson 2008 recently described two cases of robotic radical trachelectomy. Together with the report by Geisler 2008, the findings suggest that the robot offers excellent visualisation of the vasculature and parametrial tissues (the connective tissue and fat adjacent to the uterus) which must be isolated during the procedure, while still offering a minimally invasive technique that is likely to preserve fertility and lead to fast recovery. These case reports suggest that this technique may provide a good option for women seeking to preserve fertility. Surgical options for assessing para‐aortic node status include extraperitoneal (taking place outside the peritoneal cavity) or transperitoneal (passing or performed through the peritoneum) techniques. Vergote 2008 reported on a series of five patients assessed through a retroperitoneal (situated behind the peritoneum) robotic technique. The reported results are excellent with brief hospital stays and minimal blood loss. All the procedures were completed with less than one hour console time. The authors conclude that robotic retroperitoneal para‐aortic sampling is a feasible procedure that warrants further evaluation.

Lenihan 2008 indicated that 50 cases are required to reach an acceptable level of proficiency and stable operative times for benign gynaecological robotic assisted surgery. Currently however it is still generally accepted that we are on a learning curve for the procedure.

Why it is important to do this review

The mounting evidence demonstrates the feasibility and safety of robotic surgery in gynaecology. Although the experience with robotics in gynaecology is limited, the potential is significant. However, it is still unknown which gynaecological procedures are going to benefit most from robotics. Robotic technology seems to have advantages to conventional laparoscopy and open procedures for the surgical treatment of gynaecological malignancies. Therefore, this systematic review aims to fully assess the beneficial and harmful effects of robotic assisted surgery for gynaecological cancers.

Objectives

To evaluate the evidence for and against robotic assisted surgery in gynaecological cancers (vulval, vaginal, cervical, uterine, ovarian and fallopian cancers).

Methods

Criteria for considering studies for this review

Types of studies

We will include all RCTs comparing robotic assisted surgery in gynaecological cancer to laparoscopic or open surgery. We will also include RCTs comparing different types of robotic assistants. We will include trials irrespective of blinding, number of patients randomised and language of the article.
We will review quasi randomised trials or non‐RCTs but will not consider these for inclusion.

Types of participants

We will include patients with indications for elective surgical treatment in gynaecological cancer.

Types of interventions

We will include the following comparisons:

  • robotic surgery versus laparoscopic surgery;

  • robotic surgery versus open surgery;

  • comparison of different types of robotic assistants.

Types of outcome measures

Primary outcomes

  • Disease‐free survival.

  • Overall survival.

Secondary outcomes

  • Overall and postoperative hospital stay.

  • Intraoperative and postoperative complications, for example: intraoperative injury to bladder, ureters, bowel, blood vessels, and nerves; postoperative acute confusion, nausea and vomiting, postoperative fever, secondary haemorrhage atelectasis (collapse of lung tissue affecting part or all of one lung), pneumonia, wound infection, wound or anastomosis dehiscence (breakdown of the stitches), embolism and deep vein thrombosis, acute urinary retention, bowel obstruction due to fibrinous adhesions, paralytic ileus (a partial or complete non‐mechanical blockage of the small and/or large intestine), incisional hernia, persistent fistula (an abnormal connection or passageway between two organs or vessels that normally do not connect).

  • Early and late mortality (early mortality is defined as death within 30 days; late mortality is defined as death with in 3 months).

  • Total operating time.

  • The instrument set‐up time (robotic and laparoscopic surgery).

  • Estimated blood loss.

  • Re‐admission rate (within 30 days postoperatively).

  • Quality of life, measured by a validated scale: single indicator number, single index number, profile of interrelated scores, battery of independent scores.

  • Costs.

Search methods for identification of studies

Electronic searches

We will search the following electronic databases:

  • The Cochrane Gynaecological Cancer Collaborative Review Group's Trial Register;

  • The Cochrane Central Register of Controlled Trials (CENTRAL) on the Cochrane Library (current issue);

  • MEDLINE (from 1950);

  • EMBASE (from 1974).

For MEDLINE we will develop a search strategy based on the terms related to the review topic (Appendix 1).
For databases other than MEDLINE, we will adapt the search strategy accordingly.

We will identify all relevant articles found on PubMed and using the 'related articles' feature, we will carry out an update search for newly published articles every year.

Searching other resources

Grey literature

We will search Metaregister, Physicians Data Query, www.controlled‐trials.com/rct, www.clinicaltrials.gov and www.cancer.gov/clinicaltrials for ongoing trials. We will contact the main investigators of the relevant ongoing trials for further information, and the major co‐operative trials groups active in this area.

Handsearching

We will handsearch the reference lists of all relevant trials obtained by this search for further trials.

Correspondence

If required, we will contact authors of relevant trials for further published and unpublished data.

Language restrictions

We will look at papers in all languages and carry out translations if necessary.

Data collection and analysis

Selection of studies

We will download all titles and abstracts retrieved by electronic searching to the reference management database Endnote, we will remove duplicates, and two review authors will independently examine the remaining references. We will exclude those studies which clearly do not meet the inclusion criteria, and we will obtain copies of the full text of potentially relevant references. Two review authors will independently assess the eligibility of retrieved papers. We will resolve disagreements by discussion between the two review authors and if necessary using a third review author. We will document the reasons for exclusion.

Data extraction and management

We will extract the following information from the studies included in the review and present it in a table of characteristics of included studies.

Trial characteristics

(a) Trial design: multicentre or single centre; single phase or crossover design.
(b) Number of patients randomised, excluded and analysed.
(c) Duration, timing and location of the trial.
(d) Source of funding.

Baseline characteristics of the studied groups

(a) Type and stages of gynaecological cancer.
(b) Age of the patients.
(c) Investigative work‐up.
(d) Body mass index (BMI), and American Society of Anaesthesiology (ASA) classification.
(e) Type of surgical procedures.
(f) The prognostic factors for surgical recovery e.g. smoking, co‐morbidities, obesity.

Intervention

(a) Randomisation number.
(b) Type of intervention and control.
(c) Other identical perioperative care in both groups.
(d) Details of intervention.

Outcomes

(a) Outcomes reported.
(b) How are outcomes defined?
(c) How are outcomes measured?
(d) Timing of outcome measurement.

Two review authors will independently extract all data using forms designed to Cochrane guidelines. We will seek additional information on trial methodology and/or actual trial data from the authors of trials which appear to meet the eligibility criteria but have aspects of methodology that are unclear or data in an unsuitable form for meta‐analysis. We will register and resolve differences of opinion by consensus or using a third review author.

For binary outcomes, we will record the number of participants experiencing the event in each group of the trial. For continuous outcomes, for each group we will extract the arithmetic means and standard deviations. If the data are reported using geometric means, we will extract standard deviations on the log scale. We will extract medians and ranges and report them in tables.

Assessment of risk of bias in included studies

We will assess the risk of bias in included RCTs using The Cochrane Collaboration's tool and the criteria specified in Chapter Eight of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2008). This will include assessment of :

  • sequence generation;

  • allocation concealment;

  • blinding (we will restrict assessment of blinding to blinding of outcome assessors, since it is generally not possible to blind participants and personnel to surgical interventions);

  • incomplete outcome data;

  • selective reporting of outcomes;

  • other possible sources of bias (e.g. a potential source of bias related to the specific study design used, or stopped early due to some data‐dependent process, extreme baseline imbalance etc).

Two review authors will independently apply the risk of bias tool and we will resolve differences by discussion. We will present the results in the risk of bias table and also in both a risk of bias graph and a risk of bias summary section. We will interpret the results of meta‐analyses in light of the findings with respect to risk of bias.

Measures of treatment effect

  • For dichotomous outcomes (e.g. complications), we will extract the number of patients in each group (e.g. patients who did/did not get abdominal sepsis) who experienced the outcome of interest and the number of patients assessed at endpoint, in order to estimate a relative risk (RR).

  • For continuous outcomes (e.g. QoL measures), we will extract the final value and standard deviation of the outcome of interest and the number of patients assessed at endpoint in each treatment arm at the end of follow up, in order to estimate the mean difference between treatment arms and its standard error. We will use standardised mean differences where outcomes are measured using different scales.

  • For time‐to‐event data (e.g. disease‐free survival), we will extract the hazard ratio (HR) and its variance from trial reports; if these are not presented, we will extract the data required to estimate them using Parmar's methods (Parmar 1998), e.g. number of events in each arm and log‐rank P‐value comparing the relevant outcomes in each arm, or relevant data from Kaplan‐Meier survival curves. If it is not possible to estimate the HR, we will extract the number of patients in each treatment arm who experienced the outcome of interest and the number of participants assessed, in order to estimate a RR.

Where possible, we will extract all data that are relevant to an intention‐to‐treat analysis, in which participants are analysed in groups to which they were assigned.

Unit of analysis issues

We will not include cross‐over or cluster‐randomised trials.

Dealing with missing data

We will attempt to extract data on the outcomes only among participants who were assessed at endpoint. We will not impute missing outcome data. For the primary outcome, if data are missing or only imputed outcome data are reported, we will contact trial authors to request data on the outcomes among participants who were assessed.

Assessment of heterogeneity

We will assess heterogeneity among studies by visual inspection of forest plots, by estimation of the percentage heterogeneity between trials which cannot be ascribed to sampling variation (Higgins 2003), by a formal statistical test of the significance of the heterogeneity (Deeks 2001), and if possible by sub‐group analyses: Subgroup analysis and investigation of heterogeneity. If there is evidence of substantial heterogeneity, we will investigate and report the possible reasons for this.

Assessment of reporting biases

We will use funnel plots corresponding to meta‐analysis of the primary outcome to assess the potential for small study effects such as publication bias. If these plots suggest that treatment effects may not be sampled from a symmetric distribution, as assumed by the random‐effects model, we will perform further meta‐analyses using fixed‐effect models.

Data synthesis

If sufficient, where clinically similar studies are available, we will pool their results in meta‐analyses.

  • For any dichotomous outcomes (e.g. complications), we will pool RRs using the inverse‐variance random‐effects method.

  • For continuous outcomes (e.g. QoL measures), we will pool the mean differences between the treatment arms at the end of follow up using the inverse‐variance fixed‐effects method if all trials measured the outcome on the same scale; otherwise we will pool standardised mean difference.

  • For trials with multiple treatment groups, we will divide the 'shared' comparison group into the number of treatment groups and comparisons between each treatment group, and treat the split comparison group as independent comparisons.

  • We will use random‐effects models with inverse variance weighting for all meta‐analyses (DerSimonian 1986).

  • If possible, we will synthesise studies making different comparisons using the sub‐group methods of Bucher 1997.

Subgroup analysis and investigation of heterogeneity

We will perform sub‐group analyses, grouping the trials by:

  • different kinds of surgical procedures;

  • different types of gynaecological cancer;

  • different types of robot assistants.

We will consider factors such as age, stage, length of follow up, and adjusted/unadjusted analysis in interpretation of any heterogeneity.

Sensitivity analysis

We will perform sensitivity analyses excluding studies at high risk of bias.