Performance of Artificial Intelligence Content Detectors Using Human and Artificial Intelligence-Generated Scientific Writing.

BACKGROUND: Few studies have examined the performance of artificial intelligence (AI) content detection in scientific writing. This study evaluates the performance of publicly available AI content detectors when applied to both human-written and AI-generated scientific articles. METHODS: Articles published in Annals of Surgical Oncology (ASO) during the year 2022, as well as AI-generated articles using OpenAI's ChatGPT, were analyzed by three AI content detectors to assess the probability of AI-generated content. Full manuscripts and their individual sections were evaluated. Group comparisons and trend analyses were conducted by using ANOVA and linear regression. Classification performance was determined using area under the curve (AUC). RESULTS: A total of 449 original articles met inclusion criteria and were evaluated to determine the likelihood of being generated by AI. Each detector also evaluated 47 AI-generated articles by using titles from ASO articles. Human-written articles had an average probability of being AI-generated of 9.4% with significant differences between the detectors. Only two (0.4%) human-written manuscripts were detected as having a 0% probability of being AI-generated by all three detectors. Completely AI-generated articles were evaluated to have a higher average probability of being AI-generated (43.5%) with a range from 12.0 to 99.9%. CONCLUSIONS: This study demonstrates differences in the performance of various AI content detectors with the potential to label human-written articles as AI-generated. Any effort toward implementing AI detectors must include a strategy for continuous evaluation and validation as AI models and detectors rapidly evolve.

From Alpha to Omicron and Beyond: Associations Between SARS-CoV-2 Variants and Surgical Outcomes.

INTRODUCTION: The COVID-19 pandemic has significantly influenced surgical practices, with SARS-CoV-2 variants presenting unique pathologic profiles and potential impacts on perioperative outcomes. This study explores associations between Alpha, Delta, and Omicron variants of SARS-CoV-2 and surgical outcomes. METHODS: We conducted a retrospective analysis using the National COVID Cohort Collaborative database, which included patients who underwent selected major inpatient surgeries within eight weeks post-SARS-CoV-2 infection from January 2020 to April 2023. The viral variant was determined by the predominant strain at the time of the patient's infection. Multivariable logistic regression models explored the association between viral variants, COVID-19 severity, and 30-d major morbidity or mortality. RESULTS: The study included 10,617 surgical patients with preoperative COVID-19, infected by the Alpha (4456), Delta (1539), and Omicron (4622) variants. Patients infected with Omicron had the highest vaccination rates, most mild disease, and lowest 30-d morbidity and mortality rates. Multivariable logistic regression demonstrated that Omicron was linked to a reduced likelihood of adverse outcomes compared to Alpha, while Delta showed odds comparable to Alpha. Inclusion of COVID-19 severity in the model rendered the odds of major morbidity or mortality equal across all three variants. CONCLUSIONS: Our study examines the associations between the clinical and pathological characteristics of SARS-CoV-2 variants and surgical outcomes. As novel SARS-CoV-2 variants emerge, this research supports COVID-19-related surgical policy that assesses the severity of disease to estimate surgical outcomes.

Association of Daily Step Count and Postoperative Complication among All of Us Research Participants.

BACKGROUND: The association between preoperative wearable device step counts and surgical outcomes has not been examined using commercial devices linked to electronic health records (EHR). This study measured the association between daily preoperative step counts and postoperative complications. STUDY DESIGN: Data was obtained using the All of Us (AOU) Research program, a nationwide initiative to collect EHR and health-related data from the population. Included were patients who underwent a surgical procedure included in the National Surgical Quality Improvement Program (NSQIP) targeted procedures dataset. Excluded were patients who did not have available physical activity FitBit data. Primary outcome was the development of a postoperative complication. All analyses were performed in the AOU researcher workbench. RESULTS: Of 27,150 patients who underwent a surgical procedure, 475 participants with preoperative wearable data were included. 74.7% were female and 85.2% were White. The average age was 57.2 years. The overall rate of postoperative complications was 12.6%. Patients averaging fewer than 7,500 daily steps were at increased odds for developing a postoperative complication (OR 1.83, 95% CI [1.01, 3.31]). Following adjustment for age, sex, race, comorbid disease, body mass index (BMI), and relative procedure risk, patients with a baseline average steps/day < 7,500 were at increased odds for postoperative complication (aOR = 2.06, 95% CI [1.05, 4.06]). CONCLUSIONS: This study found an increase in overall postoperative complication rate in patients recording lower average preoperative step counts. Patients with a baseline of less than 7,500 steps per day had increased odds of postoperative complications in this cohort. This data supports the use of wearable devices for surgical risk stratification and suggests step count may measure preoperative fitness.

Risk of Early Postoperative Cardiovascular and Cerebrovascular Complication in Patients with Preoperative COVID-19 Undergoing Cancer Surgery.

BACKGROUND: As the COVID-19 pandemic shifts to an endemic phase, an increasing proportion of patients with cancer and a preoperative history of COVID-19 will require surgery. This study aimed to assess the influence of preoperative COVID-19 on postoperative risk for major adverse cardiovascular and cerebrovascular events (MACEs) among those undergoing surgical cancer resection. Secondary objectives included determining optimal time-to-surgery guidelines based on COVID-19 severity and discerning the influence of vaccination status on MACE risk. STUDY DESIGN: National COVID Cohort Collaborative Data Enclave, a large multi-institutional dataset, was used to identify patients that underwent surgical cancer resection between January 2020 and February 2023. Multivariate regression analysis adjusting for demographics, comorbidities, and risk of surgery was performed to evaluate risk for 30-day postoperative MACE. RESULTS: Of 204,371 included patients, 21,313 (10.4%) patients had a history of preoperative COVID-19. History of COVID-19 was associated with an increased risk for postoperative composite MACE as well as 30-day mortality. Among patients with mild disease who did not require hospitalization, MACE risk was elevated for up to 4 weeks after infection. Postoperative MACE risk remained elevated more than 8 weeks after infection in those with moderate disease. Vaccination did not reduce risk for postoperative MACE. CONCLUSIONS: Together, these data highlight that assessment of the severity of preoperative COVID-19 infection should be a routine component of both preoperative patient screening as well as surgical risk stratification. In addition, strategies beyond vaccination that increase patients' cardiovascular fitness and prevent COVID-19 infection are needed.

Increasing Trainee Engagement in Surgery Societies: The Society of Asian Academic Surgeons Experience.

INTRODUCTION: Surgical societies provide a plethora of resources for trainees; however, these opportunities are often underused due to suboptimal guidance, sponsorship, or mentorship. Here, we present the Society of Asian Academic Surgeons (SAAS) experience in enhancing the trainee experience and engagement in a surgical society focused on professional development. MATERIALS AND METHODS: We conducted an interactive idea-generating session during the 2022 SAAS Conference with all Associate Members (trainees) present in Honolulu, HI. Recurrent themes, concepts, and ideas/suggestions were carefully considered when planning the next SAAS Conference in Baltimore, MD. We employed a more targeted approach to trainee engagement at the 2023 SAAS Conference, with breakout sessions geared toward various levels of trainees, in addition to increased social events and networking opportunities. We obtained feedback from trainees in attendance through an electronic survey and informal conversations with faculty and Associate Members. RESULTS: Opportunities for surgical subspecialty networking was the most well-received portion of our formal Career Development program. The majority of trainees in attendance were medical students or junior residents who valued the exposure to junior faculty and those in leadership positions at academic institutions. The addition of a group text for easy communication among trainees and informal social activities for Associate Member networking among themselves were crucial in improving the overall conference experience. CONCLUSIONS: Opportunities to maximize trainee engagement in surgical societies are heightened during in-person conferences. Targeted session topics, guided networking opportunities, and creating channels for easier communication along with more social events have enhanced the overall experience for aspiring and current surgical trainees.

Benign mature teratoma of the cecum: a case report.

A teratoma is a typically benign tumor derived from more than one embryonic cell line, and it is characterized by presence of tissue foreign to the tumor location site. With the unlikely primary location in the gastrointestinal tract and no history of malignancy, we present a rare case of a primary mature cystic teratoma of the cecum. The patient is a 66-year-old male with imaging demonstrating an extraluminal, seemingly fat-containing mass abutting the cecum. The patient underwent resection, and final pathology revealed a mature cystic teratoma. Primary mature teratoma of the cecum is exceptionally rare; thus, diagnosis can be challenging. As he had no primary testicular or retroperitoneal mass, this cystic lesion likely represents a developmental abnormality and not a true neoplasm. The radiographic features, presentation, differential diagnoses, and treatment recommendations are discussed.

From ChatGPT to Treatment: the Future of AI and Large Language Models in Surgical Oncology.

This paper explores the transformative potential of Large Language Models (LLMs) within the context of surgical oncology and outlines the foundational mechanisms behind these models. LLMs, such as GPT-4, have rapidly evolved in terms of scale and capabilities, with profound implications for their applications in healthcare. These models, rooted in the Generative Pretrained Transformer architecture, exhibit advanced natural language understanding and generation skills. Within surgical oncology, LLMs, when integrated into a Generalist Medical AI (GMAI) framework, hold great promise in offering real-time support throughout the cancer journey. However, alongside these opportunities, this paper underscores the importance of ethical, privacy, and efficacy considerations, especially in light of issues like data drift and potential biases. Collaborative efforts among healthcare providers, AI developers, and regulatory bodies are pivotal in ensuring responsible and effective use of LLMs in surgical oncology, thereby contributing to enhanced patient care and safety. As LLMs continue to advance, they are poised to become indispensable tools in the delivery of high-quality, efficient care in this specialized medical field.

Prediction of Early Recurrence Following CRS/HIPEC in Patients With Disseminated Appendiceal Cancer.

INTRODUCTION: In patients with disseminated appendiceal cancer (dAC) who underwent cytoreductive surgery (CRS) with hyperthermic intraperitoneal chemotherapy (HIPEC), characterizing and predicting those who will develop early recurrence could provide a framework for personalizing follow-up. This study aims to: (1) characterize patients with dAC that are at risk for recurrence within 2 y following of CRS ± HIPEC (early recurrence; ER), (2) utilize automated machine learning (AutoML) to predict at-risk patients, and (3) identifying factors that are influential for prediction. METHODS: A 12-institution cohort of patients with dAC treated with CRS ± HIPEC between 2000 and 2017 was used to train predictive models using H2O.ai's AutoML. Patients with early recurrence (ER) were compared to those who did not have recurrence or presented with recurrence after 2 y (control; C). However, 75% of the data was used for training and 25% for validation, and models were 5-fold cross-validated. RESULTS: A total of 949 patients were included, with 337 ER patients (35.5%). Patients with ER had higher markers of inflammation, worse disease burden with poor response, and received greater intraoperative fluids/blood products. The highest performing AutoML model was a Stacked Ensemble (area under the curve = 0.78, area under the curve precision recall = 0.66, positive predictive value = 85%, and negative predictive value = 63%). Prediction was influenced by blood markers, operative course, and factors associated with worse disease burden. CONCLUSIONS: In this multi-institutional cohort of dAC patients that underwent CRS ± HIPEC, AutoML performed well in predicting patients with ER. Variables suggestive of poor tumor biology were the most influential for prediction. Our work provides a framework for identifying patients with ER that might benefit from shorter interval surveillance early after surgery.

Neighborhood-Level Socioeconomic Disadvantage Predicts Outcomes in Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy for Peritoneal Malignancy.

BACKGROUND: Cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (CRS/HIPEC) improves survival in select patients with peritoneal metastases (PM), but the impact of social determinants of health on CRS/HIPEC outcomes remains unclear. PATIENTS AND METHODS: A retrospective review was conducted of a multi-institutional database of patients with PM who underwent CRS/HIPEC in the USA between 2000 and 2017. The area deprivation index (ADI) was linked to the patient's residential address. Patients were categorized as living in low (1-49) or high (50-100) ADI residences, with increasing scores indicating higher socioeconomic disadvantage. The primary outcome was overall survival (OS). Secondary outcomes included perioperative complications, hospital/intensive care unit (ICU) length of stay (LOS), and disease-free survival (DFS). RESULTS: Among 1675 patients 1061 (63.3%) resided in low ADI areas and 614 (36.7%) high ADI areas. Appendiceal tumors (n = 1102, 65.8%) and colon cancer (n = 322, 19.2%) were the most common histologies. On multivariate analysis, high ADI was not associated with increased perioperative complications, hospital/ICU LOS, or DFS. High ADI was associated with worse OS (median not reached versus 49 months; 5 year OS 61.0% versus 28.2%, P < 0.0001). On multivariate Cox-regression analysis, high ADI (HR, 2.26; 95% CI 1.13-4.50; P < 0.001), cancer recurrence (HR, 2.26; 95% CI 1.61-3.20; P < 0.0001), increases in peritoneal carcinomatosis index (HR, 1.03; 95% CI 1.01-1.05; P < 0.001), and incomplete cytoreduction (HR, 4.48; 95% CI 3.01-6.53; P < 0.0001) were associated with worse OS. CONCLUSIONS: Even after controlling for cancer-specific variables, adverse outcomes persisted in association with neighborhood-level socioeconomic disadvantage. The individual and structural-level factors leading to these cancer disparities warrant further investigation to improve outcomes for all patients with peritoneal malignancies.

Severity of Prior Coronavirus Disease 2019 is Associated With Postoperative Outcomes After Major Inpatient Surgery.

OBJECTIVE: To determine how the severity of prior history (Hx) of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection influences postoperative outcomes after major elective inpatient surgery. BACKGROUND: Surgical guidelines instituted early in the coronavirus disease 2019 (COVID-19) pandemic recommended a delay in surgery of up to 8 weeks after an acute SARS-CoV-2 infection. This was based on the observation of elevated surgical risk after recovery from COVID-19 early in the pandemic. As the pandemic shifts to an endemic phase, it is unclear whether this association remains, especially for those recovering from asymptomatic or mildly symptomatic COVID-19. METHODS: Utilizing the National COVID Cohort Collaborative, we assessed postoperative outcomes for adults with and without a Hx of COVID-19 who underwent major elective inpatient surgery between January 2020 and February 2023. COVID-19 severity and time from infection to surgery were each used as independent variables in multivariable logistic regression models. RESULTS: This study included 387,030 patients, of whom 37,354 (9.7%) were diagnosed with preoperative COVID-19. Hx of COVID-19 was found to be an independent risk factor for adverse postoperative outcomes even after a 12-week delay for patients with moderate and severe SARS-CoV-2 infection. Patients with mild COVID-19 did not have an increased risk of adverse postoperative outcomes at any time point. Vaccination decreased the odds of respiratory failure. CONCLUSIONS: Impact of COVID-19 on postoperative outcomes is dependent on the severity of illness, with only moderate and severe disease leading to a higher risk of adverse outcomes. Existing perioperative policies should be updated to include consideration of COVID-19 disease severity and vaccination status.

Automated machine learning (AutoML) can predict 90-day mortality after gastrectomy for cancer.

Early postoperative mortality risk prediction is crucial for clinical management of gastric cancer. This study aims to predict 90-day mortality in gastric cancer patients undergoing gastrectomy using automated machine learning (AutoML), optimize models for preoperative prediction, and identify factors influential in prediction. National Cancer Database was used to identify stage I-III gastric cancer patients undergoing gastrectomy between 2004 and 2016. 26 features were used to train predictive models using H2O.ai AutoML. Performance on validation cohort was measured. In 39,108 patients, 90-day mortality rate was 8.8%. The highest performing model was an ensemble (AUC = 0.77); older age, nodal ratio, and length of inpatient stay (LOS) following surgery were most influential for prediction. Removing the latter two parameters decreased model performance (AUC 0.71). For optimizing models for preoperative use, models were developed to first predict node ratio or LOS, and these predicted values were inputted for 90-day mortality prediction (AUC of 0.73-0.74). AutoML performed well in predicting 90-day mortality in a larger cohort of gastric cancer patients that underwent gastrectomy. These models can be implemented preoperatively to inform prognostication and patient selection for surgery. Our study supports broader evaluation and application of AutoML to guide surgical oncologic care.

Validation of the AJCC 8th Edition Staging System for Disseminated Appendiceal Cancer Patients Treated with Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy: A Multi-institutional Analysis.

BACKGROUND: The AJCC 8th edition stratifies stage IV disseminated appendiceal cancer (dAC) patients based on grade and pathology. This study was designed to externally validate the staging system and to identify predictors of long-term survival. METHODS: A 12-institution cohort of dAC patients treated with CRS ± HIPEC was retrospectively analyzed. Overall survival (OS) and recurrence-free survival (RFS) were analyzed by using Kaplan-Meier and log-rank tests. Univariate and multivariate cox-regression was performed to assess factors associated with OS and RFS. RESULTS: Among 1009 patients, 708 had stage IVA and 301 had stage IVB disease. Median OS (120.4 mo vs. 47.2 mo) and RFS (79.3 mo vs. 19.8 mo) was significantly higher in stage IVA compared with IVB patients (p < 0.0001). RFS was greater among IVA-M1a (acellular mucin only) than IV M1b/G1 (well-differentiated cellular dissemination) patients (NR vs. 64 mo, p = 0.0004). Survival significantly differed between mucinous and nonmucinous tumors (OS 106.1 mo vs. 41.0 mo; RFS 46.7 mo vs. 21.2 mo, p < 0.05), and OS differed between well, moderate, and poorly differentiated (120.4 mo vs. 56.3 mo vs. 32.9 mo, p < 0.05). Both stage and grade were independent predictors of OS and RFS on multivariate analysis. Acellular mucin and mucinous histology were associated with better OS and RFS on univariate analysis only. CONCLUSIONS: AJCC 8th edition performed well in predicting outcomes in this large cohort of dAC patients treated with CRS ± HIPEC. Separation of stage IVA patients based on the presence of acellular mucin improved prognostication, which may inform treatment and long-term, follow-up strategies.

Severity of Prior COVID-19 Infection is Associated with Postoperative Outcomes Following Major Inpatient Surgery.

Objective: To determine the association between severity of prior history of SARS-CoV-2 infection and postoperative outcomes following major elective inpatient surgery. Summary Background Data: Surgical guidelines instituted early in the COVID-19 pandemic recommended delay in surgery up to 8 weeks following an acute SARS-CoV-2 infection. Given that surgical delay can lead to worse medical outcomes, it is unclear if continuation of such stringent policies is necessary and beneficial for all patients, especially those recovering from asymptomatic or mildly symptomatic COVID-19. Methods: Utilizing the National Covid Cohort Collaborative (N3C), we assessed postoperative outcomes for adults with and without a history of COVID-19 who underwent major elective inpatient surgery between January 2020 and February 2023. COVID-19 severity and time from SARS-CoV-2 infection to surgery were each used as independent variables in multivariable logistic regression models. Results: This study included 387,030 patients, of which 37,354 (9.7%) had a diagnosis of preoperative COVID-19. History of COVID-19 was found to be an independent risk factor for adverse postoperative outcomes even after a 12-week delay for patients with moderate and severe SARS-CoV-2 infection. Patients with mild COVID-19 did not have an increased risk of adverse postoperative outcomes at any time point. Vaccination decreased the odds of mortality and other complications. Conclusions: Impact of COVID-19 on postoperative outcomes is dependent on severity of illness, with only moderate and severe disease leading to higher risk of adverse outcomes. Existing wait time policies should be updated to include consideration of COVID-19 disease severity and vaccination status.

Patterns of Unnecessary Insurer Prior Authorization Denials in a Complex Surgical Oncology Practice.

INTRODUCTION: Insurance prior authorization (PA) is a determination of need, required by a health insurer for an ordered test/procedure. If the test/procedure is denied, a peer-to-peer (P2P) discussion between ordering provider and payer is used to appeal the decision. The objective of this study was to measure the number and patterns of unnecessary PA denials. METHODS: This was a retrospective review at a quaternary cancer center from October 2021 to March 2022. Included were all patients with outpatient imaging orders for surgical planning or surveillance of gastrointestinal, endocrine, or skin cancer. Primary outcome was unnecessary initial denial (UID) defined as an order that required preauthorization, was initially denied by the insurer, and subsequently overturned by P2P. RESULTS: Nine hundred fifty seven orders were placed and 419 required PA (44%). Of tests requiring authorization, 55/419 (13.1%) were denied. Variability in the likelihood of initial denial was seen across insurers, ranging from 0% to 57%. Following P2P, 32/55 were overturned (58.2% UID). The insurers most likely to have a UID were Aetna (100%), Anthem (77.8%), and Cigna (50.0%). UID was most common for gastrointestinal (58.9%) and endocrine (58.3%) cancers. Average P2P was 33.5 min (interquartile range 28-40). CONCLUSIONS: The majority of imaging studies initially denied were overturned after P2P. If all UIDs were eliminated, this would represent 108 less P2P discussions with an estimated time-savings of 60.3 h annually within a high-volume surgical oncology practice. Combined personnel costs to the health systems and stress on patients with cancer due to image-associated PAs and P2P appear hard to justify.