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Article ; Online: The Future of AI in Ovarian Cancer Research: The Large Language Models Perspective.

Laios, Alexandros / Theophilou, Georgios / De Jong, Diederick / Kalampokis, Evangelos

Cancer control : journal of the Moffitt Cancer Center

2023 Volume 30, Page(s) 10732748231197915

Abstract: Conversational large language model (LLM)-based chatbots utilize neural networks to process natural language. By generating highly sophisticated outputs from contextual input text, they revolutionize the access to further learning, leading to the ... ...

Abstract	Conversational large language model (LLM)-based chatbots utilize neural networks to process natural language. By generating highly sophisticated outputs from contextual input text, they revolutionize the access to further learning, leading to the development of new skills and personalized interactions. Although they are not developed to provide healthcare, their potential to address biomedical issues is rather unexplored. Healthcare digitalization and documentation of electronic health records is now developing into a standard practice. Developing tools to facilitate clinical review of unstructured data such as LLMs can derive clinical meaningful insights for ovarian cancer, a heterogeneous but devastating disease. Compared to standard approaches, they can host capacity to condense results and optimize analysis time. To help accelerate research in biomedical language processing and improve the validity of scientific writing, task-specific and domain-specific language models may be required. In turn, we propose a bespoke, proprietary ovarian cancer-specific natural language using solely in-domain text, whereas transfer learning drifts away from the pretrained language models to fine-tune task-specific models for all possible downstream applications. This venture will be fueled by the abundance of unstructured text information in the electronic health records resulting in ovarian cancer research ultimately reaching its linguistic home.
MeSH term(s)	Humans ; Female ; Ovarian Neoplasms/diagnosis ; Language ; Communication ; Electronic Health Records
Language	English
Publishing date	2023-08-25
Publishing country	United States
Document type	Editorial
ZDB-ID	1328503-8
ISSN	1526-2359 ; 1073-2748
ISSN (online)	1526-2359
ISSN	1073-2748
DOI	10.1177/10732748231197915
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Article: Breast Cancer Patients at Increased Risk of Developing Type II Endometrial Cancer: Relative and Absolute Risk Estimation and Implications for Counseling.

Portela, Sara / Cunningham, Aimee / Laios, Alexandros / Hutson, Richard / Theophilou, Georgios

Cureus

2021 Volume 13, Issue 1, Page(s) e12981

Abstract: Introduction Breast cancer (BC) is a recognized risk factor for endometrial cancer (EC). Emerging literature indicates that it confers a higher risk of type II EC (T2EC) than type I EC (T1EC). Although some surgeons offer a prophylactic hysterectomy to ... ...

Abstract	Introduction Breast cancer (BC) is a recognized risk factor for endometrial cancer (EC). Emerging literature indicates that it confers a higher risk of type II EC (T2EC) than type I EC (T1EC). Although some surgeons offer a prophylactic hysterectomy to BC patients referred for risk-reducing bilateral salpingo-oophorectomy, insufficient evidence prevents this from being the standard practice. We aimed to quantify their absolute risk and relative risk (RR) of developing both EC subtypes and identify a higher-risk group that could be considered for prophylactic hysterectomy. Methodology This retrospective service evaluation compared patients diagnosed with BC between 2008 and 2014, who subsequently developed EC within 10 years to those who did not. Absolute risk and RR were calculated using the numbers of regional BC and EC cases within this group, alongside 2009 UK female population and EC incidence statistics. Binary logistic regression generated adjusted odds ratios (ORs) for patient- and disease-specific variables. Results A total of 45 BC patients developed EC, 24 had T1EC and 21 had T2EC. Their RR of developing EC was greater than that of the general population (RR: 12.44, p < 0.0001). Notably, this was higher for T2EC (RR: 33.96, p < 0.001) than T1EC (RR: 8.63, p < 0.0001). Nonetheless, the absolute risk remained low. Tamoxifen exposure was significantly more prevalent among T2EC patients (adjusted OR: 79.61, p = 0.003). Increased age at BC diagnosis was associated with T1EC (adjusted OR: 1.10, p = 0.043) and T2EC (adjusted OR: 1.13, p = 0.03). Neither smoking status nor family history of BC was significantly associated with any outcome. Conclusion Women with BC were more likely to develop T2EC than T1EC, and although the absolute risk was low, the cumulative risk was substantial enough to warrant vigilance. Tamoxifen exposure was significantly predictive of EC, particularly T2EC, and might facilitate risk estimation. Older women at BC diagnosis who receive tamoxifen treatment should be screened and closely monitored for EC. However, given the limitations of normal screening methods for the detection of T2EC, counseling for a prophylactic hysterectomy should also be considered. Clarification of the menopausal status will help make more meaningful recommendations.
Language	English
Publishing date	2021-01-29
Publishing country	United States
Document type	Journal Article
ZDB-ID	2747273-5
ISSN	2168-8184
ISSN	2168-8184
DOI	10.7759/cureus.12981
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Article ; Online: RoBERTa-Assisted Outcome Prediction in Ovarian Cancer Cytoreductive Surgery Using Operative Notes.

Laios, Alexandros / Kalampokis, Evangelos / Mamalis, Marios Evangelos / Tarabanis, Constantine / Nugent, David / Thangavelu, Amudha / Theophilou, Georgios / De Jong, Diederick

Cancer control : journal of the Moffitt Cancer Center

2023 Volume 30, Page(s) 10732748231209892

Abstract: Introduction: Contemporary efforts to predict surgical outcomes focus on the associations between traditional discrete surgical risk factors. We aimed to determine whether natural language processing (NLP) of unstructured operative notes improves the ... ...

Abstract	Introduction: Contemporary efforts to predict surgical outcomes focus on the associations between traditional discrete surgical risk factors. We aimed to determine whether natural language processing (NLP) of unstructured operative notes improves the prediction of residual disease in women with advanced epithelial ovarian cancer (EOC) following cytoreductive surgery. Methods: Electronic Health Records were queried to identify women with advanced EOC including their operative notes. The Term Frequency - Inverse Document Frequency (TF-IDF) score was used to quantify the discrimination capacity of sequences of words (n-grams) regarding the existence of residual disease. We employed the state-of-the-art RoBERTa-based classifier to process unstructured surgical notes. Discrimination was measured using standard performance metrics. An XGBoost model was then trained on the same dataset using both discrete and engineered clinical features along with the probabilities outputted by the RoBERTa classifier. Results: The cohort consisted of 555 cases of EOC cytoreduction performed by eight surgeons between January 2014 and December 2019. Discrete word clouds weighted by n-gram TF-IDF score difference between R0 and non-R0 resection were identified. The words 'adherent' and 'miliary disease' best discriminated between the two groups. The RoBERTa model reached high evaluation metrics (AUROC .86; AUPRC .87, precision, recall, and F1 score of .77 and accuracy of .81). Equally, it outperformed models that used discrete clinical and engineered features and outplayed the performance of other state-of-the-art NLP tools. When the probabilities from the RoBERTa classifier were combined with commonly used predictors in the XGBoost model, a marginal improvement in the overall model's performance was observed (AUROC and AUPRC of .91, with all other metrics the same). Conclusion/implications: We applied a sui generis approach to extract information from the abundant textual surgical data and demonstrated how it can be effectively used for classification prediction, outperforming models relying on conventional structured data. State-of-art NLP applications in biomedical texts can improve modern EOC care.
MeSH term(s)	Humans ; Female ; Cytoreduction Surgical Procedures ; Machine Learning ; Electronic Health Records ; Natural Language Processing ; Carcinoma, Ovarian Epithelial/surgery ; Ovarian Neoplasms/surgery
Language	English
Publishing date	2023-10-17
Publishing country	United States
Document type	Journal Article
ZDB-ID	1328503-8
ISSN	1526-2359 ; 1073-2748
ISSN (online)	1526-2359
ISSN	1073-2748
DOI	10.1177/10732748231209892
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: "Learning from the experts" - a novel advanced cadaveric course for Gynaecological Oncology (GO) Cytoreductive Surgery.

Sideris, M / Elshaer, A M / Johnson, R L / Kotwal, S / Mehta, S / Quyn, A / Saunders, R / Tiernan, J / Upasani, V / Theophilou, G

Facts, views & vision in ObGyn

2022 Volume 14, Issue 3, Page(s) 265–273

Abstract: Background: Ovarian cancer cytoreductive surgery necessitates the use of advanced Simulation-Based Learning (SBL) to optimise skill-based teaching and achieve technical proficiency.: Objective: We describe and appraise the role of a novel ... ...

Abstract	Background: Ovarian cancer cytoreductive surgery necessitates the use of advanced Simulation-Based Learning (SBL) to optimise skill-based teaching and achieve technical proficiency. Objective: We describe and appraise the role of a novel postgraduate cadaveric course for cytoreductive surgery for advanced ovarian/fallopian tube or primary peritoneal cancer. Materials and methods: Several consultant-level surgeons with expertise in upper gastrointestinal, colorectal, hepatobiliary and urological surgery, were invited to teach their counterpart gynaecological oncology (GO) surgeons. The 2-day course curriculum involved advanced dissections on thiel-embalmed cadavers. All dissections included applicable steps required during GO cytoreductive surgeries. Outcome measures: We used a feedback questionnaire and structured interviews to capture trainers and delegates views respectively. Results: All delegates reported a positive educational experience and improvement of knowledge in all course components. There was no difference in the perception of feedback across junior versus senior consultants. Trainers perceived this opportunity as a "2-way learning" whether they got to explore in depth the GO perspective in how and which of their skills are applicable during cytoreductive surgery. Conclusions: Collaborating with other surgical specialities promotes a "learning from the experts" concept and has potential to meet the rapidly increased demand for multi-viscera surgical excellence in GO surgery. What’s new?: The concept of involving experts from other surgical disciplines in advanced cadaveric courses for cytoreductive surgery in ovarian cancer, will solidify the effort to achieve excellence in the GO training. Such courses can be essential educational adjunct for most GO fellowships.
Language	English
Publishing date	2022-11-03
Publishing country	Belgium
Document type	Journal Article
ZDB-ID	2701574-9
ISSN	2032-0418 ; 2684-4230
ISSN	2032-0418 ; 2684-4230
DOI	10.52054/FVVO.14.3.036
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Article: Barriers to Immunotherapy in Ovarian Cancer: Metabolic, Genomic, and Immune Perturbations in the Tumour Microenvironment.

Johnson, Racheal Louise / Cummings, Michele / Thangavelu, Amudha / Theophilou, Georgios / de Jong, Diederick / Orsi, Nicolas Michel

Cancers

2021 Volume 13, Issue 24

Abstract: A lack of explicit early clinical signs and effective screening measures mean that ovarian cancer (OC) often presents as advanced, incurable disease. While conventional treatment combines maximal cytoreductive surgery and platinum-based chemotherapy, ... ...

Abstract	A lack of explicit early clinical signs and effective screening measures mean that ovarian cancer (OC) often presents as advanced, incurable disease. While conventional treatment combines maximal cytoreductive surgery and platinum-based chemotherapy, patients frequently develop chemoresistance and disease recurrence. The clinical application of immune checkpoint blockade (ICB) aims to restore anti-cancer T-cell function in the tumour microenvironment (TME). Disappointingly, even though tumour infiltrating lymphocytes are associated with superior survival in OC, ICB has offered limited therapeutic benefits. Herein, we discuss specific TME features that prevent ICB from reaching its full potential, focussing in particular on the challenges created by immune, genomic and metabolic alterations. We explore both recent and current therapeutic strategies aiming to overcome these hurdles, including the synergistic effect of combination treatments with immune-based strategies and review the status quo of current clinical trials aiming to maximise the success of immunotherapy in OC.
Language	English
Publishing date	2021-12-11
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2527080-1
ISSN	2072-6694
ISSN	2072-6694
DOI	10.3390/cancers13246231
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Article ; Online: Survival and Chemosensitivity in Advanced High Grade Serous Epithelial Ovarian Cancer Patients with and without a BRCA Germline Mutation: More Evidence for Shifting the Paradigm towards Complete Surgical Cytoreduction.

De Jong, Diederick / Otify, Mohamed / Chen, Inga / Jackson, David / Jayasinghe, Kelum / Nugent, David / Thangavelu, Amudha / Theophilou, Georgios / Laios, Alexandros

Medicina (Kaunas, Lithuania)

2022 Volume 58, Issue 11

Abstract: Background and Objectives: Approximately 10−15% of high-grade serous ovarian cancer (HGSOC) cases are related to BRCA germline mutations. Better survival rates and increased chemosensitivity are reported in patients with a BRCA 1/2 germline mutation. ... ...

Abstract	Background and Objectives: Approximately 10−15% of high-grade serous ovarian cancer (HGSOC) cases are related to BRCA germline mutations. Better survival rates and increased chemosensitivity are reported in patients with a BRCA 1/2 germline mutation. However, the FIGO stage and histopathological entity may have been confounding factors. This study aimed to compare chemotherapy response and survival between patients with and without a BRCA 1/2 germline mutation in advanced HGSOC receiving neoadjuvant chemotherapy (NACT). Materials and Methods: A cohort of BRCA-tested advanced HGSOC patients undergoing cytoreductive surgery following NACT was analyzed for chemotherapy response and survival. Neoadjuvant chemotherapy served as a vehicle to assess chemotherapy response on biochemical (CA125), histopathological (CRS), biological (dissemination), and surgical (residual disease) levels. Univariate and multivariate analyses for chemotherapy response and survival were utilized. Results: Thirty-nine out of 168 patients had a BRCA ½ germline mutation. No differences in histopathological chemotherapy response between the patients with and without a BRCA ½ germline mutation were observed. Survival in the groups of patients was comparable Irrespective of the BRCA status, CRS 2 and 3 (HR 7.496, 95% CI 2.523−22.27, p < 0.001 & HR 4.069, 95% CI 1.388−11.93, p = 0.011), and complete surgical cytoreduction (p = 0.017) were independent parameters for a favored overall survival. Conclusions: HGSOC patients with or without BRCA ½ germline mutations, who had cytoreductive surgery, showed comparable chemotherapy responses and subsequent survival. Irrespective of BRCA status, advanced-stage HGSOC patients have a superior prognosis with complete surgical cytoreduction and good histopathological response to chemotherapy.
MeSH term(s)	Humans ; Female ; Cytoreduction Surgical Procedures ; Carcinoma, Ovarian Epithelial/drug therapy ; Carcinoma, Ovarian Epithelial/genetics ; Carcinoma, Ovarian Epithelial/surgery ; Germ-Line Mutation ; Ovarian Neoplasms/drug therapy ; Ovarian Neoplasms/genetics ; Ovarian Neoplasms/surgery ; Cystadenocarcinoma, Serous/drug therapy ; Cystadenocarcinoma, Serous/genetics ; Cystadenocarcinoma, Serous/surgery ; Neoadjuvant Therapy ; Retrospective Studies
Language	English
Publishing date	2022-11-08
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2188113-3
ISSN	1648-9144 ; 1010-660X
ISSN (online)	1648-9144
ISSN	1010-660X
DOI	10.3390/medicina58111611
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Article ; Online: Predicting complete cytoreduction for advanced ovarian cancer patients using nearest-neighbor models.

Laios, Alexandros / Gryparis, Alexandros / DeJong, Diederick / Hutson, Richard / Theophilou, Georgios / Leach, Chris

Journal of ovarian research

2020 Volume 13, Issue 1, Page(s) 117

Abstract: Background: The foundation of modern ovarian cancer care is cytoreductive surgery to remove all macroscopic disease (R0). Identification of R0 resection patients may help individualise treatment. Machine learning and AI have been shown to be effective ... ...

Abstract	Background: The foundation of modern ovarian cancer care is cytoreductive surgery to remove all macroscopic disease (R0). Identification of R0 resection patients may help individualise treatment. Machine learning and AI have been shown to be effective systems for classification and prediction. For a disease as heterogenous as ovarian cancer, they could potentially outperform conventional predictive algorithms for routine clinical use. We investigated the performance of an AI system, the k-nearest neighbor (k-NN) classifier, to predict R0, comparing it with logistic regression. Patients diagnosed with advanced stage, high grade serous ovarian, tubal and primary peritoneal cancer, undergoing surgical cytoreduction from 2015 to 2019, was selected from the ovarian database. Performance variables included age, BMI, Charlson Comorbidity Index, timing of surgery, surgical complexity and disease score. The k-NN algorithm classified R0 vs non-R0 patients using 3-20 nearest neighbors. Prediction accuracy was estimated as percentage of observations in the training set correctly classified. Results: 154 patients were identified, with mean age of 64.4 + 10.5 yrs., BMI of 27.2 + 5.8 and mean SCS of 3 + 1 (1-8). Complete and optimal cytoreduction was achieved in 62 and 88% patients. The mean predictive accuracy was 66%. R0 resection prediction of true negatives was as high as 90% using k = 20 neighbors. Conclusions: The k-NN algorithm is a promising and versatile tool for R0 resection prediction. It slightly outperforms logistic regression and is expected to improve accuracy with data expansion.
MeSH term(s)	Artificial Intelligence/standards ; Cytoreduction Surgical Procedures/methods ; Female ; Humans ; Machine Learning/standards ; Middle Aged ; Ovarian Neoplasms/surgery
Language	English
Publishing date	2020-09-29
Publishing country	England
Document type	Journal Article
ZDB-ID	2455679-8
ISSN	1757-2215 ; 1757-2215
ISSN (online)	1757-2215
ISSN	1757-2215
DOI	10.1186/s13048-020-00700-0
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Article: Explaining the Elusive Nature of a Well-Defined Threshold for Blood Transfusion in Advanced Epithelial Ovarian Cancer Cytoreductive Surgery.

Laios, Alexandros / Kalampokis, Evangelos / Mamalis, Marios-Evangelos / Thangavelu, Amudha / Tan, Yong Sheng / Hutson, Richard / Munot, Sarika / Broadhead, Tim / Nugent, David / Theophilou, Georgios / Jackson, Robert-Edward / De Jong, Diederick

Diagnostics (Basel, Switzerland)

2023 Volume 14, Issue 1

Abstract: There is no well-defined threshold for intra-operative blood transfusion (BT) in advanced epithelial ovarian cancer (EOC) surgery. To address this, we devised a Machine Learning (ML)-driven prediction algorithm aimed at prompting and elucidating a ... ...

Abstract	There is no well-defined threshold for intra-operative blood transfusion (BT) in advanced epithelial ovarian cancer (EOC) surgery. To address this, we devised a Machine Learning (ML)-driven prediction algorithm aimed at prompting and elucidating a communication alert for BT based on anticipated peri-operative events independent of existing BT policies. We analyzed data from 403 EOC patients who underwent cytoreductive surgery between 2014 and 2019. The estimated blood volume (EBV), calculated using the formula EBV = weight × 80, served for setting a 10% EBV threshold for individual intervention. Based on known estimated blood loss (EBL), we identified two distinct groups. The Receiver operating characteristic (ROC) curves revealed satisfactory results for predicting events above the established threshold (AUC 0.823, 95% CI 0.76-0.88). Operative time (OT) was the most significant factor influencing predictions. Intra-operative blood loss exceeding 10% EBV was associated with OT > 250 min, primary surgery, serous histology, performance status 0, R2 resection and surgical complexity score > 4. Certain sub-procedures including large bowel resection, stoma formation, ileocecal resection/right hemicolectomy, mesenteric resection, bladder and upper abdominal peritonectomy demonstrated clear associations with an elevated interventional risk. Our findings emphasize the importance of obtaining a rough estimate of OT in advance for precise prediction of blood requirements.
Language	English
Publishing date	2023-12-30
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662336-5
ISSN	2075-4418
ISSN	2075-4418
DOI	10.3390/diagnostics14010094
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Article ; Online: Prerequisites to improve surgical cytoreduction in FIGO stage III/IV epithelial ovarian cancer and subsequent clinical ramifications.

de Jong, Diederick / Thangavelu, Amudha / Broadhead, Timothy / Chen, Inga / Burke, Dermot / Hutson, Richard / Johnson, Racheal / Kaufmann, Angelika / Lodge, Peter / Nugent, David / Quyn, Aaron / Theophilou, Georgios / Laios, Alexandros

Journal of ovarian research

2023 Volume 16, Issue 1, Page(s) 214

Abstract: Background: No residual disease (CC 0) following cytoreductive surgery is pivotal for the prognosis of women with advanced stage epithelial ovarian cancer (EOC). Improving CC 0 resection rates without increasing morbidity and no delay in subsequent ... ...

Abstract	Background: No residual disease (CC 0) following cytoreductive surgery is pivotal for the prognosis of women with advanced stage epithelial ovarian cancer (EOC). Improving CC 0 resection rates without increasing morbidity and no delay in subsequent chemotherapy favors a better outcome in these women. Prerequisites to facilitate this surgical paradigm shift and subsequent ramifications need to be addressed. This quality improvement study assessed 559 women with advanced EOC who had cytoreductive surgery between January 2014 and December 2019 in our tertiary referral centre. Following implementation of the Enhanced Recovery After Surgery (ERAS) pathway and prehabilitation protocols, the surgical management paradigm in advanced EOC patients shifted towards maximal surgical effort cytoreduction in 2016. Surgical outcome parameters before, during, and after this paradigm shift were compared. The primary outcome measure was residual disease (RD). The secondary outcome parameters were postoperative morbidity, operative time (OT), length of stay (LOS) and progression-free-survival (PFS). Results: R0 resection rate in patients with advanced EOC increased from 57.3% to 74.4% after the paradigm shift in surgical management whilst peri-operative morbidity and delays in adjuvant chemotherapy were unchanged. The mean OT increased from 133 + 55 min to 197 + 85 min, and postoperative high dependency/intensive care unit (HDU/ICU) admissions increased from 8.1% to 33.1%. The subsequent mean LOS increased from 7.0 + 2.6 to 8.4 + 4.9 days. The median PFS was 33 months. There was no difference for PFS in the three time frames but a trend towards improvement was observed. Conclusions: Improved CC 0 surgical cytoreduction rates without compromising morbidity in advanced EOC is achievable owing to the right conditions. Maximal effort cytoreductive surgery should solely be carried out in high output tertiary referral centres due to the associated substantial prerequisites and ramifications.
MeSH term(s)	Humans ; Female ; Carcinoma, Ovarian Epithelial/drug therapy ; Ovarian Neoplasms/pathology ; Cytoreduction Surgical Procedures/methods ; Prognosis ; Chemotherapy, Adjuvant ; Retrospective Studies ; Neoplasm Staging
Language	English
Publishing date	2023-11-11
Publishing country	England
Document type	Journal Article
ZDB-ID	2455679-8
ISSN	1757-2215 ; 1757-2215
ISSN (online)	1757-2215
ISSN	1757-2215
DOI	10.1186/s13048-023-01303-1
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Article: Development of a Novel Intra-Operative Score to Record Diseases' Anatomic Fingerprints (ANAFI Score) for the Prediction of Complete Cytoreduction in Advanced-Stage Ovarian Cancer by Using Machine Learning and Explainable Artificial Intelligence.

Laios, Alexandros / Kalampokis, Evangelos / Johnson, Racheal / Munot, Sarika / Thangavelu, Amudha / Hutson, Richard / Broadhead, Tim / Theophilou, Georgios / Nugent, David / De Jong, Diederick

Cancers

2023 Volume 15, Issue 3

Abstract: Background: The Peritoneal Carcinomatosis Index (PCI) and the Intra-operative Mapping for Ovarian Cancer (IMO), to a lesser extent, have been universally validated in advanced-stage epithelial ovarian cancer (EOC) to describe the extent of peritoneal ... ...

Abstract	Background: The Peritoneal Carcinomatosis Index (PCI) and the Intra-operative Mapping for Ovarian Cancer (IMO), to a lesser extent, have been universally validated in advanced-stage epithelial ovarian cancer (EOC) to describe the extent of peritoneal dissemination and are proven to be powerful predictors of the surgical outcome with an added sensitivity of assessment at laparotomy of around 70%. This leaves room for improvement because the two-dimensional anatomic scoring model fails to reflect the patient's real anatomy, as seen by a surgeon. We hypothesized that tumor dissemination in specific anatomic locations can be more predictive of complete cytoreduction (CC0) and survival than PCI and IMO tools in EOC patients. (2) Methods: We analyzed prospectively data collected from 508 patients with FIGO-stage IIIB-IVB EOC who underwent cytoreductive surgery between January 2014 and December 2019 at a UK tertiary center. We adapted the structured ESGO ovarian cancer report to provide detailed information on the patterns of tumor dissemination (cancer anatomic fingerprints). We employed the extreme gradient boost (XGBoost) to model only the variables referring to the EOC disseminated patterns, to create an intra-operative score and judge the predictive power of the score alone for complete cytoreduction (CC0). Receiver operating characteristic (ROC) curves were then used for performance comparison between the new score and the existing PCI and IMO tools. We applied the Shapley additive explanations (SHAP) framework to support the feature selection of the narrated cancer fingerprints and provide global and local explainability. Survival analysis was performed using Kaplan-Meier curves and Cox regression. (3) Results: An intra-operative disease score was developed based on specific weights assigned to the cancer anatomic fingerprints. The scores range from 0 to 24. The XGBoost predicted CC0 resection (area under curve (AUC) = 0.88 CI = 0.854-0.913) with high accuracy. Organ-specific dissemination on the small bowel mesentery, large bowel serosa, and diaphragmatic peritoneum were the most crucial features globally. When added to the composite model, the novel score slightly enhanced its predictive value (AUC = 0.91, CI = 0.849-0.963). We identified a "turning point", ≤5, that increased the probability of CC0. Using conventional logistic regression, the new score was superior to the PCI and IMO scores for the prediction of CC0 (AUC = 0.81 vs. 0.73 and 0.67, respectively). In multivariate Cox analysis, a 1-point increase in the new intra-operative score was associated with poorer progression-free (HR: 1.06; 95% CI: 1.03-1.09,
Language	English
Publishing date	2023-02-03
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2527080-1
ISSN	2072-6694
ISSN	2072-6694
DOI	10.3390/cancers15030966
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