Tumor Burden Score and Serum Alpha-fetoprotein Subclassify Intermediate-Stage Hepatocellular Carcinoma

Lima, Henrique A.; Endo, Yutaka; Alaimo, Laura; Moazzam, Zorays; Munir, Muhammad Musaab; Shaikh, Chanza; Resende, Vívian; Guglielmi, Alfredo; Cauchy, François; Lam, Vincent; Poultsides, George A.; Popescu, Irinel; Alexandrescu, Sorin; Martel, Guillaume; Hugh, Tom; Endo, Itaru; Kitago, Minoru; Shen, Feng; Pawlik, Timothy M.

doi:10.1007/s11605-022-05469-9

Cited by 10 publications

(11 citation statements)

References 39 publications

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“…32 Therefore, some studies investigated the synergetic impact of morphological characteristics and biological characteristics (tumor markers, e.g., AFP) on long-term outcomes of HCC patients after hepatectomy and demonstrated the good performance of the combination of TBS and AFP in stratifying the OS of HCC patients after liver resection. 12,18,19,33,34 relationship between the synergetic impact of combining TBS and PIVKA-II and survival outcomes of AFPnegative HCC patients within BCLC 0/A/B after liver resection. We established a simple prognostic scoring model (TPS model) based on TBS and preoperative PIVKA-II levels and demonstrated that the TPS model had excellent performance in stratifying OS and early recurrence in AFP-negative HCC patients after liver resection.…”

Section: Discussionmentioning

confidence: 99%

“…[15][16][17] Thus, whether combine AFP and TBS could predict survival outcomes of HCC patients after liver resection is an interesting topic, and several studies have demonstrated the synergistic effect of both in predicting the postoperative prognosis of HCC patients. 12,[18][19][20] However, it is worth noting that AFP (at a threshold level of 20 ng/mL) has a limited ability to detect HCC (sensitivity of 40%-60% with specificity of 80-90%), which means that a proportion of HCC patients have negative AFP levels (< 20 ng/mL) at diagnosis, especially for small HCC patients. 21 Protein induced by vitamin K deficiency II (PIVKA-II) was identified for the first time in 1968 and was demonstrated to be a potential biomarker supplementing AFP for the diagnosis of HCC in many studies.…”

Section: Introductionmentioning

confidence: 99%

“…Serum α‐fetoprotein (AFP) is a typical tumor biomarker of HCC and plays a crucial function in monitoring, diagnosing, and determining the prognosis of HCC patients 15–17 . Thus, whether combine AFP and TBS could predict survival outcomes of HCC patients after liver resection is an interesting topic, and several studies have demonstrated the synergistic effect of both in predicting the postoperative prognosis of HCC patients 12,18–20 …”

Section: Introductionmentioning

confidence: 99%

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PIVKA‐II combined with tumor burden score to predict long‐term outcomes of AFP‐negative hepatocellular carcinoma patients after liver resection

Qiu,

Wu,

et al. 2023

Cancer Medicine

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BackgroundThis study aimed to establish a simple prognostic scoring model based on tumor burden score (TBS) and PIVKA‐II to predict long‐term outcomes of α‐fetoprotein (AFP)‐negative hepatocellular carcinoma (HCC) patients.Methods511 patients were divided into the training cohort (n = 305) and the validation cohort (n = 206) at a ratio of 6:4. Receiver operating characteristic curves (ROC) were established to identify cutoff values of TBS and PIVKA‐II. Kaplan–Meier curves were used to analyze survival outcomes. The multivariable Cox regression was used to identify variables independently associated with survival outcomes. The predictive performance of the TBS‐PIVKA II score (TPS) model was compared with Barcelona clinic liver cancer (BCLC) stage and American Joint Committee on Cancer (AJCC TNM) stage.ResultsThe present study established the TPS model using a simple scoring system (0, 1 for low/high TBS [cutoff value: 4.1]; 0, 1 for low/high PIVKA‐II [cutoff value: 239 mAU/mL]). The TPS scoring model was divided into three levels according to the summation of TBS score and PIVKA‐II score: TPS 0, TPS 1, and TPS 2. The TPS scoring model was able to stratify OS (training: p < 0.001, validation: p < 0.001) and early recurrence (training: p < 0.001; validation: p = 0.001) in the training cohort and the validation cohort. The TPS score was independently associated with OS (TPS 1 vs. 0, HR: 2.28, 95% CI: 1.01–5.17; TPS 2 vs. 0, HR: 4.21, 95% CI: 2.01–8.84) and early recurrence (TPS 1 vs. 0, HR: 3.50, 95% CI: 1.71–7.16; TPS 2 vs. 0, HR: 3.79, 95% CI: 1.86–7.75) in the training cohort. The TPS scoring model outperformed BCLC stage and AJCC TNM stage in predicting OS and early recurrence in the training cohort and the validation cohort. But the TPS scoring model was unable to stratify the late recurrence in the training cohort (p = 0.872) and the validation cohort (p = 0.458).ConclusionsThe TPS model outperformed the BCLC stage and AJCC TNM stage in predicting OS and early recurrence of AFP‐negative HCC patients after liver resection, which might better assist surgeons in screening AFP‐negative HCC patients who may benefit from liver resection.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PIVKA‐II combined with tumor burden score to predict long‐term outcomes of AFP‐negative hepatocellular carcinoma patients after liver resection

Qiu,

Wu,

et al. 2023

Cancer Medicine

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“…Preoperative variables of interest included sex, age, BMI, liver disease etiology (i.e., hepatitis B virus [HBV], hepatitis C virus [HCV], non‐alcoholic steatohepatitis [NASH] and other), presence of cirrhosis, platelet count (PLT), liver function (i.e., albumin‐bilirubin [ALBI] grade) [13], inflammation based‐scores (prognostic nutritional index [PNI]), immune status indices (i.e., SII: Platelet count x neutrophil count: lymphocyte count, NLR, PLR), and tumor‐related indicators (largest tumor size, number, AFP). Tumor burden score (TBS) was calculated by applying the Pythagorean theorem so that TBS 2 = (maximum tumor diameter) 2 + (number of tumors) 2 , where tumor diameter and number of tumors were extracted based on preoperative imaging data to allow for a cluster analysis based on preoperative factors [14]. Barcelona Clinic Liver Cancer (BCLC) staging was defined as very early, early, intermediate and advanced, based on the latest BCLC classification [15].…”

Section: Methodsmentioning

confidence: 99%

“…Tumor burden score (TBS) was calculated by applying the Pythagorean theorem so that TBS 2 = (maximum tumor diameter) 2 ? (number of tumors) 2 , where tumor diameter and number of tumors were extracted based on preoperative imaging data to allow for a cluster analysis based on preoperative factors [14]. Barcelona Clinic Liver Cancer (BCLC) staging was defined as very early, early, intermediate and advanced, based on the latest BCLC classification [15].…”

Section: Variables and Definitionsmentioning

confidence: 99%

Machine‐Based Learning Hierarchical Cluster Analysis: Sex‐Based Differences in Prognosis Following Resection of Hepatocellular Carcinoma

Resende,

Tsilimigras,

Endo

et al. 2023

World j. surg.

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Background Patients with hepatocellular carcinoma (HCC) may have a heterogeneous presentation, as well as different long-term outcomes following surgical resection. We sought to use machine learning to cluster patients into different prognostic groups based on preoperative characteristics. Methods Patients who underwent curative-intent liver resection for HCC between 2000 and 2020 were identified from a large international multi-institutional database. A hierarchical cluster analysis was performed based on preoperative factors to characterize patterns of presentation and define disease-free survival (DFS). Results Among 966 with HCC, 3 distinct clusters were identified: Cluster 1 (n = 160, 16.5%), Cluster 2 (n = 537, 55.6%) and Cluster 3 (n = 269, 27.8%). Cluster 1 (n = 160, 16.5%) consisted of female patients (n = 160, 100%), low inflammation-based scores, intermediate tumor burden score (TBS) (median: 4.71) and high alpha-fetoprotein (AFP) levels (median 41.3 ng/mL); Cluster 2 consisted of male individuals (n = 537, 100%), mainly with a history of HBV infection (n = 429, 79.9%), low inflammation-based scores, intermediate AFP levels (median 26.0 ng/mL) and lower TBS (median 4.49); Cluster 3 was comprised of older patients (median age 68 years) predominantly male (n = 248, 92.2%) who had low incidence of HBV/HCV infection (7.1% and 8.2%, respectively), intermediate AFP levels (median 16.8 ng/mL), high inflammation-based scores and high TBS (median 6.58). Median DFS worsened incrementally among the three different clusters with Cluster 3 having the lowest DFS (Cluster 1: median not reached; Cluster 2: 34 months, 95% CI 23.0-48.0, Cluster 3: 19 months, 95% CI 15.0-29.0, p \ 0.05). Conclusion Cluster analysis classified HCC patients into three distinct prognostic groups. Cluster assignment predicted DFS following resection of HCC with the female cluster having the most favorable prognosis following HCC resection.Vivian Resende and Diamantis I. Tsilimigras have contributed equally as co-first authors.

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Using the hazard function to evaluate hepatocellular carcinoma recurrence risk after curative resection

Li,

Moi,

Liu

et al. 2023

Updates Surg

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Tumor Burden Score and Serum Alpha-fetoprotein Subclassify Intermediate-Stage Hepatocellular Carcinoma

Cited by 10 publications

References 39 publications

PIVKA‐II combined with tumor burden score to predict long‐term outcomes of AFP‐negative hepatocellular carcinoma patients after liver resection

PIVKA‐II combined with tumor burden score to predict long‐term outcomes of AFP‐negative hepatocellular carcinoma patients after liver resection

Machine‐Based Learning Hierarchical Cluster Analysis: Sex‐Based Differences in Prognosis Following Resection of Hepatocellular Carcinoma

Using the hazard function to evaluate hepatocellular carcinoma recurrence risk after curative resection

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