Refining Risk Criteria May Substantially Reduce Unnecessary Additional Surgeries after Local Resection of T1 Colorectal Cancer
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
3. Variables
4. Results
Description of the Samples, Management, and Outcomes
5. Association with LNM
6. Association with Poor Outcome
7. Discussion
8. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Hashiguchi, Y.; Muro, K.; Saito, Y.; Ito, Y.; Ajioka, Y.; Hamaguchi, T.; Hasegawa, K.; Hotta, K.; Ishida, H.; Ishiguro, M.; et al. Japanese Society for Cancer of the Colon and Rectum (JSCCR) Guidelines 2019 for the Treatment of Colorectal Cancer. Int. J. Clin. Oncol. 2020, 25, 1–42. [Google Scholar] [CrossRef] [PubMed]
- Argilés, G.; Tabernero, J.; Labianca, R.; Hochhauser, D.; Salazar, R.; Iveson, T.; Laurent-Puig, P.; Quirke, P.; Yoshino, T.; Taieb, J.; et al. Localised Colon Cancer: ESMO Clinical Practice Guidelines for Diagnosis, Treatment and Follow-Up. Ann. Oncol. 2020, 31, 1291–1305. [Google Scholar] [CrossRef] [PubMed]
- Benson, A.B.; Venook, A.P.; Al-Hawary, M.M.; Azad, N.; Chen, Y.-J.; Ciombor, K.K.; Cooper, H.S.; Deming, D.; Garrido-Laguna, I.; Grem, J.L.; et al. NCCN Guidelines on Colon Cancer Version 2.2023. Available online: https://www.nccn.org/guidelines (accessed on 19 May 2023).
- Ferlitsch, M.; Moss, A.; Hassan, C.; Bhandari, P.; Dumonceau, J.-M.; Paspatis, G.; Jover, R.; Langner, C.; Bronzwaer, M.; Nalankilli, K.; et al. Colorectal Polypectomy and Endoscopic Mucosal Resection (EMR): European Society of Gastrointestinal Endoscopy (ESGE) Clinical Guideline. Endoscopy 2017, 49, 270–297. [Google Scholar] [CrossRef]
- Suh, J.; Han, K.; Kim, B.; Hong, C.; Sohn, D.; Chang, H.; Kim, M.; Park, S.; Park, J.; Choi, H.; et al. Predictors for Lymph Node Metastasis in T1 Colorectal Cancer. Endoscopy 2012, 44, 590–595. [Google Scholar] [CrossRef] [PubMed]
- Park, S.H.; Oh, S.O.; Park, S.S.; Roh, S.J.; Han, K.S.; Kim, B.; Hong, C.W.; Kim, B.C.; Sohn, D.K.; Chang, H.J.; et al. Characteristics of Minute T1 Colorectal Cancer in Relevance to Pathology and Treatment. Ann. Surg. Treat. Res. 2020, 98, 199–205. [Google Scholar] [CrossRef] [PubMed]
- Han, K.S.; Lim, S.W.; Sohn, D.K.; Chang, H.J.; Oh, J.H.; Lee, J.H.; Kim, H.R.; Kim, Y.J. Clinicopathological Characteristics of T1 Colorectal Cancer without Background Adenoma. Color. Dis. 2013, 15, e124–e129. [Google Scholar] [CrossRef] [PubMed]
- Klintrup, K.; Mäkinen, J.M.; Kauppila, S.; Väre, P.O.; Melkko, J.; Tuominen, H.; Tuppurainen, K.; Mäkelä, J.; Karttunen, T.J.; Mäkinen, M.J. Inflammation and Prognosis in Colorectal Cancer. Eur. J. Cancer 2005, 41, 2645–2654. [Google Scholar] [CrossRef] [PubMed]
- Cracco, N.; Todaro, V.; Pedrazzi, G.; Del Rio, P.; Haboubi, N.; Zinicola, R. The Risk of Lymph Node Metastasis in T1 Colorectal Cancer: New Parameters to Assess the Degree of Submucosal Invasion. Int. J. Color. Dis. 2021, 36, 41–45. [Google Scholar] [CrossRef] [PubMed]
- Barresi, V.; Reggiani Bonetti, L.; Ieni, A.; Caruso, R.A.; Tuccari, G. Histological Grading in Colorectal Cancer: New Insights and Perspectives. Histol. Histopathol. 2015, 30, 1059–1067. [Google Scholar] [CrossRef]
- Kim, J.W.; Shin, M.K.; Kim, B.C. Clinicopathologic Impacts of Poorly Differentiated Cluster-Based Grading System in Colorectal Carcinoma. J. Korean Med. Sci. 2015, 30, 16–23. [Google Scholar] [CrossRef]
- Barresi, V.; Bonetti, L.R.; Ieni, A.; Branca, G.; Baron, L.; Tuccari, G. Histologic Grading Based on Counting Poorly Differentiated Clusters in Preoperative Biopsy Predicts Nodal Involvement and PTNM Stage in Colorectal Cancer Patients. Hum. Pathol. 2014, 45, 268–275. [Google Scholar] [CrossRef] [PubMed]
- Konishi, T.; Shimada, Y.; Lee, L.H.; Cavalcanti, M.S.; Hsu, M.; Smith, J.J.; Nash, G.M.; Temple, L.K.; Guillem, J.G.; Paty, P.B.; et al. Poorly Differentiated Clusters Predict Colon Cancer Recurrence. Am. J. Surg. Pathol. 2018, 42, 705–714. [Google Scholar] [CrossRef]
- Ueno, H.; Hase, K.; Hashiguchi, Y.; Shimazaki, H.; Tanaka, M.; Miyake, O.; Masaki, T.; Shimada, Y.; Kinugasa, Y.; Mori, Y.; et al. Site-Specific Tumor Grading System in Colorectal Cancer. Am. J. Surg. Pathol. 2014, 38, 197–204. [Google Scholar] [CrossRef] [PubMed]
- Ueno, H.; Hase, K.; Hashiguchi, Y.; Shimazaki, H.; Yoshii, S.; Kudo, S.; Tanaka, M.; Akagi, Y.; Suto, T.; Nagata, S.; et al. Novel Risk Factors for Lymph Node Metastasis in Early Invasive Colorectal Cancer: A Multi-Institution Pathology Review. J. Gastroenterol. 2014, 49, 1314–1323. [Google Scholar] [CrossRef] [PubMed]
- Ueno, H.; Kajiwara, Y.; Shimazaki, H.; Shinto, E.; Hashiguchi, Y.; Nakanishi, K.; Maekawa, K.; Katsurada, Y.; Nakamura, T.; Mochizuki, H.; et al. New Criteria for Histologic Grading of Colorectal Cancer. Am. J. Surg. Pathol. 2012, 36, 193–201. [Google Scholar] [CrossRef] [PubMed]
- Barresi, V.; Reggiani Bonetti, L.; Branca, G.; Di Gregorio, C.; Ponz de Leon, M.; Tuccari, G. Colorectal Carcinoma Grading by Quantifying Poorly Differentiated Cell Clusters Is More Reproducible and Provides More Robust Prognostic Information than Conventional Grading. Virchows Arch. 2012, 461, 621–628. [Google Scholar] [CrossRef] [PubMed]
- Shivji, S.; Conner, J.R.; Barresi, V.; Kirsch, R. Poorly Differentiated Clusters in Colorectal Cancer: A Current Review and Implications for Future Practice. Histopathology 2020, 77, 351–368. [Google Scholar] [CrossRef] [PubMed]
- MedCalc® Statistical Software Version 20.106. MedCalc Software Ltd.: Ostend, Belgium, 2022. Available online: https://www.medcalc.org (accessed on 23 September 2023).
- R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2021; Available online: https://www.r-project.org/ (accessed on 23 May 2023).
- Lugli, A.; Kirsch, R.; Ajioka, Y.; Bosman, F.; Cathomas, G.; Dawson, H.; El Zimaity, H.; Fléjou, J.F.; Hansen, T.P.; Hartmann, A.; et al. Recommendations for Reporting Tumor Budding in Colorectal Cancer Based on the International Tumor Budding Consensus Conference (ITBCC) 2016. Mod. Pathol. 2017, 30, 1299–1311. [Google Scholar] [CrossRef]
- Quirke, P.; Risio, M.; Lambert, R.; von Karsa, L.; Vieth, M. Quality Assurance in Pathology in Colorectal Cancer Screening and Diagnosis—European Recommendations. Virchows Arch. 2011, 458, 1–19. [Google Scholar] [CrossRef]
- Ueno, H.; Shinto, E.; Kajiwara, Y.; Fukazawa, S.; Shimazaki, H.; Yamamoto, J.; Hase, K. Prognostic Impact of Histological Categorisation of Epithelial–Mesenchymal Transition in Colorectal Cancer. Br. J. Cancer 2014, 111, 2082–2090. [Google Scholar] [CrossRef]
- Zhang, N.; Ng, A.S.; Cai, S.; Li, Q.; Yang, L.; Kerr, D. Novel Therapeutic Strategies: Targeting Epithelial–Mesenchymal Transition in Colorectal Cancer. Lancet Oncol. 2021, 22, e358–e368. [Google Scholar] [CrossRef]
- Morgado-Diaz, J.A.; Wagner, M.S.; Sousa-Squiavinato, A.C.M.; de-Freitas-Junior, J.C.M.; de Araújo, W.M.; Tessmann, J.W.; Rocha, M.R. Epithelial-Mesenchymal Transition in Metastatic Colorectal Cancer. In Gastrointestinal Cancers; Exon Publications: Brisbane, Australia, 2022; pp. 25–42. [Google Scholar] [CrossRef]
- Ronnow, C.F.; Arthursson, V.; Toth, E.; Krarup, P.M.; Syk, I.; Thorlacius, H. Lymphovascular Infiltration, Not Depth of Invasion, Is the Critical Risk Factor of Metastases in Early Colorectal Cancer: Retrospective Population-Based Cohort Study on Prospectively Collected Data, Including Validation. Ann. Surg. 2022, 275, E148–E154. [Google Scholar] [CrossRef] [PubMed]
- Tominaga, K.; Nakanishi, Y.; Nimura, S.; Yoshimura, K.; Sakai, Y.; Shimoda, T. Predictive Histopathologic Factors for Lymph Node Metastasis in Patients with Nonpedunculated Submucosal Invasive Colorectal Carcinoma. Dis. Colon Rectum 2005, 48, 92–100. [Google Scholar] [CrossRef] [PubMed]
- Nascimbeni, R.; Burgart, L.J.; Nivatvongs, S.; Larson, D.R. Risk of Lymph Node Metastasis in T1 Carcinoma of the Colon and Rectum. Dis. Colon Rectum 2002, 45, 200–206. [Google Scholar] [CrossRef] [PubMed]
- Yamamoto, S.; Watanabe, M.; Hasegawa, H.; Baba, H.; Yoshinare, K.; Shiraishi, J.; Kitajima, M. The Risk of Lymph Node Metastasis in T1 Colorectal Carcinoma. Hepatogastroenterology 2004, 51, 998–1000. [Google Scholar]
- Ueno, H.; Mochizuki, H.; Hashiguchi, Y.; Shimazaki, H.; Aida, S.; Hase, K.; Matsukuma, S.; Kanai, T.; Kurihara, H.; Ozawa, K.; et al. Risk Factors for an Adverse Outcome in Early Invasive Colorectal Carcinoma. Gastroenterology 2004, 127, 385–394. [Google Scholar] [CrossRef] [PubMed]
- Yamauchi, H.; Togashi, K.; Kawamura, Y.J.; Horie, H.; Sasaki, J.; Tsujinaka, S.; Yasuda, Y.; Konishi, F. Pathological Predictors for Lymph Node Metastasis in T1 Colorectal Cancer. Surg. Today 2008, 38, 905–910. [Google Scholar] [CrossRef]
- Wada, H.; Shiozawa, M.; Katayama, K.; Okamoto, N.; Miyagi, Y.; Rino, Y.; Masuda, M.; Akaike, M. Systematic Review and Meta-Analysis of Histopathological Predictive Factors for Lymph Node Metastasis in T1 Colorectal Cancer. J. Gastroenterol. 2015, 50, 727–734. [Google Scholar] [CrossRef] [PubMed]
- Tateishi, Y.; Nakanishi, Y.; Taniguchi, H.; Shimoda, T.; Umemura, S. Pathological Prognostic Factors Predicting Lymph Node Metastasis in Submucosal Invasive (T1) Colorectal Carcinoma. Mod. Pathol. 2010, 23, 1068–1072. [Google Scholar] [CrossRef]
- Tamaru, Y.; Oka, S.; Tanaka, S.; Nagata, S.; Hiraga, Y.; Kuwai, T.; Furudoi, A.; Tamura, T.; Kunihiro, M.; Okanobu, H.; et al. Long-Term Outcomes after Treatment for T1 Colorectal Carcinoma: A Multicenter Retrospective Cohort Study of Hiroshima GI Endoscopy Research Group. J. Gastroenterol. 2017, 52, 1169–1179. [Google Scholar] [CrossRef]
- Sun, Z.-Q.; Ma, S.; Zhou, Q.-B.; Yang, S.-X.; Chang, Y.; Zeng, X.-Y.; Ren, W.-G.; Han, F.-H.; Xie, X.; Zeng, F.-Y.; et al. Prognostic Value of Lymph Node Metastasis in Patients with T1-Stage Colorectal Cancer from Multiple Centers in China. World J. Gastroenterol. 2017, 23, 8582–8590. [Google Scholar] [CrossRef] [PubMed]
- Chandler, I.; Houlston, R.S. Interobserver Agreement in Grading of Colorectal Cancers—Findings from a Nationwide Web-Based Survey of Histopathologists. Histopathology 2008, 52, 494–499. [Google Scholar] [CrossRef] [PubMed]
- Thomas, G.D.; Dixon, M.F.; Smeeton, N.C.; Williams, N.S. Observer Variation in the Histological Grading of Rectal Carcinoma. J. Clin. Pathol. 1983, 36, 385–391. [Google Scholar] [CrossRef]
- Watanabe, J.; Ichimasa, K.; Kataoka, Y.; Miyahara, S.; Miki, A.; Yeoh, K.G.; Kawai, S.; Martínez de Juan, F.; Machado, I.; Kotani, K.; et al. Diagnostic Accuracy of Highest-Grade or Predominant Histological Differentiation of T1 Colorectal Cancer in Predicting Lymph Node Metastasis: A Systematic Review and Meta-Analysis. Clin. Transl. Gastroenterol. 2024, 15, e00673. [Google Scholar] [CrossRef] [PubMed]
- Ueno, H.; Hashiguchi, Y.; Kajiwara, Y.; Shinto, E.; Shimazaki, H.; Kurihara, H.; Mochizuki, H.; Hase, K. Proposed Objective Criteria for “Grade 3” in Early Invasive Colorectal Cancer. Am. J. Clin. Pathol. 2010, 134, 312–322. [Google Scholar] [CrossRef]
- Ueno, H.; Mochizuki, H.; Hashiguchi, Y.; Ishiguro, M.; Kajiwara, Y.; Sato, T.; Shimazaki, H.; Hase, K.; Talbot, I.C. Histological Grading of Colorectal Cancer. Ann. Surg. 2008, 247, 811–818. [Google Scholar] [CrossRef]
- Zwager, L.W.; Bastiaansen, B.A.J.; Montazeri, N.S.M.; Hompes, R.; Barresi, V.; Ichimasa, K.; Kawachi, H.; Machado, I.; Masaki, T.; Sheng, W.; et al. Deep Submucosal Invasion Is Not an Independent Risk Factor for Lymph Node Metastasis in T1 Colorectal Cancer: A Meta-Analysis. Gastroenterology 2022, 163, 174–189. [Google Scholar] [CrossRef]
- Kouyama, Y.; Kudo, S.-E.; Miyachi, H.; Ichimasa, K.; Hisayuki, T.; Oikawa, H.; Matsudaira, S.; Kimura, Y.J.; Misawa, M.; Mori, Y.; et al. Practical Problems of Measuring Depth of Submucosal Invasion in T1 Colorectal Carcinomas. Int. J. Color. Dis. 2016, 31, 137–146. [Google Scholar] [CrossRef]
- Zwager, L.W.; Bastiaansen, B.A.J.; Van Der Spek, B.W.; Heine, D.N.; Schreuder, R.M.; Perk, L.E.; Weusten, B.L.A.M.; Boonstra, J.J.; Van Der Sluis, H.; Wolters, H.J.; et al. Endoscopic Full-Thickness Resection of T1 Colorectal Cancers: A Retrospective Analysis from a Multicenter Dutch EFTR Registry. Endoscopy 2022, 54, 475–485. [Google Scholar] [CrossRef]
- Ichimasa, K.; Kudo, S.; Kouyama, Y.; Mochizuki, K.; Takashina, Y.; Misawa, M.; Mori, Y.; Hayashi, T.; Wakamura, K.; Miyachi, H. Tumor Location as a Prognostic Factor in T1 Colorectal Cancer. J. Anus Rectum Colon 2022, 6, 2021–2029. [Google Scholar] [CrossRef]
- Mochizuki, K.; Kudo, S.; Ichimasa, K.; Kouyama, Y.; Matsudaira, S.; Takashina, Y.; Maeda, Y.; Ishigaki, T.; Nakamura, H.; Toyoshima, N.; et al. Left-Sided Location Is a Risk Factor for Lymph Node Metastasis of T1 Colorectal Cancer: A Single-Center Retrospective Study. Int. J. Color. Dis. 2020, 35, 1911–1919. [Google Scholar] [CrossRef] [PubMed]
- Kitaguchi, D.; Sasaki, T.; Nishizawa, Y.; Tsukada, Y.; Ito, M. Long-Term Outcomes and Lymph Node Metastasis in Patients Receiving Radical Surgery for Pathological T1 Lower Rectal Cancer. World J. Surg. 2019, 43, 649–656. [Google Scholar] [CrossRef] [PubMed]
- Butte, J.M.; Tang, P.; Gonen, M.; Shia, J.; Schattner, M.; Nash, G.M.; Temple, L.K.F.; Weiser, M.R. Rate of Residual Disease after Complete Endoscopic Resection of Malignant Colonic Polyp. Dis. Colon Rectum 2012, 55, 122–127. [Google Scholar] [CrossRef] [PubMed]
- Cubiella, J.; González, A.; Almazán, R.; Rodríguez-Camacho, E.; Fontenla Rodiles, J.; Domínguez Ferreiro, C.; Tejido Sandoval, C.; Sánchez Gómez, C.; de Vicente Bielza, N.; Lorenzo, I.P.; et al. pT1 Colorectal Cancer Detected in a Colorectal Cancer Mass Screening Program: Treatment and Factors Associated with Residual and Extraluminal Disease. Cancers 2020, 12, 2530. [Google Scholar] [CrossRef] [PubMed]
- Yamashita, K.; Oka, S.; Tanaka, S.; Nagata, S.; Hiraga, Y.; Kuwai, T.; Furudoi, A.; Tamura, T.; Kunihiro, M.; Okanobu, H.; et al. Preceding Endoscopic Submucosal Dissection for T1 Colorectal Carcinoma Does Not Affect the Prognosis of Patients Who Underwent Additional Surgery: A Large Multicenter Propensity Score-Matched Analysis. J. Gastroenterol. 2019, 54, 897–906. [Google Scholar] [CrossRef] [PubMed]
- Kim, J.B.; Lee, H.S.; Lee, H.J.; Kim, J.; Yang, D.H.; Yu, C.S.; Kim, J.C.; Byeon, J.S. Long-Term Outcomes of Endoscopic Versus Surgical Resection of Superficial Submucosal Colorectal Cancer. Dig. Dis. Sci. 2015, 60, 2785–2792. [Google Scholar] [CrossRef] [PubMed]
- Morini, A.; Annicchiarico, A.; De Giorgi, F.; Ferioli, E.; Romboli, A.; Montali, F.; Crafa, P.; Costi, R. Local Excision of T1 Colorectal Cancer: Good Differentiation, Absence of Lymphovascular Invasion, and Limited Tumor Radial Infiltration (≤4.25 Mm) May Allow Avoiding Radical Surgery. Int. J. Color. Dis. 2022, 37, 2525–2533. [Google Scholar] [CrossRef]
- Ozeki, T.; Shimura, T.; Ozeki, T.; Ebi, M.; Iwasaki, H.; Kato, H.; Inaguma, S.; Okuda, Y.; Katano, T.; Nishie, H.; et al. The Risk Analyses of Lymph Node Metastasis and Recurrence for Submucosal Invasive Colorectal Cancer: Novel Criteria to Skip Completion Surgery. Cancers 2022, 14, 822. [Google Scholar] [CrossRef] [PubMed]
- Kim, J.; Rhee, Y.-Y.; Bae, J.M.; Kim, J.H.; Koh, S.-J.; Lee, H.J.; Im, J.P.; Kim, M.J.; Ryoo, S.-B.; Jeong, S.-Y.; et al. Composite Scoring System and Optimal Tumor Budding Cut-off Number for Estimating Lymph Node Metastasis in Submucosal Colorectal Cancer. BMC Cancer 2022, 22, 861. [Google Scholar] [CrossRef]
- Piao, Z.; Ge, R.; Wang, C. A Proposal for Grading the Risk of Lymph Node Metastasis after Endoscopic Resection of T1 Colorectal Cancer. Int. J. Color. Dis. 2023, 38, 25. [Google Scholar] [CrossRef]
- Li, M.; Zhang, J.; Dan, Y.; Yao, Y.; Dai, W.; Cai, G.; Yang, G.; Tong, T. A Clinical-Radiomics Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer. J. Transl. Med. 2020, 18, 46. [Google Scholar] [CrossRef] [PubMed]
- Gillies, R.J.; Schabath, M.B. Radiomics Improves Cancer Screening and Early Detection. Cancer Epidemiol. Biomark. Prev. 2020, 29, 2556–2567. [Google Scholar] [CrossRef] [PubMed]
- Badic, B.; Tixier, F.; Cheze Le Rest, C.; Hatt, M.; Visvikis, D. Radiogenomics in Colorectal Cancer. Cancers 2021, 13, 973. [Google Scholar] [CrossRef] [PubMed]
- Takamatsu, M.; Yamamoto, N.; Kawachi, H.; Chino, A.; Saito, S.; Ueno, M.; Ishikawa, Y.; Takazawa, Y.; Takeuchi, K. Prediction of Early Colorectal Cancer Metastasis by Machine Learning Using Digital Slide Images. Comput. Methods Programs Biomed. 2019, 178, 155–161. [Google Scholar] [CrossRef] [PubMed]
- Song, J.H.; Hong, Y.; Kim, E.R.; Kim, S.-H.; Sohn, I. Utility of Artificial Intelligence with Deep Learning of Hematoxylin and Eosin-Stained Whole Slide Images to Predict Lymph Node Metastasis in T1 Colorectal Cancer Using Endoscopically Resected Specimens; Prediction of Lymph Node Metastasis in T1 Colorectal Cancer. J. Gastroenterol. 2022, 57, 654–666. [Google Scholar] [CrossRef] [PubMed]
- Kudo, S.; Ichimasa, K.; Villard, B.; Mori, Y.; Misawa, M.; Saito, S.; Hotta, K.; Saito, Y.; Matsuda, T.; Yamada, K.; et al. Artificial Intelligence System to Determine Risk of T1 Colorectal Cancer Metastasis to Lymph Node. Gastroenterology 2021, 160, 1075–1084.e2. [Google Scholar] [CrossRef]
- Miyazaki, K.; Wada, Y.; Okuno, K.; Murano, T.; Morine, Y.; Ikemoto, T.; Saito, Y.; Ikematsu, H.; Kinugasa, Y.; Shimada, M.; et al. An Exosome-Based Liquid Biopsy Signature for Pre-Operative Identification of Lymph Node Metastasis in Patients with Pathological High-Risk T1 Colorectal Cancer. Mol. Cancer 2023, 22, 2. [Google Scholar] [CrossRef]
Variable | LNM− | LNM+ | OR (CI 95%) | p |
---|---|---|---|---|
Sex | ||||
| 44 (81.5%) | 10 (18.5%) | ||
| 40 (87%) | 6 (13%) | 0.66 (0.22–1.98) | 0.4587 |
Age (median) | 66 | 59 | 0.92 (0.871–0.99) | 0.024 |
Location | ||||
| 71 (88.7%) | 9 (11.3%) | ||
| 13 (65%) | 7 (35%) | 4.24 (1.34–13.43) | 0.0138 |
Median endoscopically estimated size in mm | 30 | 30 | 1.00 (0.96–1.04) | 0.9333 |
(range) | (6–80) | (10–50) | ||
Median histologically measured size in mm | 18 | 18 | 0.98 (0.93–1.04) | 0.5882 |
(range) | (5–65) | (12–44) | ||
Morphology | ||||
Pedunculated | 6 (85.7%) | 1 (14.3%) | ||
Non-pedunculated | 78 (83.9%) | 15 (16.1%) | -- | 0.8984 |
Lymphovascular invasion | ||||
Absent | 72 (93.5%) | 5 (6.5%) | ||
Present | 12 (52.2%) | 11 (47.8%) | 17.25 (6.09–48.82) | <0.0001 |
Depth of invasion | ||||
<1000 µm | 23 (92%) | 2(8%) | Ref. | |
≥1000 µm | 49 (79%) | 13 (21%) | 3.05 (0.635–14.650) | 0.1635 |
Not evaluable | 12 (92.3%) | 1 (7.7%) | 0.95 (0.07–11.67) | 0.9734 |
Differentiation grade | ||||
| 73 (91.2%) | 7(8.8%) | Ref. | |
| 9 (60%) | 6(40%) | 6.95 (1.91–2 5.29) | 0.0033 |
| 2 (40%) | 3 (60%) | 15.64 (2.22–109.95) | 0.0057 |
| 0 | 0 | -- | -- |
Histologic grade | ||||
G1 or G2 | 82 (86.3%) | 13(13.7%) | Ref. | |
G3 or G4 | 2 (40%) | 3 (60%) | 9.46 (1.44–62.15) | 0.0193 |
TB grade | ||||
1 | 78 (94%) | 5 (6%) | Ref. | |
2 | 4 (44.4%) | 5 (55.6%) | 19.50 (3.95–96.1) | 0.0003 |
3 | 2 (25%) | 6 (75%) | 46.80 (7.44–294.12) | <0.0001 |
PDC | ||||
Absent | 70 (94.6%) | 4 (5.4%) | Ref. | |
Present | 14 (53.8%) | 12 (46.2%) | 15.00 (4.21–53.34) | <0.0001 |
Differentiation grade classified by PDC | ||||
G1 PDC− | 64(95.5%) | 3 (4.5%) | Ref. | -- |
G1 PDC+ | 9 (70%) | 4 (30%) | 9.48 (1.81–40.44) | 0.0076 |
G2 PDC− | 6 (86%) | 1 (14%) | 3.55 (0.31–39.7) | 0.3028 |
G2 PDC+ | 3 (38%) | 5(62%) | 35.55 (5.64–224.10) | 0.0001 |
G3 PDC− | 0 (0%) | 0 (0%) | -- | -- |
G3 PDC+ | 2 (40%) | 3 (60%) | 32.0(3.79–269.59) | 0.0014 |
Muscularis mucosae disruption | ||||
Incomplete | 65 (86.7%) | 10 (13.3%) | Ref. | 0.0667 |
Complete | 13 (68.4%) | 6 (31.6%) | 3.00 (0.92–9.70) | |
Not evaluable | 6 (100%) | 0 (0%) | -- | |
Width of invasion | ||||
<4000 µm | 64(84.2%) | 12 (15.8%) | Ref. | |
≥4000 µm | 5 (64.5%) | 3 (37.5%) | 3.2 (0.67–15.20) | 0.1435 |
Not evaluable | 15 (93.7%) | 1 (6.2%) | 0.35 (0.04–2.95) | 0.3382 |
Intratumoral inflammation | ||||
Absent or mild | 58 (81.7%) | 13 (18.3%) | Ref. | |
Moderate/intense | 26 (89.7%) | 3 (10.3%) | 0.51 (0.13–1.96) | 0.3307 |
Peritumoral inflammation | ||||
Absent or mild | 34 (85%) | 6 (15%) | ||
Moderate/intense | 50 (83.3%) | 10 (16.7%) | 1.13 (0.37–3.41) | 0.8238 |
Background adenoma | ||||
Present | 81 (84.4%) | 15 (15.6%) | Ref. | |
Absent | 3 (75%) | 1 (25%) | 1.80 (0.17–18.48) | 0.6209 |
Macroscopically complete resection | ||||
| 81 (83.3%) | 16(16.7%) | ||
| 3 (100%) | 0 (0%) | -- | -- |
Resection margin | ||||
| 48 (84.2%) | 9 (15.8%) | Ref. | |
| 28 (82.4%) | 6 (17.65) | 1.88 (0.22–16.06) | 0.5638 |
| 8 (88.9%) | 1 (11.1%) | 1.12 (0.06–21.08) | 0.9372 |
En bloc resection | ||||
| 68 (82.9%) | 14 (17.1%) | Ref. | |
| 16 (88.9%) | 2 (11.1%) | 0.56 (0.10–3.12) | 0.511 |
Residual neoplasia after endoscopic resection (in surgical specimen) | ||||
| 41 (83.7%) | 8 (16.3%) | ||
| 2 (100%) | 0 (0%) | -- | -- |
| 41 (83.7%) | 8 (16.3%) |
SE | SP | PPV | NPV | FP | FN | AIC | |
---|---|---|---|---|---|---|---|
TB > 1 + PDC | 68.7 | 92.8 | 54.8 | 95.9 | 45.2 | 4.1 | 64.129 |
PDC alone | 75 | 84.52 | 48 | 94.7 | 52 | 5.3 | 71.012 |
CPRC | 93.75 | 22.72 | 13.3 | 96.6 | 86.7 | 3.4 | -- |
TB > 1 + PDC vs. PDC Alone | TB > 1 + PDC vs. CPRC | PDC Alone vs. CPRC | ||||
---|---|---|---|---|---|---|
FP | 6.8 (−22.1–34.1) | p = 0.67 | 41.5 (17.4–62.8) | p = 0.0001 | 34.7 (14.5–54.1) | p = 0.0002 |
FN | 0.7 (−17.4–7.9) | p = 088 | 1.2 (−6.2–9.7) | p = 0.72 | 1.9 (−16.3–10.03) | p = 0.73 |
Variable | No Poor Outcome | Poor Outcome | OR (CI 95%) | p |
---|---|---|---|---|
Sex | ||||
| 66 (84.6%) | 12 (15.4%) | ||
| 72 (86.7%) | 11 (13.3%) | 1.19 (0.49–2.87) | 0.6995 |
Age (median) | 66 | 59 | 0.92 (0.87–0.99) | 0.024 |
Location | ||||
| 109 (89.3%) | 13 (10.7%) | Ref. | |
| 29 (74.4%) | 10 (25.6%) | 2.891 (1.15–7.25) | 0.0238 |
Median endoscopically estimated size in mm | 30 | 30 | 1.00 (0.97–1.09) | 0.5677 |
(range) | (6–80) | (10–50) | ||
Median histologically measured size in mm | 18 | 16.97 | 0.98 (0.93–1.03) | 0.4327 |
(range) | (5–65) | (7–44) | ||
Morphology | ||||
Pedunculated | 31 (91.2%) | 3 (8.8%) | Ref. | |
Non-pedunculated | 107 (84.3%) | 20 (15.7%) | 0.51 (0.14–1.85) | 0.3126 |
Lymphovascular invasion | ||||
Absent | 127 (93.4%) | 9 (6.6%) | Ref. | |
Present | 11 (44%) | 14 (56%) | 17.95 (6.35–50.79) | <0.0001 |
Depth of invasion | ||||
<1000 µm | 60 (95.2%) | 3 (4.8%) | Ref. | |
≥1000 µm | 64 (79%) | 17 (21%) | 5.31 (1.48–19.04) | 0.0104 |
Not evaluable | 14 (82.4%) | 3 (17.6%) | 4.28 (0.78–23.52) | 0.0939 |
Differentiation grade | ||||
| 120(90.9%) | 12(9.1%) | Ref. | |
| 17 (70.8%) | 7(29.2%) | 4.117 (1.424–11.903) | 0.009 |
| 1(20%) | 4 (80%) | 40 (4.131–387.282) | 0.0014 |
| 0 | 0 | -- | -- |
Histologic grade | ||||
G1 or G2 | 137 (87.8%) | 19(12.2%) | Ref. | |
G3 or G4 | 1 (20%) | 4 (80%) | 28.842 (3.060–271.809) | 0.0033 |
TB grade | ||||
1 | 131 (94%) | 8 (6%) | Ref. | |
>1 | 7 (44.4%) | 15 (55.6%) | 5.923 (3.339–10.508) | <0.0001 |
PDC | ||||
Absent | 120 (93.7%) | 8 (6.3%) | Ref. | |
Present | 18 (54.5%) | 15 (45.5%) | 12.500 (4.640–33.668) | <0.0001 |
Differentiation grade classified by PDC | ||||
G1 PDC− | 109 (94%) | 7 (6%) | Ref. | |
G1 PDC+ | 11 (68.7%) | 5 (31.2%) | 7.07 (1.92–26.08) | 0.0033 |
G2 PDC− | 6 (90.1%) | 1 (9.1%) | 1.55 (0.17–13.95) | 0.6923 |
G2 PDC+ | 7(53.8%) | 5(46.2%) | 13.34 (3.52–50.54) | 0.0001 |
G3 PDC− | 0 (0%) | 0 (0%) | -- | -- |
G3 PDC+ | 1 (20%) | 4(80%) | 62.28 (6.11–634.29) | 0.0005 |
Muscularis mucosae disruption | ||||
Incomplete | 112 (88.2%) | 15 (11.8%) | Ref. | |
Complete | 16 (69.6%) | 7 (30.4%) | 3.26 (1.1559–9.2319) | 0.0255 |
Not evaluable | 10 (90.9%) | 1 (9.1%) | 0.74 (0.09–6.25) | 0.7876 |
Width of invasion | ||||
<4000 µm | 109 (87.9%) | 15 (12.1%) | Ref. | |
≥4000 µm | 6 (54.5%) | 5 (45.5%) | 6.05 (1.64–22.30) | 0.0068 |
Not evaluable | 23 (88.5%) | 3 (11.5%) | 0.94 (0.25–3.54) | 0.9365 |
Intratumoral inflammation | ||||
Absent or mild | 101 (83.5%) | 20 (16.5%) | Ref. | |
Moderate/intense | 36 (92.3%) | 3 (7.7%) | 0.42 (0.12–1.51) | 0.1872 |
Peritumoral inflammation | ||||
Absent or mild | 54 (84.4%) | 10 (15.6%) | Ref. | |
Moderate/intense | 83 (86.5%) | 13 (13.5%) | 1.19(0.49–2.92) | 0.6935 |
Background adenoma | ||||
Present | 135 (86%) | 22 (14%) | ||
Absent | 3 (75%) | 1 (25%) | 0.48 (0.04–4.91) | 0.5433 |
Macroscopically complete resection | ||||
| 127(83.3%) | 23(16.7%) | ||
| 5 (100%) | 0 (0%) | -- | -- |
Resection margin | ||||
| 73 (81.1%) | 17 (8.9%) | Ref. | |
| 50 (92.6%) | 4 (7.4%) | 2.91 (0.92–9.16) | 0.0679 |
| 9 (81.8%) | 2 (8.2%) | 2.77 (0.44–17.48) | 0.2764 |
En bloc resection | ||||
| 110 (85.3%) | 19 (14.7%) | Ref. | |
| 22 (84.6%) | 4 (15.4%) | 1.05 (0.32–3.39) | 0.9316 |
Residual neoplasia after endoscopic resection (in surgical specimen) | ||||
| 95 (86.4%) | 15 (15.6%) | ||
| 2 (100%) | 0 (0%) | -- | -- |
| 41 (83.7%) | 8 (16.3%) |
SE | SP | PPV | NPV | FP | FN | |
---|---|---|---|---|---|---|
LVI + TB > 1 | 65.2 | 94.9 | 61.9 | 95.6 | 38.1 | 4.4 |
CPRC | 95.6 | 40.6 | 16.9 | 98.7 | 83.1 | 1.3 |
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Share and Cite
Martínez de Juan, F.; Navarro, S.; Machado, I. Refining Risk Criteria May Substantially Reduce Unnecessary Additional Surgeries after Local Resection of T1 Colorectal Cancer. Cancers 2024, 16, 2321. https://doi.org/10.3390/cancers16132321
Martínez de Juan F, Navarro S, Machado I. Refining Risk Criteria May Substantially Reduce Unnecessary Additional Surgeries after Local Resection of T1 Colorectal Cancer. Cancers. 2024; 16(13):2321. https://doi.org/10.3390/cancers16132321
Chicago/Turabian StyleMartínez de Juan, Fernando, Samuel Navarro, and Isidro Machado. 2024. "Refining Risk Criteria May Substantially Reduce Unnecessary Additional Surgeries after Local Resection of T1 Colorectal Cancer" Cancers 16, no. 13: 2321. https://doi.org/10.3390/cancers16132321
APA StyleMartínez de Juan, F., Navarro, S., & Machado, I. (2024). Refining Risk Criteria May Substantially Reduce Unnecessary Additional Surgeries after Local Resection of T1 Colorectal Cancer. Cancers, 16(13), 2321. https://doi.org/10.3390/cancers16132321