Sanjiv S G Gangaram Panday, David van Klaveren, Sjoerd M Lagarde, Hester F Lingsma, Bianca Mostert, Peter-Paul L O Coene, Jan Willem T Dekker, Henk H Hartgrink, Joos Heisterkamp, Merlijn Hutteman, Ewout A Kouwenhoven, Grard A P Nieuwenhuijzen, Jean-Pierre Pierie, Johanna W van Sandick, Meindert N Sosef, Edwin S van der Zaag, J Jan B van Lanschot, Bas P L Wijnhoven
{"title":"Accuracy of Predicting Residual Disease and Disease Progression During Active Surveillance for Esophageal Cancer.","authors":"Sanjiv S G Gangaram Panday, David van Klaveren, Sjoerd M Lagarde, Hester F Lingsma, Bianca Mostert, Peter-Paul L O Coene, Jan Willem T Dekker, Henk H Hartgrink, Joos Heisterkamp, Merlijn Hutteman, Ewout A Kouwenhoven, Grard A P Nieuwenhuijzen, Jean-Pierre Pierie, Johanna W van Sandick, Meindert N Sosef, Edwin S van der Zaag, J Jan B van Lanschot, Bas P L Wijnhoven","doi":"10.1245/s10434-025-18531-y","DOIUrl":"https://doi.org/10.1245/s10434-025-18531-y","url":null,"abstract":"<p><strong>Background: </strong>To date, active surveillance has been non-inferior to standard surgery for patients with esophageal cancer, achieving a clinical complete response (CCR) after neoadjuvant chemoradiotherapy (nCRT). However, two thirds of patients have residual disease detected 12 weeks after nCRT and undergo surgery. At 12 weeks, nearly half of the patients with CCR will experience locoregional regrowth. This study aimed to identify routine predictive factors for achieving (sustained) CCR to improve patient selection for active surveillance.</p><p><strong>Methods: </strong>Data from the SANO trial were analyzed, including data of patients who underwent nCRT for esophageal cancer. Logistic regression assessed predictors of CCR at 12 weeks, with potential factors including age, sex, WHO performance status, clinical T and N categories, histology, differentiation grade, tumor location, and tumor length. For patients with CCR in active surveillance, cause-specific proportional hazards regression identified predictors of sustained CCR (no locoregional regrowth, dissemination, or death) during a minimum 3-year follow-up period. Discrimination was quantified using the concordance statistic (c-statistic) with bootstrap validation.</p><p><strong>Results: </strong>Of 750 patients, 274 (37 %) achieved CCR at 12 weeks. Higher cN category was associated with lower likelihood of CCR (cN2-3 vs cN0: odds ratio [OR], 0.57; 95 % confidence interval [CI], 0.37-0.88; P < 0.01; c-statistic, 0.56). Among 198 patients in active surveillance, 25 % had sustained CCR after a median follow-up period of 54 months (interquartile range [IQR],46-58 months). Higher cN category (cN2-3 vs cN0; HR, 2.08; 95 % CI, 1.25-3.48; P < 0.01) was associated with non-sustained CCR (c-statistic, 0.58).</p><p><strong>Conclusion: </strong>Standard clinical parameters poorly predict clinical response after nCRT. Additional predictive parameters and better diagnostic tests are needed to improve patient selection for active surveillance.</p>","PeriodicalId":8229,"journal":{"name":"Annals of Surgical Oncology","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145342910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jun Arima, Kohei Chida, Rongrong Wu, Kohei Taniguchi, Amber McKenery, Brian G Morreale, Andrea M Monell, Scott I Abrams, John M L Ebos, Kenichi Hakamada, Takashi Ishikawa, Seita Hagihara, Kosei Kimura, Mitsuhiko Iwamoto, Sang-Woong Lee, Kazuaki Takabe
{"title":"ASO Visual Abstract: ESR1 Expression Negatively Correlates with Immune Cell Infiltration and Response to Immune Checkpoint Inhibitors in ER-Positive HER2-Negative Breast Cancer.","authors":"Jun Arima, Kohei Chida, Rongrong Wu, Kohei Taniguchi, Amber McKenery, Brian G Morreale, Andrea M Monell, Scott I Abrams, John M L Ebos, Kenichi Hakamada, Takashi Ishikawa, Seita Hagihara, Kosei Kimura, Mitsuhiko Iwamoto, Sang-Woong Lee, Kazuaki Takabe","doi":"10.1245/s10434-025-18480-6","DOIUrl":"https://doi.org/10.1245/s10434-025-18480-6","url":null,"abstract":"","PeriodicalId":8229,"journal":{"name":"Annals of Surgical Oncology","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145342930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Prognostic Impact of Hyperthermic Intraperitoneal Chemotherapy After Laparoscopic Gastrectomy in Gastric Cancer with Positive Peritoneal Lavage Cytology and no Other Noncurative Factors.","authors":"Chenbin Lv, Linyan Tong, Yuqin Sun, Qiuxian Chen, Yonghe Wu, Yongbin Zhang, Lisheng Cai","doi":"10.1245/s10434-025-18592-z","DOIUrl":"https://doi.org/10.1245/s10434-025-18592-z","url":null,"abstract":"<p><strong>Background: </strong>This study was designed to evaluate the prognostic impact of laparoscopic gastrectomy combined with postoperative hyperthermic intraperitoneal chemotherapy (HIPEC) in gastric cancer patients exhibiting positive peritoneal lavage cytology (CY1) without visible metastases.</p><p><strong>Methods: </strong>Data from 263 patients undergoing peritoneal lavage cytology and laparoscopic gastrectomy between December 2017 and December 2022 were retrospectively analyzed. Patients were stratified into CY1 (positive cytology) or CY0 (negative cytology) groups, with CY1 cases further subdivided into HIPEC and non-HIPEC cohorts based on postoperative HIPEC administration. Postoperative recurrence and survival outcomes were compared across cohorts.</p><p><strong>Results: </strong>Among 263 patients, 214 were CY0 and 49 CY1 with 27 CY1 patients receiving HIPEC and 22 non-HIPEC. HIPEC-treated CY1 patients demonstrated lower postoperative recurrence (66.7% vs. 90.9%) and visible peritoneal metastasis rates (29.6% vs. 59.1%) than non-HIPEC counterparts (P < 0.05). CY0 patients exhibited higher 3-year overall survival (OS) and progression-free survival (PFS) rates compared with both non-HIPEC CY1 and HIPEC-treated CY1 subgroups. HIPEC-treated CY1 patients showed superior 3-year OS (51.8% vs. 18.2%, P = 0.005) and PFS (35% vs. 13.6%, P = 0.037) than non-HIPEC CY1. Notably, stage III CY0 cohort demonstrated comparable survival outcomes to HIPEC-treated CY1 cohort (overall survival 56.4% vs. 51.8%, P = 0.39; progression-free survival 51% vs. 35%, P = 0.14). Cox analysis identified non-HIPEC and pN2-3 stage as independent risk factors for OS, while non-HIPEC, pT4, and pN2-3 stages predicted poorer PFS in CY1 patients.</p><p><strong>Conclusions: </strong>Hyperthermic intraperitoneal chemotherapy following laparoscopic gastrectomy demonstrated efficacy in reducing postoperative peritoneal metastasis rates while enhancing prognosis of CY1 patients, achieving oncologic outcomes comparable to stage III CY0 counterparts, although additional investigation remains imperative.</p>","PeriodicalId":8229,"journal":{"name":"Annals of Surgical Oncology","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145342933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christopher D Vetter, Tanya Hoskin, Carrie Olson, Karthik Giridhar, Judy C Boughey
{"title":"ASO Visual Abstract: Validation of the Performance of the Novel Prognostic Staging System for Overall Survival in De Novo Metastatic Breast Cancer and Demonstration of Performance for Cancer-Specific Outcomes.","authors":"Christopher D Vetter, Tanya Hoskin, Carrie Olson, Karthik Giridhar, Judy C Boughey","doi":"10.1245/s10434-025-18350-1","DOIUrl":"https://doi.org/10.1245/s10434-025-18350-1","url":null,"abstract":"","PeriodicalId":8229,"journal":{"name":"Annals of Surgical Oncology","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145342942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Surgical Optimization in Preoperatively Low-risk cN1a PTC: A Predictive Model for High-Volume Central Lymph Node Metastasis.","authors":"Yi Zhou, Zhixin Guo, Jianyan Long, Heyang Xu, Mingwei Liang, Yuan Hu, Ruixia Li, Zhenbang Ke, Wanna Chen, Xiangdong Xu","doi":"10.1245/s10434-025-18569-y","DOIUrl":"https://doi.org/10.1245/s10434-025-18569-y","url":null,"abstract":"<p><strong>Background: </strong>Accurate preoperative identification of high-volume central lymph node metastasis (hv-CLNM; defined as more than 5 central lymph node metastases) is critical for guiding surgical decisions-lobectomy or total thyroidectomy-in patients with papillary thyroid carcinoma (PTC) clinically diagnosed with central neck lymph node metastasis (cN1a). Total thyroidectomy is generally preferred for patients with hv-CLNM. In contrast, lobectomy may be sufficient for patients with low-volume metastasis (5 or fewer lymph node metastases). This study aimed to identify predictors of hv-CLNM in preoperatively low-risk cN1a and to develop a predictive model to estimate the risk of hv-CLNM, thereby optimizing surgical decision-making.</p><p><strong>Methods: </strong>A total of 707 patients with pathologically confirmed PTC and classified as preoperatively low-risk cN1a were retrospectively enrolled. Clinical and ultrasound features were collected. Variables were selected using least absolute shrinkage and selection operator regression, followed by multivariate logistic regression to construct a predictive model. Internal validation was performed. Recurrence-free survival was compared between lobectomy and total thyroidectomy groups using propensity score matching.</p><p><strong>Results: </strong>Hv-CLNM occurred in 13.4% (96/707) of patients. Independent predictors of hv-CLNM included age, sex, tumor size, tumor location, and lymph node calcification. The nomogram demonstrated good discrimination (area under the plasma concentration-time curve = 0.75) and calibration. After adjustment, recurrence-free survival did not significantly differ between surgical groups.</p><p><strong>Conclusions: </strong>This nomogram, based on readily available clinical and ultrasound features, effectively predicts the risk of hv-CLNM in preoperatively low-risk cN1a PTC. This tool may facilitate individualized surgical planning. Lobectomy appears to be a safe and appropriate option for most patients in this subgroup.</p>","PeriodicalId":8229,"journal":{"name":"Annals of Surgical Oncology","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145342852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"ASO Author Reflections: Defining the Role of PIPAC in Ovarian Cancer - Results from a U.S. Phase 1 Clinical Trial.","authors":"Brad Nakamura, Tri A Dinh, Thanh H Dellinger","doi":"10.1245/s10434-025-18588-9","DOIUrl":"https://doi.org/10.1245/s10434-025-18588-9","url":null,"abstract":"","PeriodicalId":8229,"journal":{"name":"Annals of Surgical Oncology","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145342897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Artificial-Intelligence-based Surgical Phase Recognition in Robot-Assisted Radical Prostatectomy and Cross-Surgeon Validation.","authors":"Yuichiro Konnai, Keishiro Fukumoto, Masashi Takeuchi, Rei Takeuchi, Shinnosuke Fujiwara, Yota Yasumizu, Nobuyuki Tanaka, Toshikazu Takeda, Kazuhiro Matsumoto, Takeo Kosaka, Hirofumi Kawakubo, Yuko Kitagawa, Mototsugu Oya","doi":"10.1245/s10434-025-18590-1","DOIUrl":"https://doi.org/10.1245/s10434-025-18590-1","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI) has shown potential in various fields; however, its practical application in surgery remains limited. We developed an AI system capable of automatically recognizing surgical phases in robot-assisted radical prostatectomy (RARP) and confirmed its accuracy through cross-surgeon validation.</p><p><strong>Materials and methods: </strong>We analyzed clinical data from 102 patients who underwent RARP, including 81 consecutive patients operated on by one surgeon (surgeon A) and 21 operated on by five other surgeons (surgeons B-F). In total, 65 of the 81 patients were used for AI development, while the remaining 16, in addition to the 21 patients operated on by surgeons B-F, were used for AI validation. We classified surgical operations into nine phases. Well-trained surgeons annotated the time corresponding to each surgical phase for each video. We used Temporal Convolutional Networks for the Operating Room (TeCNO) to develop the AI model and evaluated its precision.</p><p><strong>Results: </strong>In AI development, 919,231 frames were utilized. Testing involved 216,357 frames from surgeon A and 249,553 frames from surgeons B-F. When the developed AI was used to analyze surgical videos from surgeon A, precision reached 0.94. In contrast, when the AI was applied to videos from surgeons B-F, precision was 0.83.</p><p><strong>Conclusions: </strong>The AI we developed not only showed high accuracy, but also demonstrated generalizability across different surgeons. By comprehensively evaluating surgical videos, our AI may be used to assess the quality of surgeries, thereby providing valuable feedback to surgeons and enhancing the effectiveness of surgical education.</p>","PeriodicalId":8229,"journal":{"name":"Annals of Surgical Oncology","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145342915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}