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1.
Journal of Clinical Oncology ; 40(16), 2022.
Article in English | EMBASE | ID: covidwho-2005699

ABSTRACT

Background: We illustrate a clinical application of Covid-Death Mean-Imputation (CoDMI) algorithm in survival analysis. CoDMI algorithm is a new statistical tool that allows to adjust, through mean imputation based on the Kaplan-Meier model, Covid-19 death events in oncologic clinical trials, providing a complete sample of observations to which any statistical method in survival analysis can be applied. Methods: We analyzed a group of patients who received trimodal treatment - neoadjuvant chemoradiotherapy, followed by surgery and adjuvant chemotherapy - for primary locally advanced rectal cancer (LARC). Overall survival (OS) was calculated in months from the date of diagnosis to the first event, including date of last follow-up or death. Because Covid-19 death events potentially bias survival estimation, to eliminate skewed data due to Covid-19 death events the observed lifetime of Covid-19 cases was replaced by its corresponding expected lifetime in absence of the Covid-19 event using CoDMI algorithm. In a traditional Kaplan-Meier approach, patient died of Covid-19 (DoC) can be: i) excluded to the cohort;ii) counted as censored (Cen);iii) considered as died of disease (DoD). CoDMI algorithm offers an additional, more satisfactory option: iv) DoC events are mean-imputed as no-DoC cases at later follow-up times. With this approach, the observed lifetime of each DoC patient is considered as an “incomplete data” and is extended by an additional expected lifetime computed using the classical Kaplan-Meyer model. Results: In total 94 patient records were collected. At the time of the analysis, there were 16 DoD cases, 1 DoC patient and 77 Cen cases. The DoC patient died due to Covid-19 52 months after diagnosis. CoDMI algorithm computed the expected future lifetime (beyond the DoC time of occurrence) provided by the Kaplan-Meier estimator applied to the no-DoC observations as well as to the DoC data itself. Given the DoC event at 52 months, CoDMI algorithm (applied in its standard form: DoC as virtual DoD) estimated that this patient would be died after 79.5 months of follow-up. Table summarizes the 2-year OS and the 5-year OS rates for the different treatment of DoC event. Since our sample contains only one DoC case, the effects on the estimates of the options considered differ very little. In this situation, however, one can better understand how CoDMI algorithm works. Conclusions: CoDMI algorithm leads to the “unbiased” (appropriately adjusted) OS probability in LARC patients with Covid-19 infection, compared with that provided by a naïve application of the Kaplan-Meier approach. This allows a proper interpretation of Covid-19 events in survival analysis. A user-friendly version of CoDMI is available at https://github.com/alef-innovation/codmi.

2.
Radiotherapy and Oncology ; 170:S1107-S1108, 2022.
Article in English | EMBASE | ID: covidwho-1967475

ABSTRACT

Purpose or Objective To illustrate a clinical application of Covid-Death Mean-Imputation (CoDMI) algorithm in survival analysis. CoDMI algorithm is a new statistical tool that allows to adjust, through mean imputation based on the Kaplan-Meier model, Covid-19 death events in oncologic clinical trials, providing a complete sample of observations to which any statistical method in survival analysis can be applied. Materials and Methods We analyzed a group of patients who received trimodal treatment – neoadjuvant chemoradiotherapy, followed by surgery and adjuvant chemotherapy – for primary locally advanced rectal cancer. Overall survival was calculated in months from the date of diagnosis to the first event, including date of the last follow-up or death. Because Covid-19 death events potentially bias survival estimation, to eliminate skewed data due to Covid-19 death events the observed lifetime of Covid-19 cases was replaced by its corresponding expected lifetime in absence of the Covid-19 event using CoDMI algorithm. In a traditional Kaplan-Meier approach, patient died of Covid-19 (DoC) can be: i) excluded to the cohort (but this would represent a loss of data), or ii) counted as censored (Cen) (but actually, due to its informative nature, Covid-19 death in a cancer patient cannot be censored as death from other causes), or iii) considered as died of disease (DoD) (but this provides an inappropriate exit cause). CoDMI algorithm offers an additional, more satisfactory option: iv) DoC events are mean-imputed as no-DoC cases at later follow-up times. With this approach, the observed lifetime of each DoC patient is considered as an “incomplete data” and is extended by an additional expected lifetime computed using the classical Kaplan-Meyer model. Results A total of 94 patient records were collected. At the time of the analysis, 16 patients died of disease (DoD), 1 patient died of Covid-19 (DoC) and 77 cases were censored (Cen). The DoC patient died due to Covid-19 52 months after diagnosis. CoDMI algorithm computed the expected future lifetime (beyond the DoC time of occurrence) provided by the Kaplan-Meier estimator applied to the no-DoC observations as well as to the DoC data itself. Given the DoC event at 52 months (red triangle in Figure 1), CoDMI algorithm (applied in its standard form) estimated that this patient would be died after 79.5 months of follow-up. The blue line in Figure 1 represents the newly estimated survival curve, where the additional DoD event is denoted by a circle. (Figure Presented) Conclusion CoDMI algorithm leads to the “unbiased” (appropriately adjusted) probability of overall survival in locally advanced rectal cancer patients with Covid-19 infection, compared with that provided by a naïve application of the Kaplan-Meier approach. This allows a proper interpretation/use of Covid-19 events in survival analysis. A user-friendly version of CoDMI is freely available at https://github.com/alef-innovation/codmi.

3.
Annals of Oncology ; 33:S239, 2022.
Article in English | EMBASE | ID: covidwho-1936040

ABSTRACT

Background: During the COVID-19 pandemic, a profound decrease in the number of cancer diagnoses was observed. For patients with esophagogastric cancer, a diagnostic delay may have resulted in more advanced disease at the time of diagnosis. Also, downscaling of oncological care during COVID-19 may have resulted in postponed or different treatments. Therefore, we aimed to investigate the effects of the COVID-19 pandemic in 2020 on the stage at diagnosis and oncological care of esophagogastric cancer. Methods: Patients who were diagnosed in 2020 and included in the Netherlands Cancer Registry were allocated to 5 periods that correspond to the severity of the COVID-19 pandemic in the Netherlands. These were compared to patients diagnosed in the same period in the years 2017-2019. The number of diagnoses, tumor characteristics, type of treatment, time until the start of treatment and, in case of resection, the time between neoadjuvant therapy and resection were evaluated for esophageal cancer (EC) and gastric cancer (GC) separately. Results: The 2020 cohort in the Netherlands consisted of 2388 EC patients and 1429 GC patients. The absolute number of diagnoses decreased most prominently in the months March and April of 2020 for both EC and GC. The total number of EC diagnoses in 2020 decreased significantly compared to 2017-2019 (n=2522, p=0.027), whereas the total number of GC diagnoses did not decrease (n=1442, p=0.270). In the weeks after the first COVID-19 case in the Netherlands and before the COVID-19 lockdown, the percentage of incurable diagnoses increased from 52.5% to 67.7% for GC (p=0.011) and did not increase for EC (33.0% to 40.8%, p = 0.092). The percentage of patients with potentially curable EC receiving neoadjuvant chemoradiotherapy with resection decreased from 35.0% in 2017-2019 to 27.4% in 2020 (p < 0.001), whereas the percentage of patients receiving neoadjuvant chemoradiation without resection increased from 9.5% in 2017-2019 to 13.9% in 2020 (p < 0.001). The percentage of patients receiving definitive chemoradiation did not change significantly (p=0.119). For GC patients, no significant changes in type of treatment were found. The time between neoadjuvant chemotherapy and gastric resection decreased in 2020 with four days (p=0.006), while the time between neoadjuvant therapy and esophageal resection increased with 5 days (p=0.005). For both tumor types, the time between diagnosis and start of treatment was significantly shorter for patients diagnosed during and after the COVID-19 lockdown. Conclusions: We found a significant decrease in the number of EC diagnoses in 2020 and a shift in the type of treatment in potentially curable EC patients, with fewer resections being performed. Yet, it is unclear whether this is the result of the COVID-19 pandemic or due to an ongoing trial which implements watchful waiting after chemoradiotherapy. The oncological care for GC patients did not change during the COVID-19 pandemic. The shorter time between diagnosis and start of treatment may have been the result of a sense of urgency, since it was unknown in what way COVID-19 might affect the continuity of care in the upcoming future. Legal entity responsible for the study: The authors. Funding: Has not received any funding. Disclosures: All authors have declared no conflicts of interest.

4.
Zhonghua Wei Chang Wai Ke Za Zhi ; 24(4): 359-365, 2021 Apr 25.
Article in Chinese | MEDLINE | ID: covidwho-827753

ABSTRACT

Objective: Pelvic high-resolution magnetic resonance imaging (MRI) has now become a standard method for evaluating the efficacy of neoadjuvant treatment for locally advanced rectal cancer (LARC). However, this traditional morphological qualitative assessment method based on T2-weighted imaging (T2WI) is not effective in predicting pathological complete remission (pCR). The purpose of this study is to investigate whether combining the magnetic resonance tumor regression grade (mrTRG) with apparent diffusion coefficient (ADC) can improve diagnostic value for pCR after preoperative neoadjuvant chemoradiotherapy (nCRT) of LARC. Methods: This was a diagnostic study. Clinicopathological data of 134 LARC patients who received nCRT and radical surgery in the First Affiliated Hospital of Kunming Medical University from January 2017 to December 2019 were retrospectively analyzed. All the patients underwent MRI which included T2WI and DWI sequences before and 8 weeks after nCRT. Two radiologists independently drew ROIs on T2WI and DWI to estimate mrTRG stage and calculate the mean ADC value. Receiver operating characteristics (ROC) method was applied to evaluate the predict value of mrTRG combined with mean ADC value for pCR. Results: Of 134 LARC patients, 85 were male and 49 were female with median age of 58 (28-82) years. After nCRT, MRI suggested 21 patients (15.7%) had clinical complete remission (cCR), e.g. mrTRG stage 1-2. Postoperative pathology revealed 31 (23.1%) patients had pCR. The evaluations of mrTRG and ADC value by the two readers were highly consistent, and the intra-group correlation coefficients were 0.83 (95% CI: 0.703-0.881) and 0.96 (95% CI: 0.989-0.996), respectively. There was a negative correlation between mrTRG and pCR (r(s)=-0.505, P<0.01), and a positive correlation between mean ADC value and pCR (r(s)=0.693, P<0.01). The ROC curve showed that mrTRG alone had a medium predictive value for pCR, with an area under the curve (AUC) of 0.832 (95% CI: 0.743-0.921); the mean ADC value had a higher predictive value for pCR, with AUC of 0.906 (95% CI: 0.869-0.962). The predictive value of the combined model of mrTRG and ADC value for pCR was significantly better than that of mrTRG alone (P=0.015), and the AUC was 0.908 (95% CI: 0.849-0.968). Conclusion: Both mrTRG and mean ADC value can be non-invasive methods to predict the efficacy of nCRT for LARC. Combining the mean ADC value with mrTRG can result in better pCR prediction.


Subject(s)
Neoadjuvant Therapy , Rectal Neoplasms , Aged , Aged, 80 and over , Chemoradiotherapy , Diffusion Magnetic Resonance Imaging , Female , Humans , Magnetic Resonance Imaging , Male , Middle Aged , Rectal Neoplasms/drug therapy , Rectal Neoplasms/therapy , Retrospective Studies , Treatment Outcome
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